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      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Any     ,   __magic_name__       :				Tuple      )     ->		Optional[Any]:
        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(__magic_name__      )







  def       __A   (							self       :				Tuple      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   eager_mode=__magic_name__     ,   multi_process=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__      )
        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       :				Any      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     "sgugger/tiny-distilbert-classification"
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=__magic_name__     ,   only_pretrain_model=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__      )
        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       :				Union[str, Any]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__      )
        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       :				List[str]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"
        SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   eager_mode=__magic_name__     ,   multi_process=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__     ,   [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       :				int      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"
        SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__     ,   [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       :				List[str]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__      )
        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       :				Optional[Any]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"
        SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__     ,   [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       :				Dict      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     "patrickvonplaten/t5-tiny-random"
        SCREAMING_SNAKE_CASE_      =     AutoConfig.from_pretrained(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   multi_process=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__     ,   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      )







  @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"      )      ) == 0     ,   "Cannot do xla on CPU."      )
  def       __A   (							self       :				Dict      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
            models=[MODEL_ID]     ,   training=__magic_name__     ,   inference=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   use_xla=__magic_name__     ,   multi_process=__magic_name__     ,   )
        SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__      )
        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       :				Optional[int]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"
        with tempfile.TemporaryDirectory() as tmp_dir:
              SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmarkArguments(
                  models=[MODEL_ID]     ,   inference=__magic_name__     ,   save_to_csv=__magic_name__     ,   sequence_lengths=[8]     ,   batch_sizes=[1]     ,   inference_time_csv_file=os.path.join(__magic_name__     ,   "inf_time.csv"      )     ,   inference_memory_csv_file=os.path.join(__magic_name__     ,   "inf_mem.csv"      )     ,   env_info_csv_file=os.path.join(__magic_name__     ,   "env.csv"      )     ,   multi_process=__magic_name__     ,   )
              SCREAMING_SNAKE_CASE_      =     TensorFlowBenchmark(__magic_name__      )
              benchmark.run()
              self.assertTrue(Path(os.path.join(__magic_name__     ,   "inf_time.csv"      )      ).exists()      )
              self.assertTrue(Path(os.path.join(__magic_name__     ,   "inf_mem.csv"      )      ).exists()      )
              self.assertTrue(Path(os.path.join(__magic_name__     ,   "env.csv"      )      ).exists()      )







  def       __A   (							self       :				Tuple      )     ->		Dict:
        SCREAMING_SNAKE_CASE_      =     "sshleifer/tiny-gpt2"

        def _check_summary_is_not_empty(__magic_name__       :				Optional[Any]      ):
              self.assertTrue(hasattr(__magic_name__     ,   "sequential"      )      )
              self.assertTrue(hasattr(__magic_name__     ,   "cumulative"      )      )
              self.assertTrue(hasattr(__magic_name__     ,   "current"      )      )
              self.assertTrue(hasattr(__magic_name__     ,   "total"      )      )

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



import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A   :     Tuple       =				"src/transformers"
A   :     Optional[Any]       =				"docs/source/en/tasks"


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      with open(__UpperCamelCase   ,					"r"   ,					encoding="utf-8"   ,					newline="\n" ) as f:
            SCREAMING_SNAKE_CASE_      =     f.readlines()
      # Find the start prompt.
      SCREAMING_SNAKE_CASE_      =     0
      while not lines[start_index].startswith(__UpperCamelCase ):
            start_index += 1
      start_index += 1

      SCREAMING_SNAKE_CASE_      =     start_index
      while not lines[end_index].startswith(__UpperCamelCase ):
            end_index += 1
      end_index -= 1

      while len(lines[start_index] ) <= 1:
            start_index += 1
      while len(lines[end_index] ) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A   :     List[str]       =				direct_transformers_import(TRANSFORMERS_PATH)

A   :     List[Any]       =				{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A   :     Any       =				{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     TASK_GUIDE_TO_MODELS[task_guide]
      SCREAMING_SNAKE_CASE_      =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCamelCase   ,					set() )
      SCREAMING_SNAKE_CASE_      =     {
          code: name
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if (code in model_maping_names or code in special_model_types)
      }
      return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()] ) + "\n"


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=False ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     _find_text_in_file(
          filename=os.path.join(__UpperCamelCase   ,					__UpperCamelCase )   ,					start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"   ,					end_prompt="<!--End of the generated tip-->"   ,					)

      SCREAMING_SNAKE_CASE_      =     get_model_list_for_task(__UpperCamelCase )

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(__UpperCamelCase   ,					__UpperCamelCase )   ,					"w"   ,					encoding="utf-8"   ,					newline="\n" ) as f:
                        f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
            else:
                  raise ValueError(
                      F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
                      " to fix this." )


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

      for task_guide in TASK_GUIDE_TO_MODELS.keys():
            check_model_list_for_task(task_guide, args.fix_and_overwrite)


