





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



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Optional[Any]:


      '''simple docstring'''


      with open(_SCREAMING_SNAKE_CASE ,						"""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(_SCREAMING_SNAKE_CASE	):
            start_index += 1
      start_index += 1

      SCREAMING_SNAKE_CASE		=						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.
SCREAMING_SNAKE_CASE_						   =       direct_transformers_import(TRANSFORMERS_PATH)

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



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						TASK_GUIDE_TO_MODELS[task_guide]
      SCREAMING_SNAKE_CASE		=						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_SCREAMING_SNAKE_CASE ,						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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=False	) ->   Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						_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-->""" ,						)

      SCREAMING_SNAKE_CASE		=						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__":
      SCREAMING_SNAKE_CASE_						   =       argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()

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









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			"table-transformer"
     __snake_case    :	Union[str, Any]			=			["past_key_values"]
     __snake_case    :	List[Any]			=			{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    Optional[Any]=None						,lowerCamelCase__						:    List[Any]=3						,lowerCamelCase__						:    Optional[int]=100						,lowerCamelCase__						:    List[Any]=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    List[Any]=8						,lowerCamelCase__						:    Dict=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    Any=8						,lowerCamelCase__						:    Optional[int]=0.0						,lowerCamelCase__						:    int=0.0						,lowerCamelCase__						:    List[Any]=True						,lowerCamelCase__						:    Optional[int]="relu"						,lowerCamelCase__						:    Tuple=256						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    Optional[Any]=0.0						,lowerCamelCase__						:    Tuple=0.0						,lowerCamelCase__						:    List[Any]=0.02						,lowerCamelCase__						:    int=1.0						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    Optional[Any]="sine"						,lowerCamelCase__						:    List[str]="resnet50"						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    int=1						,lowerCamelCase__						:    Dict=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    Union[str, Any]=1						,lowerCamelCase__						:    str=1						,lowerCamelCase__						:    Any=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    str=0.1						,**lowerCamelCase__						:    List[str]						,)     ->		Optional[int]:




           '''simple docstring'''


           if backbone_config is not None and use_timm_backbone:
                 raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""       )

           if not use_timm_backbone:
                 if backbone_config is None:
                       logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]       )
                 elif isinstance(lowerCamelCase__						,lowerCamelCase__       ):
                       SCREAMING_SNAKE_CASE		=						backbone_config.get("""model_type"""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE		=						config_class.from_dict(lowerCamelCase__       )
                 # set timm attributes to None
                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						None, None, None

           SCREAMING_SNAKE_CASE		=						use_timm_backbone
           SCREAMING_SNAKE_CASE		=						backbone_config
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						num_queries
           SCREAMING_SNAKE_CASE		=						d_model
           SCREAMING_SNAKE_CASE		=						encoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						encoder_attention_heads
           SCREAMING_SNAKE_CASE		=						decoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						decoder_layers
           SCREAMING_SNAKE_CASE		=						decoder_attention_heads
           SCREAMING_SNAKE_CASE		=						dropout
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						activation_dropout
           SCREAMING_SNAKE_CASE		=						activation_function
           SCREAMING_SNAKE_CASE		=						init_std
           SCREAMING_SNAKE_CASE		=						init_xavier_std
           SCREAMING_SNAKE_CASE		=						encoder_layerdrop
           SCREAMING_SNAKE_CASE		=						decoder_layerdrop
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						auxiliary_loss
           SCREAMING_SNAKE_CASE		=						position_embedding_type
           SCREAMING_SNAKE_CASE		=						backbone
           SCREAMING_SNAKE_CASE		=						use_pretrained_backbone
           SCREAMING_SNAKE_CASE		=						dilation
           # Hungarian matcher
           SCREAMING_SNAKE_CASE		=						class_cost
           SCREAMING_SNAKE_CASE		=						bbox_cost
           SCREAMING_SNAKE_CASE		=						giou_cost
           # Loss coefficients
           SCREAMING_SNAKE_CASE		=						mask_loss_coefficient
           SCREAMING_SNAKE_CASE		=						dice_loss_coefficient
           SCREAMING_SNAKE_CASE		=						bbox_loss_coefficient
           SCREAMING_SNAKE_CASE		=						giou_loss_coefficient
           SCREAMING_SNAKE_CASE		=						eos_coefficient
           super().__init__(is_encoder_decoder=lowerCamelCase__						,**lowerCamelCase__       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		int:




           '''simple docstring'''


           return self.d_model

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			version.parse("1.11"   )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                   ("""pixel_mask""", {0: """batch"""}),
               ]       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		float:




           '''simple docstring'''


           return 1e-5






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		int:




           '''simple docstring'''


           return 12


