





'''simple docstring'''





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf

					from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
@require_tf
class 			UpperCamelCase_		(  unittest.TestCase					):




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[Any]:
										for model_result in results.values():
															for batch_size, sequence_length in zip(model_result['bs']				,						model_result['ss']  ):
																				_snake_case									=				model_result['result'][batch_size][sequence_length]
																				self.assertIsNotNone(A__  )




					def 	lowerCAmelCase			(		self  )		->							int:
										_snake_case									=				'sshleifer/tiny-gpt2'
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=A__				,						multi_process=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__  )
										_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 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										_snake_case									=				'sgugger/tiny-distilbert-classification'
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=A__				,						only_pretrain_model=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__  )
										_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 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				'sshleifer/tiny-gpt2'
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__  )
										_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 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				'sshleifer/tiny-gpt2'
										_snake_case									=				AutoConfig.from_pretrained(A__  )
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						eager_mode=A__				,						multi_process=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__				,						[config]  )
										_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 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				'sshleifer/tiny-gpt2'
										_snake_case									=				AutoConfig.from_pretrained(A__  )
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__				,						[config]  )
										_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 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				'sshleifer/tiny-gpt2'
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__  )
										_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 	lowerCAmelCase			(		self  )		->							str:
										_snake_case									=				'sshleifer/tiny-gpt2'
										_snake_case									=				AutoConfig.from_pretrained(A__  )
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__				,						[config]  )
										_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 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										_snake_case									=				'patrickvonplaten/t5-tiny-random'
										_snake_case									=				AutoConfig.from_pretrained(A__  )
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						multi_process=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__				,						configs=[config]  )
										_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(is_tf_available() and len(tf.config.list_physical_devices('GPU'  )  ) == 0				,						'Cannot do xla on CPU.'  )
					def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
										_snake_case									=				'sshleifer/tiny-gpt2'
										_snake_case									=				TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]				,						training=A__				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						use_xla=A__				,						multi_process=A__				,						)
										_snake_case									=				TensorFlowBenchmark(A__  )
										_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 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				'sshleifer/tiny-gpt2'
										with tempfile.TemporaryDirectory() as tmp_dir:
															_snake_case									=				TensorFlowBenchmarkArguments(
															    models=[MODEL_ID]				,						inference=A__				,						save_to_csv=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						inference_time_csv_file=os.path.join(A__				,						'inf_time.csv'  )				,						inference_memory_csv_file=os.path.join(A__				,						'inf_mem.csv'  )				,						env_info_csv_file=os.path.join(A__				,						'env.csv'  )				,						multi_process=A__				,						)
															_snake_case									=				TensorFlowBenchmark(A__  )
															benchmark.run()
															self.assertTrue(Path(os.path.join(A__				,						'inf_time.csv'  )  ).exists()  )
															self.assertTrue(Path(os.path.join(A__				,						'inf_mem.csv'  )  ).exists()  )
															self.assertTrue(Path(os.path.join(A__				,						'env.csv'  )  ).exists()  )






					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				'sshleifer/tiny-gpt2'

										def _check_summary_is_not_empty(lowerCAmelCase_  ):
															self.assertTrue(hasattr(A__				,						'sequential'  )  )
															self.assertTrue(hasattr(A__				,						'cumulative'  )  )
															self.assertTrue(hasattr(A__				,						'current'  )  )
															self.assertTrue(hasattr(A__				,						'total'  )  )

										with tempfile.TemporaryDirectory() as tmp_dir:
															_snake_case									=				TensorFlowBenchmarkArguments(
															    models=[MODEL_ID]				,						inference=A__				,						sequence_lengths=[8]				,						batch_sizes=[1]				,						log_filename=os.path.join(A__				,						'log.txt'  )				,						log_print=A__				,						trace_memory_line_by_line=A__				,						eager_mode=A__				,						multi_process=A__				,						)
															_snake_case									=				TensorFlowBenchmark(A__  )
															_snake_case									=				benchmark.run()
															_check_summary_is_not_empty(result.inference_summary  )
															self.assertTrue(Path(os.path.join(A__				,						'log.txt'  )  ).exists()  )










import argparse
from collections import defaultdict

import yaml


UpperCAmelCase_      							=     """docs/source/en/_toctree.yml"""





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]						)			-> str:



					'''simple docstring'''




					_snake_case									=				defaultdict(UpperCamelCase__						)
					_snake_case									=				[]
					_snake_case									=				[]
					for doc in doc_list:
										if "local" in doc:
															counts[doc["local"]] += 1

										if doc["title"].lower() == "overview":
															overview_doc.append({'local': doc['local'], 'title': doc['title']}						)
										else:
															new_doc_list.append(UpperCamelCase__						)

					_snake_case									=				new_doc_list
					_snake_case									=				[key for key, value in counts.items() if value > 1]

					_snake_case									=				[]
					for duplicate_key in duplicates:
										_snake_case									=				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}						)
										if len(UpperCamelCase__						) > 1:
															raise ValueError(
															    F'''{duplicate_key} is present several times in the documentation table of content at '''
															    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
															    'others.'						)
										# Only add this once
										new_doc.append({'local': duplicate_key, 'title': titles[0]}						)

					# Add none duplicate-keys
					new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]						)
					_snake_case									=				sorted(UpperCamelCase__       ,    key=lambda UpperCamelCase__						: s["title"].lower()						)

					# "overview" gets special treatment and is always first
					if len(UpperCamelCase__						) > 1:
										raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'						)

					overview_doc.extend(UpperCamelCase__						)

					# Sort
					return overview_doc





def 						lowerCamelCase__     (     UpperCamelCase__			:       Dict=False						)			-> Optional[int]:



					'''simple docstring'''




					with open(UpperCamelCase__       ,    encoding='utf-8'						) as f:
										_snake_case									=				yaml.safe_load(f.read()						)

					# Get to the API doc
					_snake_case									=				0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					_snake_case									=				content[api_idx]['sections']

					# Then to the model doc
					_snake_case									=				0
					while api_doc[scheduler_idx]["title"] != "Schedulers":
										scheduler_idx += 1

					_snake_case									=				api_doc[scheduler_idx]['sections']
					_snake_case									=				clean_doc_toc(UpperCamelCase__						)

					_snake_case									=				False
					if new_scheduler_doc != scheduler_doc:
										_snake_case									=				True
										if overwrite:
															_snake_case									=				new_scheduler_doc

					if diff:
										if overwrite:
															_snake_case									=				api_doc
															with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'						) as f:
																				f.write(yaml.dump(UpperCamelCase__       ,    allow_unicode=UpperCamelCase__						)						)
										else:
															raise ValueError(
															    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'						)





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple=False						)			-> List[Any]:



					'''simple docstring'''




					with open(UpperCamelCase__       ,    encoding='utf-8'						) as f:
										_snake_case									=				yaml.safe_load(f.read()						)

					# Get to the API doc
					_snake_case									=				0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					_snake_case									=				content[api_idx]['sections']

					# Then to the model doc
					_snake_case									=				0
					while api_doc[pipeline_idx]["title"] != "Pipelines":
										pipeline_idx += 1

					_snake_case									=				False
					_snake_case									=				api_doc[pipeline_idx]['sections']
					_snake_case									=				[]

					# sort sub pipeline docs
					for pipeline_doc in pipeline_docs:
										if "section" in pipeline_doc:
															_snake_case									=				pipeline_doc['section']
															_snake_case									=				clean_doc_toc(UpperCamelCase__						)
															if overwrite:
																				_snake_case									=				new_sub_pipeline_doc
										new_pipeline_docs.append(UpperCamelCase__						)

					# sort overall pipeline doc
					_snake_case									=				clean_doc_toc(UpperCamelCase__						)

					if new_pipeline_docs != pipeline_docs:
										_snake_case									=				True
										if overwrite:
															_snake_case									=				new_pipeline_docs

					if diff:
										if overwrite:
															_snake_case									=				api_doc
															with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'						) as f:
																				f.write(yaml.dump(UpperCamelCase__       ,    allow_unicode=UpperCamelCase__						)						)
										else:
															raise ValueError(
															    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'						)


if __name__ == "__main__":
					UpperCAmelCase_      							=     argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					UpperCAmelCase_      							=     parser.parse_args()

					check_scheduler_doc(args.fix_and_overwrite)
					check_pipeline_doc(args.fix_and_overwrite)



