import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
		from comet_ml import OfflineExperiment

if is_tensorboard_available():
		import struct

		import tensorboard.compat.proto.event_pba as event_pba

__lowerCamelCase :     List[str]          =  logging.getLogger(__name__)






@require_tensorboard
class 		a__						( unittest.TestCase ):

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   "test_project_with_config"
							with tempfile.TemporaryDirectory() as dirpath:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   Accelerator(log_with="tensorboard",project_dir=_A   )
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
									accelerator.init_trackers(_A,_A   )
									accelerator.end_training()
									for child in Path(F'{dirpath}/{project_name}'   ).glob("*/**"   ):
											SCREAMING_SNAKE_CASE_  :		Any    				=   list(filter(lambda _A   : x.is_file(),child.iterdir()   )   )[0]
									self.assertNotEqual(str(_A   ),""   )

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									SCREAMING_SNAKE_CASE_  :		Dict    				=   Accelerator(log_with="tensorboard",project_dir=_A   )
									accelerator.init_trackers(_A   )
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
									accelerator.log(_A,step=0   )
									accelerator.end_training()
									# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
									# Names are randomly generated each time
									SCREAMING_SNAKE_CASE_  :		int    				=   list(filter(lambda _A   : x.is_file(),Path(F'{dirpath}/{project_name}'   ).iterdir()   )   )[0]
									self.assertNotEqual(str(_A   ),""   )

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   "test_project_with_log"
							with tempfile.TemporaryDirectory() as dirpath:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   Accelerator(log_with="tensorboard",project_dir=_A   )
									accelerator.init_trackers(_A   )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   {"tensor": torch.tensor(1   )}
									accelerator.log(_A,step=0   )
									accelerator.end_training()
									# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
									# Names are randomly generated each time
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   list(filter(lambda _A   : x.is_file(),Path(F'{dirpath}/{project_name}'   ).iterdir()   )   )[0]
									# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
									with open(_A,"rb"   ) as f:
											SCREAMING_SNAKE_CASE_  :		Any    				=   f.read()
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   False
									while data:
											SCREAMING_SNAKE_CASE_  :		Tuple    				=   struct.unpack("Q",data[:8]   )

											SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   data[12 : 12 + int(header[0]   )]  # 8+4
											SCREAMING_SNAKE_CASE_  :		List[Any]    				=   data[12 + int(header[0]   ) + 4 :]
											SCREAMING_SNAKE_CASE_  :		Any    				=   event_pba.Event()

											event.ParseFromString(_A   )
											if event.HasField("summary"   ):
													for value in event.summary.value:
															if value.simple_value == 1.0 and value.tag == "tensor":
																	SCREAMING_SNAKE_CASE_  :		Tuple    				=   True
									self.assertTrue(_A,"Converted tensor was not found in the log file!"   )

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							with self.assertRaisesRegex(_A,"Logging with `tensorboard` requires a `logging_dir`"   ):
									SCREAMING_SNAKE_CASE_  :		Dict    				=   Accelerator(log_with="tensorboard"   )
							with tempfile.TemporaryDirectory() as dirpath:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   Accelerator(log_with="tensorboard",project_dir=_A   )






					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   ProjectConfiguration(total_limit=30   )
							with tempfile.TemporaryDirectory() as dirpath:
									SCREAMING_SNAKE_CASE_  :		Dict    				=   Accelerator(log_with="tensorboard",project_dir=_A,project_config=_A   )







@require_wandb
@mock.patch.dict(os.environ   ,		{'WANDB_MODE': 'offline'} )
class 		a__						( A__   ,		A__ ):

					def     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							super().setUp()
							# wandb let's us override where logs are stored to via the WANDB_DIR env var
							self.add_mocks(mock.patch.dict(os.environ,{"WANDB_DIR": self.tmpdir}   )   )

					@staticmethod
					def     __UpperCamelCase       (   _A				:	str,_A				:	str,_A				:	bool = True   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   RF'{section} ([\S\s]*?)\n\n'
							if record:
									SCREAMING_SNAKE_CASE_  :		int    				=   RF'Record: {pattern}'
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   re.findall(_A,_A   )[0]
							# A config
							if section == "config" or section == "history":
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   re.findall(R"\"([a-zA-Z0-9_.,]+)",_A   )
									return {key: val for key, val in zip(cleaned_record[0::2],cleaned_record[1::2]   )}
							# Everything else
							else:
									return dict(re.findall(R"(\w+): \"([^\s]+)\"",_A   )   )






					@skip
					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   "test_project_with_config"
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   Accelerator(log_with="wandb"   )
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
							SCREAMING_SNAKE_CASE_  :		Dict    				=   {"wandb": {"tags": ["my_tag"]}}
							accelerator.init_trackers(_A,_A,_A   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
							accelerator.log(_A,step=0   )
							accelerator.end_training()
							# The latest offline log is stored at wandb/latest-run/*.wandb
							for child in Path(F'{self.tmpdir}/wandb/latest-run'   ).glob("*"   ):
									if child.is_file() and child.suffix == ".wandb":
											SCREAMING_SNAKE_CASE_  :		Tuple    				=   subprocess.check_output(
											    ["wandb", "sync", "--view", "--verbose", str(_A   )],env=os.environ.copy()   ).decode("utf8","ignore"   )
											break

        # Check HPS through careful parsing and cleaning
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.parse_log(_A,"config"   )
							self.assertEqual(logged_items["num_iterations"],"12"   )
							self.assertEqual(logged_items["learning_rate"],"0.01"   )
							self.assertEqual(logged_items["some_boolean"],"false"   )
							self.assertEqual(logged_items["some_string"],"some_value"   )
							self.assertEqual(logged_items["some_string"],"some_value"   )

							# Run tags
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.parse_log(_A,"run",_A   )
							self.assertEqual(logged_items["tags"],"my_tag"   )

							# Actual logging
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.parse_log(_A,"history"   )
							self.assertEqual(logged_items["total_loss"],"0.1"   )
							self.assertEqual(logged_items["iteration"],"1"   )
							self.assertEqual(logged_items["my_text"],"some_value"   )
							self.assertEqual(logged_items["_step"],"0"   )







def 				_snake_case     (       self							:       Union[str, Any]       ,			lowerCAmelCase							:       str       ,			lowerCAmelCase							:       str     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]    				=   run_name
		SCREAMING_SNAKE_CASE_  :		Any    				=   OfflineExperiment(project_name=lowerCAmelCase       ,			offline_directory=lowerCAmelCase     )
		logger.info(f'Initialized offline CometML project {self.run_name}'     )
		logger.info("Make sure to log any initial configurations with `self.store_init_configuration` before training!"     )






@require_comet_ml
@mock.patch.object(A__   ,		'__init__'   ,		A__ )
class 		a__						( unittest.TestCase ):

					@staticmethod
					def     __UpperCamelCase       (   _A				:	List[str],_A				:	str,_A				:	bool = False   ):







							"""simple docstring"""






							for log in log_list:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   json.loads(_A   )["payload"]
									if is_param and "param" in j.keys():
											if j["param"]["paramName"] == key:
													return j["param"]["paramValue"]
									if "log_other" in j.keys():
											if j["log_other"]["key"] == key:
													return j["log_other"]["val"]
									if "metric" in j.keys():
											if j["metric"]["metricName"] == key:
													return j["metric"]["metricValue"]

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							with tempfile.TemporaryDirectory() as d:
									SCREAMING_SNAKE_CASE_  :		Any    				=   CometMLTracker("test_project_with_config",_A   )
									SCREAMING_SNAKE_CASE_  :		int    				=   Accelerator(log_with=_A   )
									SCREAMING_SNAKE_CASE_  :		Any    				=   {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
									accelerator.init_trackers(_A,_A   )
									accelerator.end_training()
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   os.listdir(_A   )[0]  # Comet is nice, it's just a zip file here
									# We parse the raw logs
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   os.path.join(_A,_A   )
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   zipfile.ZipFile(_A,"r"   )
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   archive.open("messages.json"   ).read().decode("utf-8"   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   log.split("\n"   )[:-1]
							self.assertEqual(self.get_value_from_key(_A,"num_iterations",_A   ),12   )
							self.assertEqual(self.get_value_from_key(_A,"learning_rate",_A   ),0.01   )
							self.assertEqual(self.get_value_from_key(_A,"some_boolean",_A   ),_A   )
							self.assertEqual(self.get_value_from_key(_A,"some_string",_A   ),"some_value"   )






					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							with tempfile.TemporaryDirectory() as d:
									SCREAMING_SNAKE_CASE_  :		str    				=   CometMLTracker("test_project_with_config",_A   )
									SCREAMING_SNAKE_CASE_  :		Any    				=   Accelerator(log_with=_A   )
									accelerator.init_trackers(_A   )
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
									accelerator.log(_A,step=0   )
									accelerator.end_training()
									SCREAMING_SNAKE_CASE_  :		Any    				=   os.listdir(_A   )[0]  # Comet is nice, it's just a zip file here
									# We parse the raw logs
									SCREAMING_SNAKE_CASE_  :		Any    				=   os.path.join(_A,_A   )
									SCREAMING_SNAKE_CASE_  :		Any    				=   zipfile.ZipFile(_A,"r"   )
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   archive.open("messages.json"   ).read().decode("utf-8"   )
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   log.split("\n"   )[:-1]
							self.assertEqual(self.get_value_from_key(_A,"curr_step",_A   ),0   )
							self.assertEqual(self.get_value_from_key(_A,"total_loss"   ),0.1   )
							self.assertEqual(self.get_value_from_key(_A,"iteration"   ),1   )
							self.assertEqual(self.get_value_from_key(_A,"my_text"   ),"some_value"   )







class 		a__						( A__ ):
					A     	=							[
					    'total_loss',
					    'iteration',
					    'my_text',
					    'learning_rate',
					    'num_iterations',
					    'some_boolean',
					    'some_string',
					]

					A     	=							'my_custom_tracker'
					A     	=							False

					def __init__(   self				:	str,_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   open(F'{dir}/log.csv',"w+"   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   csv.DictWriter(self.f,fieldnames=self._col_names   )
							self.writer.writeheader()

					@property
					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							return self.writer

					def     __UpperCamelCase       (   self				:	Dict,_A				:	dict   ):







							"""simple docstring"""






							logger.info("Call init"   )
							self.writer.writerow(_A   )

					def     __UpperCamelCase       (   self				:	List[str],_A				:	dict,_A				:	Optional[int]   ):







							"""simple docstring"""






							logger.info("Call log"   )
							self.writer.writerow(_A   )






					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							self.f.close()







class 		a__						( unittest.TestCase ):

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							with tempfile.TemporaryDirectory() as d:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   MyCustomTracker(_A   )
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   Accelerator(log_with=_A   )
									SCREAMING_SNAKE_CASE_  :		Any    				=   {"num_iterations": 12, "learning_rate": 1E-2, "some_boolean": False, "some_string": "some_value"}
									accelerator.init_trackers("Some name",_A   )
									accelerator.end_training()
									with open(F'{d}/log.csv',"r"   ) as f:
											SCREAMING_SNAKE_CASE_  :		str    				=   csv.DictReader(_A   )
											SCREAMING_SNAKE_CASE_  :		int    				=   next(_A   )
											SCREAMING_SNAKE_CASE_  :		List[Any]    				=   {
											    "total_loss": "",
											    "iteration": "",
											    "my_text": "",
											    "learning_rate": "0.01",
											    "num_iterations": "12",
											    "some_boolean": "False",
											    "some_string": "some_value",
											}
											self.assertDictEqual(_A,_A   )






					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							with tempfile.TemporaryDirectory() as d:
									SCREAMING_SNAKE_CASE_  :		str    				=   MyCustomTracker(_A   )
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   Accelerator(log_with=_A   )
									accelerator.init_trackers("Some name"   )
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   {"total_loss": 0.1, "iteration": 1, "my_text": "some_value"}
									accelerator.log(_A,step=0   )
									accelerator.end_training()
									with open(F'{d}/log.csv',"r"   ) as f:
											SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   csv.DictReader(_A   )
											SCREAMING_SNAKE_CASE_  :		Dict    				=   next(_A   )
											SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   {
											    "total_loss": "0.1",
											    "iteration": "1",
											    "my_text": "some_value",
											    "learning_rate": "",
											    "num_iterations": "",
											    "some_boolean": "",
											    "some_string": "",
											}
											self.assertDictEqual(_A,_A   )

import unittest

from transformers import LayoutLMConfig, 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    LayoutLMForMaskedLM,
		    LayoutLMForQuestionAnswering,
		    LayoutLMForSequenceClassification,
		    LayoutLMForTokenClassification,
		    LayoutLMModel,
		)






class 		a__						:

					def __init__(   self				:	Optional[int],_A				:	List[str],_A				:	Union[str, Any]=13,_A				:	Union[str, Any]=7,_A				:	Tuple=True,_A				:	Dict=True,_A				:	Optional[int]=True,_A				:	List[Any]=True,_A				:	Optional[int]=99,_A				:	List[str]=32,_A				:	List[Any]=5,_A				:	Dict=4,_A				:	Union[str, Any]=37,_A				:	Dict="gelu",_A				:	Optional[Any]=0.1,_A				:	Dict=0.1,_A				:	str=512,_A				:	Tuple=16,_A				:	str=2,_A				:	str=0.02,_A				:	Tuple=3,_A				:	Optional[Any]=4,_A				:	str=None,_A				:	Tuple=1000,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   parent
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   batch_size
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   seq_length
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   is_training
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   use_input_mask
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   use_token_type_ids
							SCREAMING_SNAKE_CASE_  :		int    				=   use_labels
							SCREAMING_SNAKE_CASE_  :		Dict    				=   vocab_size
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   hidden_size
							SCREAMING_SNAKE_CASE_  :		str    				=   num_hidden_layers
							SCREAMING_SNAKE_CASE_  :		str    				=   num_attention_heads
							SCREAMING_SNAKE_CASE_  :		Dict    				=   intermediate_size
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   hidden_act
							SCREAMING_SNAKE_CASE_  :		Any    				=   hidden_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		Any    				=   type_vocab_size
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   type_sequence_label_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   initializer_range
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   num_labels
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   num_choices
							SCREAMING_SNAKE_CASE_  :		int    				=   scope
							SCREAMING_SNAKE_CASE_  :		int    				=   range_bbox

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size   )

							SCREAMING_SNAKE_CASE_  :		str    				=   ids_tensor([self.batch_size, self.seq_length, 4],self.range_bbox   )
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]   ):
									for j in range(bbox.shape[1]   ):
											if bbox[i, j, 3] < bbox[i, j, 1]:
													SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   bbox[i, j, 3]
													SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   bbox[i, j, 1]
													SCREAMING_SNAKE_CASE_  :		Tuple    				=   t
											if bbox[i, j, 2] < bbox[i, j, 0]:
													SCREAMING_SNAKE_CASE_  :		Any    				=   bbox[i, j, 2]
													SCREAMING_SNAKE_CASE_  :		str    				=   bbox[i, j, 0]
													SCREAMING_SNAKE_CASE_  :		Tuple    				=   t

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   None
							if self.use_input_mask:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   ids_tensor([self.batch_size, self.seq_length],vocab_size=2   )

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

							SCREAMING_SNAKE_CASE_  :		int    				=   None
							SCREAMING_SNAKE_CASE_  :		str    				=   None
							SCREAMING_SNAKE_CASE_  :		Dict    				=   None
							if self.use_labels:
									SCREAMING_SNAKE_CASE_  :		Any    				=   ids_tensor([self.batch_size],self.type_sequence_label_size   )
									SCREAMING_SNAKE_CASE_  :		Any    				=   ids_tensor([self.batch_size, self.seq_length],self.num_labels   )
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   ids_tensor([self.batch_size],self.num_choices   )

							SCREAMING_SNAKE_CASE_  :		str    				=   self.get_config()

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

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							return LayoutLMConfig(
							    vocab_size=self.vocab_size,hidden_size=self.hidden_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,initializer_range=self.initializer_range,)

					def     __UpperCamelCase       (   self				:	Any,_A				:	Union[str, Any],_A				:	Union[str, Any],_A				:	str,_A				:	Dict,_A				:	Any,_A				:	Any,_A				:	Tuple,_A				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   LayoutLMModel(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   model(_A,_A,attention_mask=_A,token_type_ids=_A   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   model(_A,_A,token_type_ids=_A   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   model(_A,_A   )
							self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)   )
							self.parent.assertEqual(result.pooler_output.shape,(self.batch_size, self.hidden_size)   )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   LayoutLMForMaskedLM(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   model(_A,_A,attention_mask=_A,token_type_ids=_A,labels=_A   )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)   )

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	List[str],_A				:	Optional[Any],_A				:	Optional[int],_A				:	int,_A				:	int,_A				:	int,_A				:	int,_A				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.num_labels
							SCREAMING_SNAKE_CASE_  :		Any    				=   LayoutLMForSequenceClassification(_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Any    				=   model(
							    _A,_A,attention_mask=_A,token_type_ids=_A,labels=_A   )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)   )

					def     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	int,_A				:	List[Any],_A				:	Any,_A				:	Any,_A				:	Optional[int],_A				:	List[Any],_A				:	Union[str, Any],_A				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.num_labels
							SCREAMING_SNAKE_CASE_  :		int    				=   LayoutLMForTokenClassification(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		int    				=   model(_A,_A,attention_mask=_A,token_type_ids=_A,labels=_A   )
							self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)   )

					def     __UpperCamelCase       (   self				:	Any,_A				:	int,_A				:	Dict,_A				:	List[Any],_A				:	List[Any],_A				:	Any,_A				:	Union[str, Any],_A				:	str,_A				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   LayoutLMForQuestionAnswering(config=_A   )
							model.to(_A   )
							model.eval()
							SCREAMING_SNAKE_CASE_  :		Any    				=   model(
							    _A,bbox=_A,attention_mask=_A,token_type_ids=_A,start_positions=_A,end_positions=_A,)
							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     __UpperCamelCase       (   self				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.prepare_config_and_inputs()
							(
							    (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   (
							    SCREAMING_SNAKE_CASE_
							)				,   
							)  :		Dict    				=   config_and_inputs
							SCREAMING_SNAKE_CASE_  :		Any    				=   {
							    "input_ids": input_ids,
							    "bbox": bbox,
							    "token_type_ids": token_type_ids,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict







@require_torch
class 		a__						( A__   ,		A__   ,		unittest.TestCase ):
					A     	=							(
					    (
					        LayoutLMModel,
					        LayoutLMForMaskedLM,
					        LayoutLMForSequenceClassification,
					        LayoutLMForTokenClassification,
					        LayoutLMForQuestionAnswering,
					    )
					    if is_torch_available()
					    else None
					)
					A     	=							(
					    {
					        'document-question-answering': LayoutLMForQuestionAnswering,
					        'feature-extraction': LayoutLMModel,
					        'fill-mask': LayoutLMForMaskedLM,
					        'text-classification': LayoutLMForSequenceClassification,
					        'token-classification': LayoutLMForTokenClassification,
					        'zero-shot': LayoutLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					A     	=							True

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   LayoutLMModelTester(self   )
							SCREAMING_SNAKE_CASE_  :		str    				=   ConfigTester(self,config_class=_A,hidden_size=37   )

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							self.config_tester.run_common_tests()

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A   )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   type
									self.model_tester.create_and_check_model(*_A   )

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_A   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_A   )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_A   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_A   )






					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							pass







def 				_snake_case     (       ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]       ,			device=lowerCAmelCase     )  # noqa: E231
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]       ,			device=lowerCAmelCase     )  # noqa: E231
		SCREAMING_SNAKE_CASE_  :		Dict    				=   torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]       ,			device=lowerCAmelCase     )  # noqa: E231
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]       ,			device=lowerCAmelCase     )  # noqa: E231
		# these are sequence labels (i.e. at the token level)
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]       ,			device=lowerCAmelCase     )  # noqa: E231
		# fmt: on

		return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 		a__						( unittest.TestCase ):

					@slow
					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"   ).to(_A   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   prepare_layoutlm_batch_inputs()

							# forward pass
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   model(input_ids=_A,bbox=_A,attention_mask=_A,token_type_ids=_A   )

							# test the sequence output on [0, :3, :3]
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.tensor(
							    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],device=_A,)

							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3],_A,atol=1E-3   )   )

							# test the pooled output on [1, :3]
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.tensor([-0.6580, -0.0214, 0.8552],device=_A   )

							self.assertTrue(torch.allclose(outputs.pooler_output[1, :3],_A,atol=1E-3   )   )

					@slow
					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased",num_labels=2   ).to(
							    _A   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Dict    				=   prepare_layoutlm_batch_inputs()

							# forward pass
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   model(
							    input_ids=_A,bbox=_A,attention_mask=_A,token_type_ids=_A,labels=torch.tensor([1, 1],device=_A   ),)

							# test whether we get a loss as a scalar
							SCREAMING_SNAKE_CASE_  :		Dict    				=   outputs.loss
							SCREAMING_SNAKE_CASE_  :		str    				=   torch.Size([]   )
							self.assertEqual(loss.shape,_A   )

							# test the shape of the logits
							SCREAMING_SNAKE_CASE_  :		int    				=   outputs.logits
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.Size((2, 2)   )
							self.assertEqual(logits.shape,_A   )

					@slow
					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased",num_labels=13   ).to(
							    _A   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Tuple    				=   prepare_layoutlm_batch_inputs()

							# forward pass
							SCREAMING_SNAKE_CASE_  :		Any    				=   model(
							    input_ids=_A,bbox=_A,attention_mask=_A,token_type_ids=_A,labels=_A   )

							# test the loss calculation to be around 2.65
							# expected_loss = torch.tensor(2.65, device=torch_device)

							# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
							# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

							# test the shape of the logits
							SCREAMING_SNAKE_CASE_  :		str    				=   outputs.logits
							SCREAMING_SNAKE_CASE_  :		int    				=   torch.Size((2, 25, 13)   )
							self.assertEqual(logits.shape,_A   )






					@slow
					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"   ).to(_A   )

							SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		str    				=   prepare_layoutlm_batch_inputs()

							# forward pass
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   model(input_ids=_A,bbox=_A,attention_mask=_A,token_type_ids=_A   )

							# test the shape of the logits
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.Size((2, 25)   )
							self.assertEqual(outputs.start_logits.shape,_A   )
							self.assertEqual(outputs.end_logits.shape,_A   )
