





"""simple docstring"""





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase__				:Optional[Any]				   =       {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowercase__				:Any				   =       logging.get_logger(__name__)




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       int         =       'maskformer'
      _A     :       Dict         =       {'hidden_size': 'mask_feature_size'}
      _A     :       int         =       ['resnet', 'swin']
      _A     :       Tuple         =       ['detr']

      def __init__(							self       :      Any						,				__lowercase       :      int = 256						,				__lowercase       :      int = 256						,				__lowercase       :      float = 0.1						,				__lowercase       :      bool = False						,				__lowercase       :      Optional[Dict] = None						,				__lowercase       :      Optional[Dict] = None						,				__lowercase       :      float = 0.0_2						,				__lowercase       :      float = 1.0						,				__lowercase       :      float = 1.0						,				__lowercase       :      float = 1.0						,				__lowercase       :      float = 20.0						,				__lowercase       :      Optional[bool] = None						,				**__lowercase       :      Dict						,				):


        '''simple docstring'''




        if backbone_config is None:
          # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
          __UpperCAmelCase    :						Optional[int]       =						SwinConfig(
              image_size=384						,				in_channels=3						,				patch_size=4						,				embed_dim=128						,				depths=[2, 2, 18, 2]						,				num_heads=[4, 8, 16, 32]						,				window_size=12						,				drop_path_rate=0.3						,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						,				)

        if isinstance(__lowercase						,				__lowercase ):
          __UpperCAmelCase    :						List[str]       =						backbone_config.pop('''model_type''' )
          __UpperCAmelCase    :						List[Any]       =						CONFIG_MAPPING[backbone_model_type]
          __UpperCAmelCase    :						str       =						config_class.from_dict(__lowercase )

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
          logger.warning_once(
              f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
              f'''Supported model types: {','.join(self.backbones_supported )}''' )

        if decoder_config is None:
          # fall back to https://huggingface.co/facebook/detr-resnet-50
          __UpperCAmelCase    :						Tuple       =						DetrConfig()
        else:
          # verify that the decoder is supported
          __UpperCAmelCase    :						List[str]       =						(
              decoder_config.pop('''model_type''' ) if isinstance(__lowercase						,				__lowercase ) else decoder_config.model_type
          )
          if decoder_type not in self.decoders_supported:
            raise ValueError(
                f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                f''' {','.join(self.decoders_supported )}''' )
          if isinstance(__lowercase						,				__lowercase ):
            __UpperCAmelCase    :						Dict       =						CONFIG_MAPPING[decoder_type]
            __UpperCAmelCase    :						Tuple       =						config_class.from_dict(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						backbone_config
        __UpperCAmelCase    :						List[str]       =						decoder_config
        # main feature dimension for the model
        __UpperCAmelCase    :						Any       =						fpn_feature_size
        __UpperCAmelCase    :						Any       =						mask_feature_size
        # initializer
        __UpperCAmelCase    :						Optional[int]       =						init_std
        __UpperCAmelCase    :						Optional[int]       =						init_xavier_std
        # Hungarian matcher && loss
        __UpperCAmelCase    :						str       =						cross_entropy_weight
        __UpperCAmelCase    :						int       =						dice_weight
        __UpperCAmelCase    :						int       =						mask_weight
        __UpperCAmelCase    :						Tuple       =						use_auxiliary_loss
        __UpperCAmelCase    :						List[Any]       =						no_object_weight
        __UpperCAmelCase    :						str       =						output_auxiliary_logits

        __UpperCAmelCase    :						Tuple       =						self.decoder_config.encoder_attention_heads
        __UpperCAmelCase    :						Optional[Any]       =						self.decoder_config.num_hidden_layers
        super().__init__(**__lowercase )

      @classmethod
      def       A_					(							cls       :      List[Any]						,				__lowercase       :      PretrainedConfig						,				__lowercase       :      PretrainedConfig						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        return cls(
            backbone_config=__lowercase						,				decoder_config=__lowercase						,				**__lowercase						,				)







      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						copy.deepcopy(self.__dict__ )
        __UpperCAmelCase    :						List[str]       =						self.backbone_config.to_dict()
        __UpperCAmelCase    :						Dict       =						self.decoder_config.to_dict()
        __UpperCAmelCase    :						List[Any]       =						self.__class__.model_type
        return output






"""simple docstring"""





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
lowercase__				:int				   =       'src/transformers'
lowercase__				:List[str]				   =       'docs/source/en/tasks'


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->str:





  """simple docstring"""


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

  __UpperCAmelCase    :						Optional[Any]       =						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.
lowercase__				:Any				   =       direct_transformers_import(TRANSFORMERS_PATH)

lowercase__				: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`).
lowercase__				:Union[str, Any]				   =       {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						List[str]       =						TASK_GUIDE_TO_MODELS[task_guide]
  __UpperCAmelCase    :						Dict       =						SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase_  ,			set()  )
  __UpperCAmelCase    :						List[Any]       =						{
      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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=False  )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Dict       =						_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-->'''  ,			)

  __UpperCAmelCase    :						List[str]       =						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__":
  lowercase__				:int				   =       argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  lowercase__				: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)