






'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       str:
							lowerCamelCase__					:	Dict         =       [
							    """encoder.version""",
							    """decoder.version""",
							    """model.encoder.version""",
							    """model.decoder.version""",
							    """decoder.output_projection.weight""",
							    """_float_tensor""",
							    """encoder.embed_positions._float_tensor""",
							    """decoder.embed_positions._float_tensor""",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase				,			UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Optional[Any]:
							lowerCamelCase__					:	Tuple         =       list(s_dict.keys()      )
							for key in keys:
														if "transformer_layers" in key:
																					lowerCamelCase__					:	Tuple         =       s_dict.pop(UpperCamelCase      )
														elif "subsample" in key:
																					lowerCamelCase__					:	List[Any]         =       s_dict.pop(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Tuple:
							lowerCamelCase__  ,		lowerCamelCase__					:	Tuple         =       emb.weight.shape
							lowerCamelCase__					:	int         =       nn.Linear(UpperCamelCase				,			UpperCamelCase				,			bias=UpperCamelCase      )
							lowerCamelCase__					:	Optional[Any]         =       emb.weight.data
							return lin_layer





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[Any]:
							lowerCamelCase__					:	int         =       torch.load(UpperCamelCase				,			map_location="""cpu"""      )
							lowerCamelCase__					:	List[Any]         =       mam_aaa["""args"""]
							lowerCamelCase__					:	Union[str, Any]         =       mam_aaa["""model"""]
							lowerCamelCase__					:	List[str]         =       state_dict["""decoder.output_projection.weight"""]

							remove_ignore_keys_(UpperCamelCase      )
							rename_keys(UpperCamelCase      )

							lowerCamelCase__					:	List[Any]         =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

							lowerCamelCase__					:	int         =       args.share_decoder_input_output_embed

							lowerCamelCase__					:	Optional[int]         =       [int(UpperCamelCase      ) for i in args.conv_kernel_sizes.split(""","""      )]
							lowerCamelCase__					:	List[Any]         =       SpeechaTextConfig(
							    vocab_size=UpperCamelCase				,			max_source_positions=args.max_source_positions				,			max_target_positions=args.max_target_positions				,			encoder_layers=args.encoder_layers				,			decoder_layers=args.decoder_layers				,			encoder_attention_heads=args.encoder_attention_heads				,			decoder_attention_heads=args.decoder_attention_heads				,			encoder_ffn_dim=args.encoder_ffn_embed_dim				,			decoder_ffn_dim=args.decoder_ffn_embed_dim				,			d_model=args.encoder_embed_dim				,			dropout=args.dropout				,			attention_dropout=args.attention_dropout				,			activation_dropout=args.activation_dropout				,			activation_function="""relu"""				,			num_conv_layers=len(UpperCamelCase      )				,			conv_channels=args.conv_channels				,			conv_kernel_sizes=UpperCamelCase				,			input_feat_per_channel=args.input_feat_per_channel				,			input_channels=args.input_channels				,			tie_word_embeddings=UpperCamelCase				,			num_beams=5				,			max_length=200				,			use_cache=UpperCamelCase				,			decoder_start_token_id=2				,			early_stopping=UpperCamelCase				,			)

							lowerCamelCase__					:	List[Any]         =       SpeechaTextForConditionalGeneration(UpperCamelCase      )
							lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       model.model.load_state_dict(UpperCamelCase				,			strict=UpperCamelCase      )
							if len(UpperCamelCase      ) > 0 and not set(UpperCamelCase      ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
														    f''' but all the following weights are missing {missing}'''      )

							if tie_embeds:
														lowerCamelCase__					:	Union[str, Any]         =       make_linear_from_emb(model.model.decoder.embed_tokens      )
							else:
														lowerCamelCase__					:	Optional[Any]         =       lm_head_weights

							model.save_pretrained(UpperCamelCase      )


if __name__ == "__main__":
			_A	:			Optional[Any]         =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
			parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			_A	:			str         =parser.parse_args()
			convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





'''simple docstring'''

from __future__ import annotations

def _SCREAMING_SNAKE_CASE      (A       )       ->       bool:






       """simple docstring"""







       return len(set(A       )       ) == len(A       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




