

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    :Optional[Any]      =			'src/transformers'
__a    :Tuple      =			'docs/source/en/tasks'

def       __snake_case						( __UpperCamelCase	: List[Any]				,__UpperCamelCase	: Dict				,__UpperCamelCase	: int    ):



      """simple docstring"""

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

      A_						  =				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    :Optional[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    :Optional[Any]      =			{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def       __snake_case						( __UpperCamelCase	: Tuple    ):



      """simple docstring"""

      A_						  =				TASK_GUIDE_TO_MODELS[task_guide]
      A_						  =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCamelCase				,set()    )
      A_						  =				{
          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       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: List[str]=False    ):



      """simple docstring"""

      A_  ,     A_  ,     A_  ,     A_						  =				_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-->"				,)

      A_						  =				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    :int      =			argparse.ArgumentParser()
      parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
      __a    :Optional[Any]      =			parser.parse_args()

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

"""simple docstring"""

import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE_            =    '''1'''
SCREAMING_SNAKE_CASE_            =    '''0'''
SCREAMING_SNAKE_CASE_            =    '''1'''

SCREAMING_SNAKE_CASE_            =    ort.SessionOptions()
SCREAMING_SNAKE_CASE_            =    ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
SCREAMING_SNAKE_CASE_            =    ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
SCREAMING_SNAKE_CASE_            =    ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
SCREAMING_SNAKE_CASE_            =    ort.RunOptions()

SCREAMING_SNAKE_CASE_            =    128
SCREAMING_SNAKE_CASE_            =    1
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)
SCREAMING_SNAKE_CASE_            =    np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
SCREAMING_SNAKE_CASE_            =    time.time()
SCREAMING_SNAKE_CASE_            =    2000
SCREAMING_SNAKE_CASE_            =    {}
for iter in range(max_iters):
   SCREAMING_SNAKE_CASE_            =    sess.run(
       None,
       {
           sess.get_inputs()[0].name: input_ids,
           sess.get_inputs()[1].name: attention_mask,
           sess.get_inputs()[2].name: token_type_ids,
       },
       run_options=run_opt,
   )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))



