



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def 							A     (					_UpperCAmelCase    :       str   )    ->			str:







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

			return orig_key





def 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Tuple   )    ->			Dict:







			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						_UpperCAmelCase			=       orig_state_dict.pop(__lowerCamelCase   )

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

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

			return orig_state_dict





def 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       Union[str, Any]							,       _UpperCAmelCase    :       List[str]   )    ->			Tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       torch.load(__lowerCamelCase							,       map_location='cpu'   )["model_state_dict"]
			_UpperCAmelCase			=       YosoConfig.from_json_file(__lowerCamelCase   )
			_UpperCAmelCase			=       YosoForMaskedLM(__lowerCamelCase   )

			_UpperCAmelCase			=       convert_checkpoint_helper(config.max_position_embeddings							,       __lowerCamelCase   )

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

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


if __name__ == "__main__":
							UpperCAmelCase__       =	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."
							)
							UpperCAmelCase__       =	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
UpperCAmelCase__       =	"src/transformers"
UpperCAmelCase__       =	"docs/source/en/tasks"





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Any   )    ->			int:







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

			_UpperCAmelCase			=       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.
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 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       TASK_GUIDE_TO_MODELS[task_guide]
			_UpperCAmelCase			=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCAmelCase							,       set()   )
			_UpperCAmelCase			=       {
			    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 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Dict=False   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       _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			=       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__":
							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)
