



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      if "model" in orig_key:
            lowerCamelCase								=		orig_key.replace("model."   ,   "" )
      if "norm1" in orig_key:
            lowerCamelCase								=		orig_key.replace("norm1"   ,   "attention.output.LayerNorm" )
      if "norm2" in orig_key:
            lowerCamelCase								=		orig_key.replace("norm2"   ,   "output.LayerNorm" )
      if "norm" in orig_key:
            lowerCamelCase								=		orig_key.replace("norm"   ,   "LayerNorm" )
      if "transformer" in orig_key:
            lowerCamelCase								=		orig_key.split("." )[0].split("_" )[-1]
            lowerCamelCase								=		orig_key.replace(F"""transformer_{layer_num}"""   ,   F"""encoder.layer.{layer_num}""" )
      if "mha.attn" in orig_key:
            lowerCamelCase								=		orig_key.replace("mha.attn"   ,   "attention.self" )
      if "mha" in orig_key:
            lowerCamelCase								=		orig_key.replace("mha"   ,   "attention" )
      if "W_q" in orig_key:
            lowerCamelCase								=		orig_key.replace("W_q"   ,   "self.query" )
      if "W_k" in orig_key:
            lowerCamelCase								=		orig_key.replace("W_k"   ,   "self.key" )
      if "W_v" in orig_key:
            lowerCamelCase								=		orig_key.replace("W_v"   ,   "self.value" )
      if "ff1" in orig_key:
            lowerCamelCase								=		orig_key.replace("ff1"   ,   "intermediate.dense" )
      if "ff2" in orig_key:
            lowerCamelCase								=		orig_key.replace("ff2"   ,   "output.dense" )
      if "ff" in orig_key:
            lowerCamelCase								=		orig_key.replace("ff"   ,   "output.dense" )
      if "mlm_class" in orig_key:
            lowerCamelCase								=		orig_key.replace("mlm.mlm_class"   ,   "cls.predictions.decoder" )
      if "mlm" in orig_key:
            lowerCamelCase								=		orig_key.replace("mlm"   ,   "cls.predictions.transform" )
      if "cls" not in orig_key:
            lowerCamelCase								=		"yoso." + orig_key

      return orig_key

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for key in orig_state_dict.copy().keys():
            lowerCamelCase								=		orig_state_dict.pop(UpperCAmelCase__ )

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  lowerCamelCase								=		val

      lowerCamelCase								=		orig_state_dict["cls.predictions.decoder.bias"]
      lowerCamelCase								=		torch.arange(UpperCAmelCase__ ).expand((1, -1) ) + 2

      return orig_state_dict

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		torch.load(UpperCAmelCase__   ,   map_location="cpu" )["model_state_dict"]
      lowerCamelCase								=		YosoConfig.from_json_file(UpperCAmelCase__ )
      lowerCamelCase								=		YosoForMaskedLM(UpperCAmelCase__ )

      lowerCamelCase								=		convert_checkpoint_helper(config.max_position_embeddings   ,   UpperCAmelCase__ )

      print(model.load_state_dict(UpperCAmelCase__ ) )
      model.eval()
      model.save_pretrained(UpperCAmelCase__ )

      print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
     a_						:     List[str]	     =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for YOSO model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_						:     Union[str, Any]	     =		parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




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)