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

						lowerCAmelCase__  :  List[Any]											=     True
except ImportError:
						lowerCAmelCase__  :  Any											=     False


try:
						from torch.hub import _get_torch_home

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

lowerCAmelCase__  :  List[Any]											=     os.path.join(torch_cache_home, 'transformers')

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



def   a_						( lowerCamelCase=OBJECTS ,      lowerCamelCase=ATTRIBUTES	):
				UpperCAmelCase__					=							[]
				with open(SCREAMING_SNAKE_CASE_	) as f:
								for object in f.readlines():
												vg_classes.append(object.split(','	)[0].lower().strip()	)

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



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							OrderedDict()
				with open(SCREAMING_SNAKE_CASE_ ,      'rb'	) as f:
								UpperCAmelCase__					=							pkl.load(SCREAMING_SNAKE_CASE_	)['model']
				for k in copy.deepcopy(list(ckp.keys()	)	):
								UpperCAmelCase__					=							ckp.pop(SCREAMING_SNAKE_CASE_	)
								if isinstance(SCREAMING_SNAKE_CASE_ ,      np.ndarray	):
												UpperCAmelCase__					=							torch.tensor(SCREAMING_SNAKE_CASE_	)
								else:
												assert isinstance(SCREAMING_SNAKE_CASE_ ,      torch.tensor	), type(SCREAMING_SNAKE_CASE_	)
								UpperCAmelCase__					=							v
				return r
class     snake_case	:




					"""simple docstring"""

					snake_case__       =			{}







					def __init__(    self       :     Dict   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[int] = "root"   ,lowerCamelCase__       :     Any=0      ):
									UpperCAmelCase__					=							name
									UpperCAmelCase__					=							level
									UpperCAmelCase__					=							{}
									for k, v in dictionary.items():
													if v is None:
																	raise ValueError()
													UpperCAmelCase__					=							copy.deepcopy(lowerCamelCase__      )
													UpperCAmelCase__					=							copy.deepcopy(lowerCamelCase__      )
													if isinstance(lowerCamelCase__   ,lowerCamelCase__      ):
																	UpperCAmelCase__					=							Config(lowerCamelCase__   ,name=lowerCamelCase__   ,level=level + 1      )
													UpperCAmelCase__					=							v
													setattr(self   ,lowerCamelCase__   ,lowerCamelCase__      )

									UpperCAmelCase__					=							d







					def __repr__(    self       :     Tuple      ):
									return str(list((self._pointer.keys())      )      )







					def __setattr__(    self       :     List[Any]   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     Dict      ):
									UpperCAmelCase__					=							val
									UpperCAmelCase__					=							val
									UpperCAmelCase__					=							key.split('.'      )
									UpperCAmelCase__					=							len(lowerCamelCase__      ) - 1
									UpperCAmelCase__					=							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:
																					UpperCAmelCase__					=							val
																	else:
																					UpperCAmelCase__					=							pointer[l]







					def 		__lowerCAmelCase						(    self       :     Any      ):
									return self._pointer







					def 		__lowerCAmelCase						(    self       :     List[str]   ,lowerCamelCase__       :     Tuple   ,lowerCamelCase__       :     List[Any]      ):






									with open(f'''{file_name}'''   ,'w'      ) as stream:
													dump(lowerCamelCase__   ,lowerCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     Optional[Any]   ,lowerCamelCase__       :     int   ,lowerCamelCase__       :     Union[str, Any]      ):






									with open(f'''{file_name}'''   ,'w'      ) as stream:
													json.dump(lowerCamelCase__   ,lowerCamelCase__      )







					@staticmethod
					def 		__lowerCAmelCase						(    lowerCamelCase__       :     Optional[Any]      ):






									with open(lowerCamelCase__      ) as stream:
													UpperCAmelCase__					=							load(lowerCamelCase__   ,Loader=lowerCamelCase__      )
									return data







					def __str__(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							'    '
									if self._name != "root":
													UpperCAmelCase__					=							f'''{t * (self._level-1)}{self._name}:\n'''
									else:
													UpperCAmelCase__					=							''
									UpperCAmelCase__					=							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'''
													UpperCAmelCase__					=							level
									return r[:-1]







					@classmethod
					def 		__lowerCAmelCase						(    cls       :     List[Any]   ,lowerCamelCase__       :     List[Any]   ,**lowerCamelCase__       :     Dict      ):
									UpperCAmelCase__				,     UpperCAmelCase__					=							cls.get_config_dict(lowerCamelCase__   ,**lowerCamelCase__      )
									return cls(lowerCamelCase__      )







					@classmethod
					def 		__lowerCAmelCase						(    cls       :     Optional[int]   ,lowerCamelCase__       :     Dict   ,**lowerCamelCase__       :     Union[str, Any]      ):
									UpperCAmelCase__					=							kwargs.pop('cache_dir'   ,lowerCamelCase__      )
									UpperCAmelCase__					=							kwargs.pop('force_download'   ,lowerCamelCase__      )
									UpperCAmelCase__					=							kwargs.pop('resume_download'   ,lowerCamelCase__      )
									UpperCAmelCase__					=							kwargs.pop('proxies'   ,lowerCamelCase__      )
									UpperCAmelCase__					=							kwargs.pop('local_files_only'   ,lowerCamelCase__      )

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

									try:
													# Load from URL or cache if already cached
													UpperCAmelCase__					=							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

													UpperCAmelCase__					=							Config.load_yaml(lowerCamelCase__      )

									except EnvironmentError:
													UpperCAmelCase__					=							'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	):
				UpperCAmelCase__					=							torch.load('dump.pt' ,      map_location=in_tensor.device	)
				UpperCAmelCase__					=							in_tensor.numpy()
				UpperCAmelCase__					=							out_tensor.numpy()[0]
				print(na.shape ,      na[0, 0, :5]	)
				print(na.shape ,      na[0, 0, :5]	)
				assert np.allclose(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_ ,      rtol=0.01 ,      atol=0.1	), (
				    f'''{sum([1 for x in np.isclose(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_ ,      rtol=0.01 ,      atol=0.1	).flatten() if x is False]	)/len(na.flatten()	)*1_0_0:.4f} %'''
				    " element-wise mismatch"
				)
				raise Exception('tensors are all good'	)

				# Hugging face functions below



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							urlparse(SCREAMING_SNAKE_CASE_	)
				return parsed.scheme in ("http", "https")



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase=True	):
				UpperCAmelCase__					=							CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
				UpperCAmelCase__					=							'/' 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 ,      ):
				UpperCAmelCase__					=							'python/{}'.format(sys.version.split()[0]	)
				if _torch_available:
								ua += "; torch/{}".format(torch.__version__	)
				if isinstance(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	):
								ua += "; " + "; ".join('{}/{}'.format(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	) for k, v in user_agent.items()	)
				elif isinstance(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	):
								ua += "; " + user_agent
				UpperCAmelCase__					=							{'user-agent': ua}
				if resume_size > 0:
								UpperCAmelCase__					=							'bytes=%d-' % (resume_size,)
				UpperCAmelCase__					=							requests.get(SCREAMING_SNAKE_CASE_ ,      stream=SCREAMING_SNAKE_CASE_ ,      proxies=SCREAMING_SNAKE_CASE_ ,      headers=SCREAMING_SNAKE_CASE_	)
				if response.status_code == 4_1_6:  # Range not satisfiable
								return
				UpperCAmelCase__					=							response.headers.get('Content-Length'	)
				UpperCAmelCase__					=							resume_size + int(SCREAMING_SNAKE_CASE_	) if content_length is not None else None
				UpperCAmelCase__					=							tqdm(
				    unit='B' ,      unit_scale=SCREAMING_SNAKE_CASE_ ,      total=SCREAMING_SNAKE_CASE_ ,      initial=SCREAMING_SNAKE_CASE_ ,      desc='Downloading' ,      )
				for chunk in response.iter_content(chunk_size=1_0_2_4	):
								if chunk:  # filter out keep-alive new chunks
												progress.update(len(SCREAMING_SNAKE_CASE_	)	)
												temp_file.write(SCREAMING_SNAKE_CASE_	)
				progress.close()



def   a_						( lowerCamelCase ,      lowerCamelCase=None ,      lowerCamelCase=False ,      lowerCamelCase=None ,      lowerCamelCase=1_0 ,      lowerCamelCase=False ,      lowerCamelCase=None ,      lowerCamelCase=False ,      ):





				if cache_dir is None:
								UpperCAmelCase__					=							TRANSFORMERS_CACHE
				if isinstance(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	):
								UpperCAmelCase__					=							str(SCREAMING_SNAKE_CASE_	)

				os.makedirs(SCREAMING_SNAKE_CASE_ ,      exist_ok=SCREAMING_SNAKE_CASE_	)

				UpperCAmelCase__					=							None
				if not local_files_only:
								try:
												UpperCAmelCase__					=							requests.head(SCREAMING_SNAKE_CASE_ ,      allow_redirects=SCREAMING_SNAKE_CASE_ ,      proxies=SCREAMING_SNAKE_CASE_ ,      timeout=SCREAMING_SNAKE_CASE_	)
												if response.status_code == 2_0_0:
																UpperCAmelCase__					=							response.headers.get('ETag'	)
								except (EnvironmentError, requests.exceptions.Timeout):
												# etag is already None
												pass

				UpperCAmelCase__					=							url_to_filename(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)

				# get cache path to put the file
				UpperCAmelCase__					=							os.path.join(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)

				# 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(SCREAMING_SNAKE_CASE_	):
												return cache_path
								else:
												UpperCAmelCase__					=							[
												    file
												    for file in fnmatch.filter(os.listdir(SCREAMING_SNAKE_CASE_	) ,      filename + '.*'	)
												    if not file.endswith('.json'	) and not file.endswith('.lock'	)
												]
												if len(SCREAMING_SNAKE_CASE_	) > 0:
																return os.path.join(SCREAMING_SNAKE_CASE_ ,      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(SCREAMING_SNAKE_CASE_	) and not force_download:
								return cache_path

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

								if resume_download:
												UpperCAmelCase__					=							cache_path + '.incomplete'

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

												UpperCAmelCase__					=							_resumable_file_manager
												if os.path.exists(SCREAMING_SNAKE_CASE_	):
																UpperCAmelCase__					=							os.stat(SCREAMING_SNAKE_CASE_	).st_size
												else:
																UpperCAmelCase__					=							0
								else:
												UpperCAmelCase__					=							partial(tempfile.NamedTemporaryFile ,      dir=SCREAMING_SNAKE_CASE_ ,      delete=SCREAMING_SNAKE_CASE_	)
												UpperCAmelCase__					=							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' ,      SCREAMING_SNAKE_CASE_ ,      temp_file.name ,      )

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

								os.replace(temp_file.name ,      SCREAMING_SNAKE_CASE_	)

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

				return cache_path



def   a_						( lowerCamelCase ,      lowerCamelCase=None	):
				UpperCAmelCase__					=							url.encode('utf-8'	)
				UpperCAmelCase__					=							shaaaa(SCREAMING_SNAKE_CASE_	)
				UpperCAmelCase__					=							url_hash.hexdigest()

				if etag:
								UpperCAmelCase__					=							etag.encode('utf-8'	)
								UpperCAmelCase__					=							shaaaa(SCREAMING_SNAKE_CASE_	)
								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 ,      ):





				if cache_dir is None:
								UpperCAmelCase__					=							TRANSFORMERS_CACHE
				if isinstance(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	):
								UpperCAmelCase__					=							str(SCREAMING_SNAKE_CASE_	)
				if isinstance(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	):
								UpperCAmelCase__					=							str(SCREAMING_SNAKE_CASE_	)

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

				if extract_compressed_file:
								if not is_zipfile(SCREAMING_SNAKE_CASE_	) and not tarfile.is_tarfile(SCREAMING_SNAKE_CASE_	):
												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/"
								UpperCAmelCase__				,     UpperCAmelCase__					=							os.path.split(SCREAMING_SNAKE_CASE_	)
								UpperCAmelCase__					=							output_file.replace('.' ,      '-'	) + '-extracted'
								UpperCAmelCase__					=							os.path.join(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)

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

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

								return output_path_extracted

				return output_path



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



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							requests.get(SCREAMING_SNAKE_CASE_	)
				UpperCAmelCase__					=							np.array(Image.open(BytesIO(response.content	)	)	)
				return img



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



def   a_						( ):
				print(f'''{os.path.abspath(os.path.join(SCREAMING_SNAKE_CASE_ ,      os.pardir	)	)}/demo.ipynb'''	)



def   a_						( lowerCamelCase ,      lowerCamelCase="RGB"	):
				assert isinstance(SCREAMING_SNAKE_CASE_ ,      SCREAMING_SNAKE_CASE_	)
				if os.path.isfile(SCREAMING_SNAKE_CASE_	):
								UpperCAmelCase__					=							cva.imread(SCREAMING_SNAKE_CASE_	)
				else:
								UpperCAmelCase__					=							get_image_from_url(SCREAMING_SNAKE_CASE_	)
								assert img is not None, f'''could not connect to: {im}'''
				UpperCAmelCase__					=							cva.cvtColor(SCREAMING_SNAKE_CASE_ ,      cva.COLOR_BGR2RGB	)
				if input_format == "RGB":
								UpperCAmelCase__					=							img[:, :, ::-1]
				return img







def   a_						( lowerCamelCase ,      lowerCamelCase=1	):
				return (images[i : i + batch] for i in range(0 ,      len(SCREAMING_SNAKE_CASE_	) ,      SCREAMING_SNAKE_CASE_	))




"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     snake_case	(			__UpperCAmelCase				,   unittest.TestCase    ):




					"""simple docstring"""

					snake_case__       =			KandinskyPriorPipeline
					snake_case__       =			["prompt"]
					snake_case__       =			["prompt", "negative_prompt"]
					snake_case__       =			[
					    "num_images_per_prompt",
					    "generator",
					    "num_inference_steps",
					    "latents",
					    "negative_prompt",
					    "guidance_scale",
					    "output_type",
					    "return_dict",
					]
					snake_case__       =			False







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 32







					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return 32







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									return self.time_input_dim







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									return 100







					@property
					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
									return tokenizer







					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							CLIPTextConfig(
									    bos_token_id=0   ,eos_token_id=2   ,hidden_size=self.text_embedder_hidden_size   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=37   ,layer_norm_eps=1e-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_000   ,)
									return CLIPTextModelWithProjection(lowerCamelCase__      )







					@property
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									torch.manual_seed(0      )

									UpperCAmelCase__					=							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									UpperCAmelCase__					=							PriorTransformer(**lowerCamelCase__      )
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									UpperCAmelCase__					=							nn.Parameter(torch.ones(model.clip_std.shape      )      )
									return model







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									torch.manual_seed(0      )
									UpperCAmelCase__					=							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size   ,image_size=224   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=37   ,num_attention_heads=4   ,num_channels=3   ,num_hidden_layers=5   ,patch_size=14   ,)

									UpperCAmelCase__					=							CLIPVisionModelWithProjection(lowerCamelCase__      )
									return model







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							CLIPImageProcessor(
									    crop_size=224   ,do_center_crop=lowerCamelCase__   ,do_normalize=lowerCamelCase__   ,do_resize=lowerCamelCase__   ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,resample=3   ,size=224   ,)

									return image_processor







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.dummy_prior
									UpperCAmelCase__					=							self.dummy_image_encoder
									UpperCAmelCase__					=							self.dummy_text_encoder
									UpperCAmelCase__					=							self.dummy_tokenizer
									UpperCAmelCase__					=							self.dummy_image_processor

									UpperCAmelCase__					=							UnCLIPScheduler(
									    variance_type='fixed_small_log'   ,prediction_type='sample'   ,num_train_timesteps=1_000   ,clip_sample=lowerCamelCase__   ,clip_sample_range=1_0.0   ,)

									UpperCAmelCase__					=							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'scheduler': scheduler,
									    'image_processor': image_processor,
									}

									return components







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     Union[str, Any]   ,lowerCamelCase__       :     Optional[int]=0      ):
									if str(lowerCamelCase__      ).startswith('mps'      ):
													UpperCAmelCase__					=							torch.manual_seed(lowerCamelCase__      )
									else:
													UpperCAmelCase__					=							torch.Generator(device=lowerCamelCase__      ).manual_seed(lowerCamelCase__      )
									UpperCAmelCase__					=							{
									    'prompt': 'horse',
									    'generator': generator,
									    'guidance_scale': 4.0,
									    'num_inference_steps': 2,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							'cpu'

									UpperCAmelCase__					=							self.get_dummy_components()

									UpperCAmelCase__					=							self.pipeline_class(**lowerCamelCase__      )
									UpperCAmelCase__					=							pipe.to(lowerCamelCase__      )

									pipe.set_progress_bar_config(disable=lowerCamelCase__      )

									UpperCAmelCase__					=							pipe(**self.get_dummy_inputs(lowerCamelCase__      )      )
									UpperCAmelCase__					=							output.image_embeds

									UpperCAmelCase__					=							pipe(
									    **self.get_dummy_inputs(lowerCamelCase__      )   ,return_dict=lowerCamelCase__   ,)[0]

									UpperCAmelCase__					=							image[0, -10:]
									UpperCAmelCase__					=							image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									UpperCAmelCase__					=							np.array(
									    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2







					@skip_mps
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							torch_device == 'cpu'
									UpperCAmelCase__					=							True
									UpperCAmelCase__					=							False

									self._test_inference_batch_single_identical(
									    test_max_difference=lowerCamelCase__   ,relax_max_difference=lowerCamelCase__   ,test_mean_pixel_difference=lowerCamelCase__   ,)







					@skip_mps
					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							torch_device == 'cpu'
									UpperCAmelCase__					=							False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=lowerCamelCase__   ,test_mean_pixel_difference=lowerCamelCase__   ,)



