



'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[Any]	=     [
			    """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(lowerCamelCase__						,      lowerCamelCase__    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     list(s_dict.keys()    )
			for key in keys:
						if "transformer_layers" in key:
									A_	:						str	=     s_dict.pop(lowerCamelCase__    )
						elif "subsample" in key:
									A_	:						Any	=     s_dict.pop(lowerCamelCase__    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_,    A_	:						Optional[int]	=     emb.weight.shape
			A_	:						int	=     nn.Linear(lowerCamelCase__						,      lowerCamelCase__						,      bias=lowerCamelCase__    )
			A_	:						int	=     emb.weight.data
			return lin_layer



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[Any]	=     torch.load(lowerCamelCase__						,      map_location="""cpu"""    )
			A_	:						List[Any]	=     mam_aaa["""args"""]
			A_	:						Dict	=     mam_aaa["""model"""]
			A_	:						Dict	=     state_dict["""decoder.output_projection.weight"""]

			remove_ignore_keys_(lowerCamelCase__    )
			rename_keys(lowerCamelCase__    )

			A_	:						Union[str, Any]	=     state_dict["""decoder.embed_tokens.weight"""].shape[0]

			A_	:						int	=     args.share_decoder_input_output_embed

			A_	:						List[Any]	=     [int(lowerCamelCase__    ) for i in args.conv_kernel_sizes.split(""","""    )]
			A_	:						Any	=     SpeechaTextConfig(
			    vocab_size=lowerCamelCase__						,      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(lowerCamelCase__    )						,      conv_channels=args.conv_channels						,      conv_kernel_sizes=lowerCamelCase__						,      input_feat_per_channel=args.input_feat_per_channel						,      input_channels=args.input_channels						,      tie_word_embeddings=lowerCamelCase__						,      num_beams=5						,      max_length=2_00						,      use_cache=lowerCamelCase__						,      decoder_start_token_id=2						,      early_stopping=lowerCamelCase__						,      )

			A_	:						Any	=     SpeechaTextForConditionalGeneration(lowerCamelCase__    )
			A_,    A_	:						Any	=     model.model.load_state_dict(lowerCamelCase__						,      strict=lowerCamelCase__    )
			if len(lowerCamelCase__    ) > 0 and not set(lowerCamelCase__    ) <= {
			    "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:
						A_	:						int	=     make_linear_from_emb(model.model.decoder.embed_tokens    )
			else:
						A_	:						Tuple	=     lm_head_weights

			model.save_pretrained(lowerCamelCase__    )


if __name__ == "__main__":
						lowerCamelCase							:Union[str, 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.''')
						lowerCamelCase							:Optional[Any]				    =					parser.parse_args()
						convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)




'''simple docstring'''




from string import ascii_lowercase, ascii_uppercase



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if not sentence:
						return ""
			A_	:						Optional[int]	=     dict(zip(lowerCamelCase__						,      lowerCamelCase__    )    )
			return lower_to_upper.get(sentence[0]						,      sentence[0]    ) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()