



import functools
import operator

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


a_						:     Dict	     =		logging.get_logger(__name__)

a_						:     Optional[Any]	     =		{
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			'sew'







     def __init__(self				,			__a=32				,			__a=7_68				,			__a=12				,			__a=12				,			__a=30_72				,			__a=2				,			__a="gelu"				,			__a=0.1				,			__a=0.1				,			__a=0.1				,			__a=0.0				,			__a=0.1				,			__a=0.1				,			__a=0.02				,			__a=1E-5				,			__a="group"				,			__a="gelu"				,			__a=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)				,			__a=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,			__a=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,			__a=False				,			__a=1_28				,			__a=16				,			__a=True				,			__a=0.05				,			__a=10				,			__a=2				,			__a=0.0				,			__a=10				,			__a=0				,			__a="mean"				,			__a=False				,			__a=False				,			__a=2_56				,			__a=0				,			__a=1				,			__a=2				,			**__a				,			):






           '''simple docstring'''



           super().__init__(**__a				,			pad_token_id=__a				,			bos_token_id=__a				,			eos_token_id=__a    )
           lowerCamelCase								=		hidden_size
           lowerCamelCase								=		feat_extract_norm
           lowerCamelCase								=		feat_extract_activation
           lowerCamelCase								=		list(__a    )
           lowerCamelCase								=		list(__a    )
           lowerCamelCase								=		list(__a    )
           lowerCamelCase								=		conv_bias
           lowerCamelCase								=		num_conv_pos_embeddings
           lowerCamelCase								=		num_conv_pos_embedding_groups
           lowerCamelCase								=		len(self.conv_dim    )
           lowerCamelCase								=		num_hidden_layers
           lowerCamelCase								=		intermediate_size
           lowerCamelCase								=		squeeze_factor
           lowerCamelCase								=		hidden_act
           lowerCamelCase								=		num_attention_heads
           lowerCamelCase								=		hidden_dropout
           lowerCamelCase								=		attention_dropout
           lowerCamelCase								=		activation_dropout
           lowerCamelCase								=		feat_proj_dropout
           lowerCamelCase								=		final_dropout
           lowerCamelCase								=		layerdrop
           lowerCamelCase								=		layer_norm_eps
           lowerCamelCase								=		initializer_range
           lowerCamelCase								=		vocab_size

           if (
               (len(self.conv_stride    ) != self.num_feat_extract_layers)
               or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
               or (len(self.conv_dim    ) != self.num_feat_extract_layers)
           ):
                 raise ValueError(
                     "Configuration for convolutional layers is incorrect."
                     "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                     F"""but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)"""
                     F"""= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           lowerCamelCase								=		apply_spec_augment
           lowerCamelCase								=		mask_time_prob
           lowerCamelCase								=		mask_time_length
           lowerCamelCase								=		mask_time_min_masks
           lowerCamelCase								=		mask_feature_prob
           lowerCamelCase								=		mask_feature_length
           lowerCamelCase								=		mask_feature_min_masks

           # ctc loss
           lowerCamelCase								=		ctc_loss_reduction
           lowerCamelCase								=		ctc_zero_infinity

           # sequence classification
           lowerCamelCase								=		use_weighted_layer_sum
           lowerCamelCase								=		classifier_proj_size







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return functools.reduce(operator.mul				,			self.conv_stride				,			1    )




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_						:     Any	     =		'docs/source/en/tasks'

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

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

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		TASK_GUIDE_TO_MODELS[task_guide]
      lowerCamelCase								=		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__   ,   set() )
      lowerCamelCase								=		{
          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(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		_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-->"   ,   )

      lowerCamelCase								=		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_						:     str	     =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     a_						:     str	     =		parser.parse_args()

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