


'''simple docstring'''




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		):





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      str    )				->  Any:
        for model_result in results.values():
           for batch_size, sequence_length in zip(model_result['bs']    ,      model_result['ss']    ):
              lowerCAmelCase     =			model_result['result'][batch_size][sequence_length]
              self.assertIsNotNone(UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Optional[Any]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Union[str, Any]:
        lowerCAmelCase     =			'sgugger/tiny-distilbert-classification'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      only_pretrain_model=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Optional[Any]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      torchscript=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			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 							__UpperCAmelCase      ( self       :      Tuple    )				->  int:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      fpaa=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  List[str]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        # set architectures equal to `None`
        lowerCAmelCase     =			None
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      configs=[config]    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  str:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			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 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Optional[Any]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      fpaa=UpperCAmelCase__    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result    )
        self.check_results_dict_not_empty(results.memory_train_result    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      configs=[config]    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  str:
        lowerCAmelCase     =			'sshleifer/tinier_bart'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      configs=[config]    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result    )
        self.check_results_dict_not_empty(results.memory_inference_result    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Union[str, Any]:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      configs=[config]    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result    )
        self.check_results_dict_not_empty(results.memory_train_result    )





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Union[str, Any]:
        lowerCAmelCase     =			'sshleifer/tinier_bart'
        lowerCAmelCase     =			AutoConfig.from_pretrained(UpperCAmelCase__    )
        lowerCAmelCase     =			PyTorchBenchmarkArguments(
            models=[MODEL_ID]    ,      training=UpperCAmelCase__    ,      inference=UpperCAmelCase__    ,      sequence_lengths=[8]    ,      batch_sizes=[1]    ,      multi_process=UpperCAmelCase__    ,      )
        lowerCAmelCase     =			PyTorchBenchmark(UpperCAmelCase__    ,      configs=[config]    )
        lowerCAmelCase     =			benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result    )
        self.check_results_dict_not_empty(results.memory_train_result    )





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





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  int:
        lowerCAmelCase     =			'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(UpperCAmelCase__       :      str    ):
           self.assertTrue(hasattr(UpperCAmelCase__    ,      'sequential'    )    )
           self.assertTrue(hasattr(UpperCAmelCase__    ,      'cumulative'    )    )
           self.assertTrue(hasattr(UpperCAmelCase__    ,      'current'    )    )
           self.assertTrue(hasattr(UpperCAmelCase__    ,      'total'    )    )

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












import argparse
from collections import defaultdict

import yaml


lowercase      :       int									=  """docs/source/en/_toctree.yml"""



def A_	(		A__				)  ->		Tuple:
       a__						:	int =				defaultdict(A__				)
       a__						:	List[str] =				[]
       a__						:	Any =				[]
       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(A__				)

       a__						:	int =				new_doc_list
       a__						:	List[str] =				[key for key, value in counts.items() if value > 1]

       a__						:	Tuple =				[]
       for duplicate_key in duplicates:
              a__						:	int =				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
              if len(A__				) > 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]				)
       a__						:	Any =				sorted(A__		,      key=lambda A__				: s["title"].lower()				)

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

       overview_doc.extend(A__				)

       # Sort
       return overview_doc



def A_	(		A__=False				)  ->		str:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	int =				yaml.safe_load(f.read()				)

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

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

       a__						:	Tuple =				api_doc[scheduler_idx]['sections']
       a__						:	Optional[int] =				clean_doc_toc(A__				)

       a__						:	int =				False
       if new_scheduler_doc != scheduler_doc:
              a__						:	int =				True
              if overwrite:
                     a__						:	int =				new_scheduler_doc

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



def A_	(		A__=False				)  ->		Tuple:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	Optional[Any] =				yaml.safe_load(f.read()				)

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

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

       a__						:	Optional[int] =				False
       a__						:	Union[str, Any] =				api_doc[pipeline_idx]['sections']
       a__						:	List[str] =				[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     a__						:	Any =				pipeline_doc['section']
                     a__						:	Dict =				clean_doc_toc(A__				)
                     if overwrite:
                            a__						:	Optional[Any] =				new_sub_pipeline_doc
              new_pipeline_docs.append(A__				)

       # sort overall pipeline doc
       a__						:	Optional[Any] =				clean_doc_toc(A__				)

       if new_pipeline_docs != pipeline_docs:
              a__						:	List[Any] =				True
              if overwrite:
                     a__						:	Union[str, Any] =				new_pipeline_docs

       if diff:
              if overwrite:
                     a__						:	Dict =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              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__":
    lowercase      :       List[Any]									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

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


