




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						int										=[
				    """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 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						List[Any]										=list(s_dict.keys() )
				for key in keys:
								if "transformer_layers" in key:
												__magic_name__						:						List[Any]										=s_dict.pop(lowerCamelCase )
								elif "subsample" in key:
												__magic_name__						:						Any										=s_dict.pop(lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__					,			__magic_name__						:						Union[str, Any]										=emb.weight.shape
				__magic_name__						:						int										=nn.Linear(lowerCamelCase						,  lowerCamelCase						,  bias=lowerCamelCase )
				__magic_name__						:						int										=emb.weight.data
				return lin_layer




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						int										=torch.load(lowerCamelCase						,  map_location="""cpu""" )
				__magic_name__						:						Optional[Any]										=mam_aaa["""args"""]
				__magic_name__						:						List[str]										=mam_aaa["""model"""]
				__magic_name__						:						str										=state_dict["""decoder.output_projection.weight"""]

				remove_ignore_keys_(lowerCamelCase )
				rename_keys(lowerCamelCase )

				__magic_name__						:						Tuple										=state_dict["""decoder.embed_tokens.weight"""].shape[0]

				__magic_name__						:						int										=args.share_decoder_input_output_embed

				__magic_name__						:						List[Any]										=[int(lowerCamelCase ) for i in args.conv_kernel_sizes.split(""",""" )]
				__magic_name__						:						Union[str, 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=200						,  use_cache=lowerCamelCase						,  decoder_start_token_id=2						,  early_stopping=lowerCamelCase						,  )

				__magic_name__						:						List[Any]										=SpeechaTextForConditionalGeneration(lowerCamelCase )
				__magic_name__					,			__magic_name__						:						Dict										=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:
								__magic_name__						:						Optional[int]										=make_linear_from_emb(model.model.decoder.embed_tokens )
				else:
								__magic_name__						:						Tuple										=lm_head_weights

				model.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
							UpperCAmelCase_		:					Optional[int]          =  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.")
							UpperCAmelCase_		:					List[Any]          =  parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										debug_launcher(test_script.main			)


						def      A__    (		self :Dict			):
										'''simple docstring'''

										debug_launcher(test_ops.main			)




