






import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
							import torch

							A__     :      Dict             =			True
except ImportError:
							A__     :      str             =			False


try:
							from torch.hub import _get_torch_home

							A__     :      Optional[int]             =			_get_torch_home()
except ImportError:
							A__     :      Union[str, Any]             =			os.path.expanduser(
							    os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
							)

A__     :      Any             =			os.path.join(torch_cache_home, 'transformers')

A__     :      Optional[int]             =			'https://cdn.huggingface.co'
A__     :      List[Any]             =			'https://s3.amazonaws.com/models.huggingface.co/bert'
A__     :      Dict             =			'/'.join(str(Path(__file__).resolve()).split('/')[:-1])
A__     :      Union[str, Any]             =			os.path.join(PATH, 'config.yaml')
A__     :      Optional[Any]             =			os.path.join(PATH, 'attributes.txt')
A__     :      Any             =			os.path.join(PATH, 'objects.txt')
A__     :      List[str]             =			os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
A__     :      Tuple             =			os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
A__     :      str             =			os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
A__     :      Tuple             =			'pytorch_model.bin'
A__     :      Dict             =			'config.yaml'






def   a      (						lowerCamelCase_=OBJECTS		,       lowerCamelCase_=ATTRIBUTES    ):






				'''simple docstring'''
				lowercase__       			=      []
				with open(lowerCamelCase_    ) as f:
								for object in f.readlines():
												vg_classes.append(object.split(''','''    )[0].lower().strip()    )

				lowercase__       			=      []
				with open(lowerCamelCase_    ) as f:
								for object in f.readlines():
												vg_attrs.append(object.split(''','''    )[0].lower().strip()    )
				return vg_classes, vg_attrs






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      OrderedDict()
				with open(lowerCamelCase_		,       '''rb'''    ) as f:
								lowercase__       			=      pkl.load(lowerCamelCase_    )['''model''']
				for k in copy.deepcopy(list(ckp.keys()    )    ):
								lowercase__       			=      ckp.pop(lowerCamelCase_    )
								if isinstance(lowerCamelCase_		,       np.ndarray    ):
												lowercase__       			=      torch.tensor(lowerCamelCase_    )
								else:
												assert isinstance(lowerCamelCase_		,       torch.tensor    ), type(lowerCamelCase_    )
								lowercase__       			=      v
				return r
class 				_UpperCAmelCase :

				"""simple docstring"""




				lowercase__    							=	{}




				def __init__(					self						:   Union[str, Any],  lowerCamelCase						:   dict,  lowerCamelCase						:   str = "root",  lowerCamelCase						:   Any=0  ):

								'''simple docstring'''
								lowercase__       			=      name
								lowercase__       			=      level
								lowercase__       			=      {}
								for k, v in dictionary.items():
												if v is None:
																raise ValueError()
												lowercase__       			=      copy.deepcopy(lowerCamelCase  )
												lowercase__       			=      copy.deepcopy(lowerCamelCase  )
												if isinstance(lowerCamelCase,  lowerCamelCase  ):
																lowercase__       			=      Config(lowerCamelCase,  name=lowerCamelCase,  level=level + 1  )
												lowercase__       			=      v
												setattr(self,  lowerCamelCase,  lowerCamelCase  )

								lowercase__       			=      d




				def __repr__(					self						:   Dict  ):

								'''simple docstring'''
								return str(list((self._pointer.keys())  )  )




				def __setattr__(					self						:   Tuple,  lowerCamelCase						:   int,  lowerCamelCase						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      val
								lowercase__       			=      val
								lowercase__       			=      key.split('''.'''  )
								lowercase__       			=      len(lowerCamelCase  ) - 1
								lowercase__       			=      self._pointer
								if len(lowerCamelCase  ) > 1:
												for i, l in enumerate(lowerCamelCase  ):
																if hasattr(self,  lowerCamelCase  ) and isinstance(getattr(self,  lowerCamelCase  ),  lowerCamelCase  ):
																				setattr(getattr(self,  lowerCamelCase  ),  '''.'''.join(levels[i:]  ),  lowerCamelCase  )
																if l == last_level:
																				lowercase__       			=      val
																else:
																				lowercase__       			=      pointer[l]




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								return self._pointer




				def     lowercase__       (					self						:   Dict,  lowerCamelCase						:   str,  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								with open(F"""{file_name}""",  '''w'''  ) as stream:
												dump(lowerCamelCase,  lowerCamelCase  )




				def     lowercase__       (					self						:   str,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								with open(F"""{file_name}""",  '''w'''  ) as stream:
												json.dump(lowerCamelCase,  lowerCamelCase  )




				@staticmethod
				def     lowercase__       (					lowerCamelCase						:   Any  ):

								'''simple docstring'''
								with open(lowerCamelCase  ) as stream:
												lowercase__       			=      load(lowerCamelCase,  Loader=lowerCamelCase  )
								return data




				def __str__(					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      '''    '''
								if self._name != "root":
												lowercase__       			=      F"""{t * (self._level-1)}{self._name}:\n"""
								else:
												lowercase__       			=      ''''''
								lowercase__       			=      self._level
								for i, (k, v) in enumerate(self._pointer.items()  ):
												if isinstance(lowerCamelCase,  lowerCamelCase  ):
																r += F"""{t * (self._level)}{v}\n"""
																self._level += 1
												else:
																r += F"""{t * (self._level)}{k}: {v} ({type(lowerCamelCase  ).__name__})\n"""
												lowercase__       			=      level
								return r[:-1]




				@classmethod
				def     lowercase__       (					cls						:   Any,  lowerCamelCase						:   str,  **lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      cls.get_config_dict(lowerCamelCase,  **lowerCamelCase  )
								return cls(lowerCamelCase  )




				@classmethod
				def     lowercase__       (					cls						:   Union[str, Any],  lowerCamelCase						:   str,  **lowerCamelCase						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      kwargs.pop('''cache_dir''',  lowerCamelCase  )
								lowercase__       			=      kwargs.pop('''force_download''',  lowerCamelCase  )
								lowercase__       			=      kwargs.pop('''resume_download''',  lowerCamelCase  )
								lowercase__       			=      kwargs.pop('''proxies''',  lowerCamelCase  )
								lowercase__       			=      kwargs.pop('''local_files_only''',  lowerCamelCase  )

								if os.path.isdir(lowerCamelCase  ):
												lowercase__       			=      os.path.join(lowerCamelCase,  lowerCamelCase  )
								elif os.path.isfile(lowerCamelCase  ) or is_remote_url(lowerCamelCase  ):
												lowercase__       			=      pretrained_model_name_or_path
								else:
												lowercase__       			=      hf_bucket_url(lowerCamelCase,  filename=lowerCamelCase,  use_cdn=lowerCamelCase  )

								try:
												# Load from URL or cache if already cached
												lowercase__       			=      cached_path(
												    lowerCamelCase,  cache_dir=lowerCamelCase,  force_download=lowerCamelCase,  proxies=lowerCamelCase,  resume_download=lowerCamelCase,  local_files_only=lowerCamelCase,  )
												# Load config dict
												if resolved_config_file is None:
																raise EnvironmentError

												lowercase__       			=      Config.load_yaml(lowerCamelCase  )

								except EnvironmentError:
												lowercase__       			=      '''Can\'t load config for'''
												raise EnvironmentError(lowerCamelCase  )

								if resolved_config_file == config_file:
												print('''loading configuration file from path'''  )
								else:
												print('''loading configuration file cache'''  )

								return Config.load_yaml(lowerCamelCase  ), kwargs






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      torch.load('''dump.pt'''		,       map_location=in_tensor.device    )
				lowercase__       			=      in_tensor.numpy()
				lowercase__       			=      out_tensor.numpy()[0]
				print(na.shape		,       na[0, 0, :5]    )
				print(na.shape		,       na[0, 0, :5]    )
				assert np.allclose(lowerCamelCase_		,       lowerCamelCase_		,       rtol=0.01		,       atol=0.1    ), (
				    F"""{sum([1 for x in np.isclose(lowerCamelCase_		,       lowerCamelCase_		,       rtol=0.01		,       atol=0.1    ).flatten() if x is False]    )/len(na.flatten()    )*100:.4f} %"""
				    " element-wise mismatch"
				)
				raise Exception('''tensors are all good'''    )

				# Hugging face functions below






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      urlparse(lowerCamelCase_    )
				return parsed.scheme in ("http", "https")






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=True    ):






				'''simple docstring'''
				lowercase__       			=      CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				lowercase__       			=      '''/''' not in model_id
				if legacy_format:
								return F"""{endpoint}/{model_id}-{filename}"""
				else:
								return F"""{endpoint}/{model_id}/{filename}"""






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=0		,       lowerCamelCase_=None		,       ):






				'''simple docstring'''
				lowercase__       			=      '''python/{}'''.format(sys.version.split()[0]    )
				if _torch_available:
								ua += "; torch/{}".format(torch.__version__    )
				if isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								ua += "; " + "; ".join('''{}/{}'''.format(lowerCamelCase_		,       lowerCamelCase_    ) for k, v in user_agent.items()    )
				elif isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								ua += "; " + user_agent
				lowercase__       			=      {'''user-agent''': ua}
				if resume_size > 0:
								lowercase__       			=      '''bytes=%d-''' % (resume_size,)
				lowercase__       			=      requests.get(lowerCamelCase_		,       stream=lowerCamelCase_		,       proxies=lowerCamelCase_		,       headers=lowerCamelCase_    )
				if response.status_code == 416:  # Range not satisfiable
								return
				lowercase__       			=      response.headers.get('''Content-Length'''    )
				lowercase__       			=      resume_size + int(lowerCamelCase_    ) if content_length is not None else None
				lowercase__       			=      tqdm(
				    unit='''B'''		,       unit_scale=lowerCamelCase_		,       total=lowerCamelCase_		,       initial=lowerCamelCase_		,       desc='''Downloading'''		,       )
				for chunk in response.iter_content(chunk_size=1024    ):
								if chunk:  # filter out keep-alive new chunks
												progress.update(len(lowerCamelCase_    )    )
												temp_file.write(lowerCamelCase_    )
				progress.close()






def   a      (						lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=False		,       lowerCamelCase_=None		,       lowerCamelCase_=10		,       lowerCamelCase_=False		,       lowerCamelCase_=None		,       lowerCamelCase_=False		,       ):






				'''simple docstring'''
				if cache_dir is None:
								lowercase__       			=      TRANSFORMERS_CACHE
				if isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								lowercase__       			=      str(lowerCamelCase_    )

				os.makedirs(lowerCamelCase_		,       exist_ok=lowerCamelCase_    )

				lowercase__       			=      None
				if not local_files_only:
								try:
												lowercase__       			=      requests.head(lowerCamelCase_		,       allow_redirects=lowerCamelCase_		,       proxies=lowerCamelCase_		,       timeout=lowerCamelCase_    )
												if response.status_code == 200:
																lowercase__       			=      response.headers.get('''ETag'''    )
								except (EnvironmentError, requests.exceptions.Timeout):
												# etag is already None
												pass

				lowercase__       			=      url_to_filename(lowerCamelCase_		,       lowerCamelCase_    )

				# get cache path to put the file
				lowercase__       			=      os.path.join(lowerCamelCase_		,       lowerCamelCase_    )

				# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
				# try to get the last downloaded one
				if etag is None:
								if os.path.exists(lowerCamelCase_    ):
												return cache_path
								else:
												lowercase__       			=      [
												    file
												    for file in fnmatch.filter(os.listdir(lowerCamelCase_    )		,       filename + '''.*'''    )
												    if not file.endswith('''.json'''    ) and not file.endswith('''.lock'''    )
												]
												if len(lowerCamelCase_    ) > 0:
																return os.path.join(lowerCamelCase_		,       matching_files[-1]    )
												else:
																# If files cannot be found and local_files_only=True,
																# the models might've been found if local_files_only=False
																# Notify the user about that
																if local_files_only:
																				raise ValueError(
																				    '''Cannot find the requested files in the cached path and outgoing traffic has been'''
																				    ''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
																				    ''' to False.'''    )
																return None

    # From now on, etag is not None.
				if os.path.exists(lowerCamelCase_    ) and not force_download:
								return cache_path

				# Prevent parallel downloads of the same file with a lock.
				lowercase__       			=      cache_path + '''.lock'''
				with FileLock(lowerCamelCase_    ):
								# If the download just completed while the lock was activated.
								if os.path.exists(lowerCamelCase_    ) and not force_download:
												# Even if returning early like here, the lock will be released.
												return cache_path

								if resume_download:
												lowercase__       			=      cache_path + '''.incomplete'''

												@contextmanager
												def _resumable_file_manager():
																with open(lowerCamelCase_		,       '''a+b'''    ) as f:
																				yield f

												lowercase__       			=      _resumable_file_manager
												if os.path.exists(lowerCamelCase_    ):
																lowercase__       			=      os.stat(lowerCamelCase_    ).st_size
												else:
																lowercase__       			=      0
								else:
												lowercase__       			=      partial(tempfile.NamedTemporaryFile		,       dir=lowerCamelCase_		,       delete=lowerCamelCase_    )
												lowercase__       			=      0

								# Download to temporary file, then copy to cache dir once finished.
								# Otherwise you get corrupt cache entries if the download gets interrupted.
								with temp_file_manager() as temp_file:
												print(
												    '''%s not found in cache or force_download set to True, downloading to %s'''		,       lowerCamelCase_		,       temp_file.name		,       )

												http_get(
												    lowerCamelCase_		,       lowerCamelCase_		,       proxies=lowerCamelCase_		,       resume_size=lowerCamelCase_		,       user_agent=lowerCamelCase_		,       )

								os.replace(temp_file.name		,       lowerCamelCase_    )

								lowercase__       			=      {'''url''': url, '''etag''': etag}
								lowercase__       			=      cache_path + '''.json'''
								with open(lowerCamelCase_		,       '''w'''    ) as meta_file:
												json.dump(lowerCamelCase_		,       lowerCamelCase_    )

				return cache_path






def   a      (						lowerCamelCase_		,       lowerCamelCase_=None    ):






				'''simple docstring'''
				lowercase__       			=      url.encode('''utf-8'''    )
				lowercase__       			=      shaaaa(lowerCamelCase_    )
				lowercase__       			=      url_hash.hexdigest()

				if etag:
								lowercase__       			=      etag.encode('''utf-8'''    )
								lowercase__       			=      shaaaa(lowerCamelCase_    )
								filename += "." + etag_hash.hexdigest()

				if url.endswith('''.h5'''    ):
								filename += ".h5"

				return filename






def   a      (						lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=False		,       lowerCamelCase_=None		,       lowerCamelCase_=False		,       lowerCamelCase_=None		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       ):






				'''simple docstring'''
				if cache_dir is None:
								lowercase__       			=      TRANSFORMERS_CACHE
				if isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								lowercase__       			=      str(lowerCamelCase_    )
				if isinstance(lowerCamelCase_		,       lowerCamelCase_    ):
								lowercase__       			=      str(lowerCamelCase_    )

				if is_remote_url(lowerCamelCase_    ):
								# URL, so get it from the cache (downloading if necessary)
								lowercase__       			=      get_from_cache(
								    lowerCamelCase_		,       cache_dir=lowerCamelCase_		,       force_download=lowerCamelCase_		,       proxies=lowerCamelCase_		,       resume_download=lowerCamelCase_		,       user_agent=lowerCamelCase_		,       local_files_only=lowerCamelCase_		,       )
				elif os.path.exists(lowerCamelCase_    ):
								# File, and it exists.
								lowercase__       			=      url_or_filename
				elif urlparse(lowerCamelCase_    ).scheme == "":
								# File, but it doesn't exist.
								raise EnvironmentError('''file {} not found'''.format(lowerCamelCase_    )    )
				else:
								# Something unknown
								raise ValueError('''unable to parse {} as a URL or as a local path'''.format(lowerCamelCase_    )    )

				if extract_compressed_file:
								if not is_zipfile(lowerCamelCase_    ) and not tarfile.is_tarfile(lowerCamelCase_    ):
												return output_path

								# Path where we extract compressed archives
								# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
								lowercase__			,						lowercase__       			=      os.path.split(lowerCamelCase_    )
								lowercase__       			=      output_file.replace('''.'''		,       '''-'''    ) + '''-extracted'''
								lowercase__       			=      os.path.join(lowerCamelCase_		,       lowerCamelCase_    )

								if os.path.isdir(lowerCamelCase_    ) and os.listdir(lowerCamelCase_    ) and not force_extract:
												return output_path_extracted

								# Prevent parallel extractions
								lowercase__       			=      output_path + '''.lock'''
								with FileLock(lowerCamelCase_    ):
												shutil.rmtree(lowerCamelCase_		,       ignore_errors=lowerCamelCase_    )
												os.makedirs(lowerCamelCase_    )
												if is_zipfile(lowerCamelCase_    ):
																with ZipFile(lowerCamelCase_		,       '''r'''    ) as zip_file:
																				zip_file.extractall(lowerCamelCase_    )
																				zip_file.close()
												elif tarfile.is_tarfile(lowerCamelCase_    ):
																lowercase__       			=      tarfile.open(lowerCamelCase_    )
																tar_file.extractall(lowerCamelCase_    )
																tar_file.close()
												else:
																raise EnvironmentError('''Archive format of {} could not be identified'''.format(lowerCamelCase_    )    )

								return output_path_extracted

				return output_path






def   a      (						lowerCamelCase_		,       lowerCamelCase_=","    ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_		,       lowerCamelCase_    )
				if os.path.isfile(lowerCamelCase_    ):
								with open(lowerCamelCase_    ) as f:
												lowercase__       			=      eval(f.read()    )
				else:
								lowercase__       			=      requests.get(lowerCamelCase_    )
								try:
												lowercase__       			=      requests.json()
								except Exception:
												lowercase__       			=      req.content.decode()
												assert data is not None, "could not connect"
												try:
																lowercase__       			=      eval(lowerCamelCase_    )
												except Exception:
																lowercase__       			=      data.split('''\n'''    )
								req.close()
				return data






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      requests.get(lowerCamelCase_    )
				lowercase__       			=      np.array(Image.open(BytesIO(response.content    )    )    )
				return img






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      url.split('''/'''    )[-1]
				if fn not in os.listdir(os.getcwd()    ):
								wget.download(lowerCamelCase_    )
				with open(lowerCamelCase_		,       '''rb'''    ) as stream:
								lowercase__       			=      pkl.load(lowerCamelCase_    )
				lowercase__       			=      weights.pop('''model'''    )
				lowercase__       			=      {}
				for k, v in model.items():
								lowercase__       			=      torch.from_numpy(lowerCamelCase_    )
								if "running_var" in k:
												lowercase__       			=      torch.tensor([0]    )
												lowercase__       			=      k.replace('''running_var'''		,       '''num_batches_tracked'''    )
												lowercase__       			=      zero
				return new






def   a      (						):






				'''simple docstring'''
				print(F"""{os.path.abspath(os.path.join(lowerCamelCase_		,       os.pardir    )    )}/demo.ipynb"""    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_="RGB"    ):






				'''simple docstring'''
				assert isinstance(lowerCamelCase_		,       lowerCamelCase_    )
				if os.path.isfile(lowerCamelCase_    ):
								lowercase__       			=      cva.imread(lowerCamelCase_    )
				else:
								lowercase__       			=      get_image_from_url(lowerCamelCase_    )
								assert img is not None, F"""could not connect to: {im}"""
				lowercase__       			=      cva.cvtColor(lowerCamelCase_		,       cva.COLOR_BGR2RGB    )
				if input_format == "RGB":
								lowercase__       			=      img[:, :, ::-1]
				return img






def   a      (						lowerCamelCase_		,       lowerCamelCase_=1    ):






				'''simple docstring'''
				return (images[i : i + batch] for i in range(0		,       len(lowerCamelCase_    )		,       lowerCamelCase_    ))












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    RemBertConfig,
							    RemBertForCausalLM,
							    RemBertForMaskedLM,
							    RemBertForMultipleChoice,
							    RemBertForQuestionAnswering,
							    RemBertForSequenceClassification,
							    RemBertForTokenClassification,
							    RemBertModel,
							)
							from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   Optional[int],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   int=13,  lowerCamelCase						:   str=7,  lowerCamelCase						:   int=True,  lowerCamelCase						:   Optional[Any]=True,  lowerCamelCase						:   List[str]=True,  lowerCamelCase						:   str=True,  lowerCamelCase						:   List[str]=99,  lowerCamelCase						:   Any=32,  lowerCamelCase						:   int=18,  lowerCamelCase						:   Optional[int]=43,  lowerCamelCase						:   Optional[Any]=5,  lowerCamelCase						:   int=4,  lowerCamelCase						:   Union[str, Any]=37,  lowerCamelCase						:   List[str]="gelu",  lowerCamelCase						:   List[str]=0.1,  lowerCamelCase						:   Dict=0.1,  lowerCamelCase						:   Dict=512,  lowerCamelCase						:   str=16,  lowerCamelCase						:   Optional[Any]=2,  lowerCamelCase						:   int=0.02,  lowerCamelCase						:   Optional[int]=3,  lowerCamelCase						:   Optional[int]=4,  lowerCamelCase						:   Union[str, Any]=None,  ):

								'''simple docstring'''
								lowercase__       			=      parent
								lowercase__       			=      batch_size
								lowercase__       			=      seq_length
								lowercase__       			=      is_training
								lowercase__       			=      use_input_mask
								lowercase__       			=      use_token_type_ids
								lowercase__       			=      use_labels
								lowercase__       			=      vocab_size
								lowercase__       			=      hidden_size
								lowercase__       			=      input_embedding_size
								lowercase__       			=      output_embedding_size
								lowercase__       			=      num_hidden_layers
								lowercase__       			=      num_attention_heads
								lowercase__       			=      intermediate_size
								lowercase__       			=      hidden_act
								lowercase__       			=      hidden_dropout_prob
								lowercase__       			=      attention_probs_dropout_prob
								lowercase__       			=      max_position_embeddings
								lowercase__       			=      type_vocab_size
								lowercase__       			=      type_sequence_label_size
								lowercase__       			=      initializer_range
								lowercase__       			=      num_labels
								lowercase__       			=      num_choices
								lowercase__       			=      scope




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__       			=      None
								if self.use_input_mask:
												lowercase__       			=      random_attention_mask([self.batch_size, self.seq_length]  )

								lowercase__       			=      None
								if self.use_token_type_ids:
												lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.type_vocab_size  )

								lowercase__       			=      None
								lowercase__       			=      None
								lowercase__       			=      None
								if self.use_labels:
												lowercase__       			=      ids_tensor([self.batch_size],  self.type_sequence_label_size  )
												lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  self.num_labels  )
												lowercase__       			=      ids_tensor([self.batch_size],  self.num_choices  )

								lowercase__       			=      RemBertConfig(
								    vocab_size=self.vocab_size,  hidden_size=self.hidden_size,  input_embedding_size=self.input_embedding_size,  output_embedding_size=self.output_embedding_size,  num_hidden_layers=self.num_hidden_layers,  num_attention_heads=self.num_attention_heads,  intermediate_size=self.intermediate_size,  hidden_act=self.hidden_act,  hidden_dropout_prob=self.hidden_dropout_prob,  attention_probs_dropout_prob=self.attention_probs_dropout_prob,  max_position_embeddings=self.max_position_embeddings,  type_vocab_size=self.type_vocab_size,  is_decoder=lowerCamelCase,  initializer_range=self.initializer_range,  )

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								(
								    (
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						
								)       			=      self.prepare_config_and_inputs()

								lowercase__       			=      True
								lowercase__       			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
								lowercase__       			=      ids_tensor([self.batch_size, self.seq_length],  vocab_size=2  )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_mask,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   int,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      RemBertModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__       			=      model(lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__       			=      model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def     lowercase__       (					self						:   Optional[Any],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Dict,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   str,  lowerCamelCase						:   Any,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Any,  ):

								'''simple docstring'''
								lowercase__       			=      True
								lowercase__       			=      RemBertModel(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  )
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  )
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )




				def     lowercase__       (					self						:   str,  lowerCamelCase						:   str,  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   Optional[Any],  ):

								'''simple docstring'''
								lowercase__       			=      RemBertForCausalLM(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def     lowercase__       (					self						:   Any,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Dict,  lowerCamelCase						:   str,  lowerCamelCase						:   List[str],  lowerCamelCase						:   Any,  lowerCamelCase						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      RemBertForMaskedLM(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   List[str],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Any,  lowerCamelCase						:   Union[str, Any],  lowerCamelCase						:   int,  lowerCamelCase						:   Dict,  ):

								'''simple docstring'''
								lowercase__       			=      True
								lowercase__       			=      True
								lowercase__       			=      RemBertForCausalLM(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								# first forward pass
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  use_cache=lowerCamelCase,  )
								lowercase__       			=      outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								lowercase__       			=      ids_tensor((self.batch_size, 3),  config.vocab_size  )
								lowercase__       			=      ids_tensor((self.batch_size, 3),  vocab_size=2  )

								# append to next input_ids and
								lowercase__       			=      torch.cat([input_ids, next_tokens],  dim=-1  )
								lowercase__       			=      torch.cat([input_mask, next_mask],  dim=-1  )

								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  output_hidden_states=lowerCamelCase,  )['''hidden_states'''][0]
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  encoder_attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase,  output_hidden_states=lowerCamelCase,  )['''hidden_states'''][0]

								# select random slice
								lowercase__       			=      ids_tensor((1,),  output_from_past.shape[-1]  ).item()
								lowercase__       			=      output_from_no_past[:, -3:, random_slice_idx].detach()
								lowercase__       			=      output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(lowerCamelCase,  lowerCamelCase,  atol=1E-3  )  )




				def     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[Any],  lowerCamelCase						:   List[str],  lowerCamelCase						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      RemBertForQuestionAnswering(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  start_positions=lowerCamelCase,  end_positions=lowerCamelCase,  )
								self.parent.assertEqual(result.start_logits.shape,  (self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape,  (self.batch_size, self.seq_length)  )




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   Dict,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      RemBertForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Optional[int],  lowerCamelCase						:   Dict,  lowerCamelCase						:   str,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.num_labels
								lowercase__       			=      RemBertForTokenClassification(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      model(lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.num_labels)  )




				def     lowercase__       (					self						:   Optional[int],  lowerCamelCase						:   Any,  lowerCamelCase						:   Tuple,  lowerCamelCase						:   int,  lowerCamelCase						:   str,  lowerCamelCase						:   int,  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.num_choices
								lowercase__       			=      RemBertForMultipleChoice(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__       			=      input_ids.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      token_type_ids.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      input_mask.unsqueeze(1  ).expand(-1,  self.num_choices,  -1  ).contiguous()
								lowercase__       			=      model(
								    lowerCamelCase,  attention_mask=lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase,  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_choices)  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.prepare_config_and_inputs()
								(
								    (
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						
								)       			=      config_and_inputs
								lowercase__       			=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict



@require_torch
class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	(
				    (
				        RemBertModel,
				        RemBertForMaskedLM,
				        RemBertForCausalLM,
				        RemBertForMultipleChoice,
				        RemBertForQuestionAnswering,
				        RemBertForSequenceClassification,
				        RemBertForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__    							=	(RemBertForCausalLM,) if is_torch_available() else ()
				lowercase__    							=	(
				    {
				        """feature-extraction""": RemBertModel,
				        """fill-mask""": RemBertForMaskedLM,
				        """question-answering""": RemBertForQuestionAnswering,
				        """text-classification""": RemBertForSequenceClassification,
				        """text-generation""": RemBertForCausalLM,
				        """token-classification""": RemBertForTokenClassification,
				        """zero-shot""": RemBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      RemBertModelTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  hidden_size=37  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def     lowercase__       (					self						:   List[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
												lowercase__       			=      type
												self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase  )




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase  )




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								# This regression test was failing with PyTorch < 1.3
								(
								    (
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						(
								    lowercase__
								)			,						
								)       			=      self.model_tester.prepare_config_and_inputs_for_decoder()

								lowercase__       			=      None

								self.model_tester.create_and_check_model_as_decoder(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  )




				@slow
				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__       			=      RemBertModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )



@require_torch
class 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				@slow
				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								# Test exact values at the last hidden layer
								lowercase__       			=      RemBertModel.from_pretrained('''google/rembert'''  )
								lowercase__       			=      torch.tensor([[312, 56_498, 313, 2_125, 313]]  )
								lowercase__       			=      torch.tensor([[0, 0, 0, 1, 1]]  )
								with torch.no_grad():
												lowercase__       			=      model(lowerCamelCase,  token_type_ids=lowerCamelCase,  output_hidden_states=lowerCamelCase  )

								lowercase__       			=      1_152

								lowercase__       			=      torch.Size((1, 5, hidden_size)  )
								self.assertEqual(output['''last_hidden_state'''].shape,  lowerCamelCase  )

								lowercase__       			=      torch.tensor(
								    [
								        [
								            [0.0754, -0.2022, 0.1904],
								            [-0.3354, -0.3692, -0.4791],
								            [-0.2314, -0.6729, -0.0749],
								            [-0.0396, -0.3105, -0.4234],
								            [-0.1571, -0.0525, 0.5353],
								        ]
								    ]  )

								# Running on the original tf implementation gives slightly different results here.
								# Not clear why this variations is present
								# TODO: Find reason for discrepancy
								# expected_original_implementation = [[
								#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
								#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
								#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
								#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
								#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
								# ]]

								self.assertTrue(torch.allclose(output['''last_hidden_state'''][:, :, :3],  lowerCamelCase,  atol=1E-4  )  )




