

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
_UpperCamelCase 			=		"src/transformers"
_UpperCamelCase 			=		"docs/source/en/tasks"







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):


      with open(_SCREAMING_SNAKE_CASE   ,	'''r'''   ,	encoding='''utf-8'''   ,	newline='''\n'''						) as f:
            __lowerCAmelCase					:						Union[str, Any]					=					f.readlines()
      # Find the start prompt.
      __lowerCAmelCase					:						int					=					0
      while not lines[start_index].startswith(_SCREAMING_SNAKE_CASE						):
            start_index += 1
      start_index += 1

      __lowerCAmelCase					:						Tuple					=					start_index
      while not lines[end_index].startswith(_SCREAMING_SNAKE_CASE						):
            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.
_UpperCamelCase 			=		direct_transformers_import(TRANSFORMERS_PATH)

_UpperCamelCase 			=		{
    "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`).
_UpperCamelCase 			=		{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Dict					=					TASK_GUIDE_TO_MODELS[task_guide]
      __lowerCAmelCase					:						Dict					=					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_SCREAMING_SNAKE_CASE   ,	set()						)
      __lowerCAmelCase					:						Optional[int]					=					{
          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 		_lowercase (     lowercase__   ,	lowercase__=False						):
      __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Optional[Any]					=					_find_text_in_file(
          filename=os.path.join(_SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE						)   ,	start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''   ,	end_prompt='''<!--End of the generated tip-->'''   ,	)

      __lowerCAmelCase					:						Any					=					get_model_list_for_task(_SCREAMING_SNAKE_CASE						)

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(_SCREAMING_SNAKE_CASE   ,	_SCREAMING_SNAKE_CASE						)   ,	'''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__":
 _UpperCamelCase 			=		argparse.ArgumentParser()
 parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
 _UpperCamelCase 			=		parser.parse_args()

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









from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			"efficientformer"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    List[int] = [3, 2, 6, 4]						,lowerCamelCase__						:    List[int] = [48, 96, 224, 448]						,lowerCamelCase__						:    List[bool] = [True, True, True, True]						,lowerCamelCase__						:    int = 448						,lowerCamelCase__						:    int = 32						,lowerCamelCase__						:    int = 4						,lowerCamelCase__						:    int = 7						,lowerCamelCase__						:    int = 5						,lowerCamelCase__						:    int = 8						,lowerCamelCase__						:    int = 4						,lowerCamelCase__						:    float = 0.0						,lowerCamelCase__						:    int = 16						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 2						,lowerCamelCase__						:    int = 1						,lowerCamelCase__						:    float = 0.0						,lowerCamelCase__						:    int = 1						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    float = 1e-5						,lowerCamelCase__						:    str = "gelu"						,lowerCamelCase__						:    float = 0.02						,lowerCamelCase__						:    float = 1e-1_2						,lowerCamelCase__						:    int = 224						,lowerCamelCase__						:    float = 1e-0_5						,**lowerCamelCase__						:    str						,)     ->		None:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						hidden_sizes
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						mlp_expansion_ratio
           SCREAMING_SNAKE_CASE		=						downsamples
           SCREAMING_SNAKE_CASE		=						dim
           SCREAMING_SNAKE_CASE		=						key_dim
           SCREAMING_SNAKE_CASE		=						attention_ratio
           SCREAMING_SNAKE_CASE		=						resolution
           SCREAMING_SNAKE_CASE		=						pool_size
           SCREAMING_SNAKE_CASE		=						downsample_patch_size
           SCREAMING_SNAKE_CASE		=						downsample_stride
           SCREAMING_SNAKE_CASE		=						downsample_pad
           SCREAMING_SNAKE_CASE		=						drop_path_rate
           SCREAMING_SNAKE_CASE		=						num_metaad_blocks
           SCREAMING_SNAKE_CASE		=						distillation
           SCREAMING_SNAKE_CASE		=						use_layer_scale
           SCREAMING_SNAKE_CASE		=						layer_scale_init_value
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						batch_norm_eps


