




import os
import tempfile
import unittest
from pathlib import Path

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


if is_torch_available():
			from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

@require_torch
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													for model_result in results.values():
																				for batch_size, sequence_length in zip(model_result['bs']				,							model_result['ss']		):
																											__SCREAMING_SNAKE_CASE									= model_result['''result'''][batch_size][sequence_length]
																											self.assertIsNotNone(lowercase_		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result		)
													self.check_results_dict_not_empty(results.memory_inference_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sgugger/tiny-distilbert-classification'''
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							only_pretrain_model=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result		)
													self.check_results_dict_not_empty(results.memory_inference_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							torchscript=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result		)
													self.check_results_dict_not_empty(results.memory_inference_result		)






						@unittest.skipIf(torch_device == 'cpu'				,							'Cant do half precision'		)
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							fpaa=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result		)
													self.check_results_dict_not_empty(results.memory_inference_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(lowercase_		)
													# set architectures equal to `None`
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_				,							configs=[config]		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result		)
													self.check_results_dict_not_empty(results.memory_inference_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result		)
													self.check_results_dict_not_empty(results.memory_train_result		)






						@unittest.skipIf(torch_device == 'cpu'				,							'Can\'t do half precision'		)
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							fpaa=lowercase_				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result		)
													self.check_results_dict_not_empty(results.memory_train_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(lowercase_		)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_				,							configs=[config]		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result		)
													self.check_results_dict_not_empty(results.memory_inference_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tinier_bart'''
													__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(lowercase_		)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_				,							configs=[config]		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result		)
													self.check_results_dict_not_empty(results.memory_inference_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(lowercase_		)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_				,							configs=[config]		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result		)
													self.check_results_dict_not_empty(results.memory_train_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tinier_bart'''
													__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(lowercase_		)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
													    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							multi_process=lowercase_				,							)
													__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_				,							configs=[config]		)
													__SCREAMING_SNAKE_CASE									= benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result		)
													self.check_results_dict_not_empty(results.memory_train_result		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''
													with tempfile.TemporaryDirectory() as tmp_dir:
																				__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
																				    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							save_to_csv=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							inference_time_csv_file=os.path.join(lowercase_				,							'inf_time.csv'		)				,							train_memory_csv_file=os.path.join(lowercase_				,							'train_mem.csv'		)				,							inference_memory_csv_file=os.path.join(lowercase_				,							'inf_mem.csv'		)				,							train_time_csv_file=os.path.join(lowercase_				,							'train_time.csv'		)				,							env_info_csv_file=os.path.join(lowercase_				,							'env.csv'		)				,							multi_process=lowercase_				,							)
																				__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_		)
																				benchmark.run()
																				self.assertTrue(Path(os.path.join(lowercase_				,							'inf_time.csv'		)		).exists()		)
																				self.assertTrue(Path(os.path.join(lowercase_				,							'train_time.csv'		)		).exists()		)
																				self.assertTrue(Path(os.path.join(lowercase_				,							'inf_mem.csv'		)		).exists()		)
																				self.assertTrue(Path(os.path.join(lowercase_				,							'train_mem.csv'		)		).exists()		)
																				self.assertTrue(Path(os.path.join(lowercase_				,							'env.csv'		)		).exists()		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= '''sshleifer/tiny-gpt2'''

													def _check_summary_is_not_empty(_A		):
																				self.assertTrue(hasattr(lowercase_				,							'sequential'		)		)
																				self.assertTrue(hasattr(lowercase_				,							'cumulative'		)		)
																				self.assertTrue(hasattr(lowercase_				,							'current'		)		)
																				self.assertTrue(hasattr(lowercase_				,							'total'		)		)

													with tempfile.TemporaryDirectory() as tmp_dir:
																				__SCREAMING_SNAKE_CASE									= PyTorchBenchmarkArguments(
																				    models=[MODEL_ID]				,							training=lowercase_				,							inference=lowercase_				,							sequence_lengths=[8]				,							batch_sizes=[1]				,							log_filename=os.path.join(lowercase_				,							'log.txt'		)				,							log_print=lowercase_				,							trace_memory_line_by_line=lowercase_				,							multi_process=lowercase_				,							)
																				__SCREAMING_SNAKE_CASE									= PyTorchBenchmark(lowercase_		)
																				__SCREAMING_SNAKE_CASE									= benchmark.run()
																				_check_summary_is_not_empty(result.inference_summary		)
																				_check_summary_is_not_empty(result.train_summary		)
																				self.assertTrue(Path(os.path.join(lowercase_				,							'log.txt'		)		).exists()		)











import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class        UpperCAmelCase_  (       UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  LayoutLMTokenizer
						UpperCamelCase__      :			Any      =  LayoutLMTokenizerFast
						UpperCamelCase__      :			Optional[int]      =  True
						UpperCamelCase__      :			int      =  True






						def 	_A     (   self		):







													'''simple docstring'''

													super().setUp()

													__SCREAMING_SNAKE_CASE									= [
													    '[UNK]',
													    '[CLS]',
													    '[SEP]',
													    'want',
													    '##want',
													    '##ed',
													    'wa',
													    'un',
													    'runn',
													    '##ing',
													    ',',
													    'low',
													    'lowest',
													]
													__SCREAMING_SNAKE_CASE									= os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['vocab_file']		)
													with open(self.vocab_file				,							'w'				,							encoding='utf-8'		) as vocab_writer:
																				vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)






						def 	_A     (   self				,							**_A		):







													'''simple docstring'''

													return LayoutLMTokenizer.from_pretrained(self.tmpdirname				,							**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 'UNwant\u00E9d,running'
													__SCREAMING_SNAKE_CASE									= 'unwanted, running'
													return input_text, output_text






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.tokenizer_class(self.vocab_file		)

													__SCREAMING_SNAKE_CASE									= tokenizer.tokenize('UNwant\u00E9d,running'		)
													self.assertListEqual(_A				,							['un', '##want', '##ed', ',', 'runn', '##ing']		)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_A		)				,							[7, 4, 5, 10, 8, 9]		)






						def 	_A     (   self		):







													'''simple docstring'''

													pass






