



"""simple docstring"""







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


lowerCAmelCase_   						=   logging.get_logger(__name__)

lowerCAmelCase_   						=   {
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					int   	=				"speech_to_text_2"
		lowerCAmelCase			:					Optional[int]   	=				["past_key_values"]
		lowerCAmelCase			:					str   	=				{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}







		def __init__(	self						:  List[Any]			,_snake_case						:  Optional[int]=10_000			,_snake_case						:  List[Any]=6			,_snake_case						:  Any=2_048			,_snake_case						:  Optional[Any]=4			,_snake_case						:  Optional[int]=0.0			,_snake_case						:  Any=True			,_snake_case						:  Any="relu"			,_snake_case						:  Dict=256			,_snake_case						:  str=0.1			,_snake_case						:  int=0.0			,_snake_case						:  int=0.0			,_snake_case						:  Union[str, Any]=0.02			,_snake_case						:  List[Any]=2			,_snake_case						:  List[str]=True			,_snake_case						:  List[Any]=1			,_snake_case						:  Optional[Any]=0			,_snake_case						:  Optional[Any]=2			,_snake_case						:  Dict=1_024			,**_snake_case						:  Any			,)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Dict				=			vocab_size
				lowercase__   :    List[Any]				=			d_model
				lowercase__   :    int				=			decoder_ffn_dim
				lowercase__   :    int				=			decoder_layers
				lowercase__   :    str				=			decoder_attention_heads
				lowercase__   :    Tuple				=			dropout
				lowercase__   :    str				=			attention_dropout
				lowercase__   :    Union[str, Any]				=			activation_dropout
				lowercase__   :    int				=			activation_function
				lowercase__   :    int				=			init_std
				lowercase__   :    Tuple				=			decoder_layerdrop
				lowercase__   :    Dict				=			use_cache
				lowercase__   :    str				=			decoder_layers
				lowercase__   :    Any				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				lowercase__   :    Optional[Any]				=			max_target_positions

				super().__init__(
				    pad_token_id=_snake_case			,bos_token_id=_snake_case			,eos_token_id=_snake_case			,decoder_start_token_id=_snake_case			,**_snake_case			,)





"""simple docstring"""







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[int]:
		if "model" in orig_key:
				lowercase__   :    Tuple				=			orig_key.replace('''model.'''						,     ''''''	)
		if "norm1" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm1'''						,     '''attention.output.LayerNorm'''	)
		if "norm2" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm2'''						,     '''output.LayerNorm'''	)
		if "norm" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm'''						,     '''LayerNorm'''	)
		if "transformer" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.split('''.'''	)[0].split('''_'''	)[-1]
				lowercase__   :    List[str]				=			orig_key.replace(f"""transformer_{layer_num}"""						,     f"""encoder.layer.{layer_num}"""	)
		if "mha.attn" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.replace('''mha.attn'''						,     '''attention.self'''	)
		if "mha" in orig_key:
				lowercase__   :    str				=			orig_key.replace('''mha'''						,     '''attention'''	)
		if "W_q" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_q'''						,     '''self.query'''	)
		if "W_k" in orig_key:
				lowercase__   :    List[Any]				=			orig_key.replace('''W_k'''						,     '''self.key'''	)
		if "W_v" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_v'''						,     '''self.value'''	)
		if "ff1" in orig_key:
				lowercase__   :    Optional[int]				=			orig_key.replace('''ff1'''						,     '''intermediate.dense'''	)
		if "ff2" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''ff2'''						,     '''output.dense'''	)
		if "ff" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''ff'''						,     '''output.dense'''	)
		if "mlm_class" in orig_key:
				lowercase__   :    int				=			orig_key.replace('''mlm.mlm_class'''						,     '''cls.predictions.decoder'''	)
		if "mlm" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''mlm'''						,     '''cls.predictions.transform'''	)
		if "cls" not in orig_key:
				lowercase__   :    Optional[Any]				=			'''yoso.''' + orig_key

		return orig_key



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    Optional[Any]				=			orig_state_dict.pop(__lowerCamelCase	)

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

		lowercase__   :    Union[str, Any]				=			orig_state_dict['''cls.predictions.decoder.bias''']
		lowercase__   :    List[str]				=			torch.arange(__lowerCamelCase	).expand((1, -1)	) + 2

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Tuple				=			torch.load(__lowerCamelCase						,     map_location='''cpu'''	)['''model_state_dict''']
		lowercase__   :    List[Any]				=			YosoConfig.from_json_file(__lowerCamelCase	)
		lowercase__   :    List[Any]				=			YosoForMaskedLM(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			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__":
							lowerCAmelCase_   						=   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.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
