






from ...configuration_utils import PretrainedConfig
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   "bert-generation"
		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Tuple=5_0_3_5_8						,_SCREAMING_SNAKE_CASE		:       str=1_0_2_4						,_SCREAMING_SNAKE_CASE		:       List[str]=2_4						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=1_6						,_SCREAMING_SNAKE_CASE		:       Dict=4_0_9_6						,_SCREAMING_SNAKE_CASE		:       str="gelu"						,_SCREAMING_SNAKE_CASE		:       str=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Tuple=5_1_2						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.02						,_SCREAMING_SNAKE_CASE		:       List[str]=1E-12						,_SCREAMING_SNAKE_CASE		:       Tuple=0						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=2						,_SCREAMING_SNAKE_CASE		:       Optional[int]=1						,_SCREAMING_SNAKE_CASE		:       str="absolute"						,_SCREAMING_SNAKE_CASE		:       Optional[int]=True						,**_SCREAMING_SNAKE_CASE		:       Optional[int]						,)    ->   Dict:


						'''simple docstring'''
						super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,bos_token_id=_SCREAMING_SNAKE_CASE						,eos_token_id=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						A	 =      vocab_size
						A	 =      hidden_size
						A	 =      num_hidden_layers
						A	 =      num_attention_heads
						A	 =      hidden_act
						A	 =      intermediate_size
						A	 =      hidden_dropout_prob
						A	 =      attention_probs_dropout_prob
						A	 =      max_position_embeddings
						A	 =      initializer_range
						A	 =      layer_norm_eps
						A	 =      position_embedding_type
						A	 =      use_cache













import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				A	 =      [
				    '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      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				A	 =      list(s_dict.keys()   )
				for key in keys:
								if "transformer_layers" in key:
												A	 =      s_dict.pop(UpperCAmelCase   )
								elif "subsample" in key:
												A	 =      s_dict.pop(UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				A ,		A	 =      emb.weight.shape
				A	 =      nn.Linear(UpperCAmelCase,						UpperCAmelCase,						bias=UpperCAmelCase   )
				A	 =      emb.weight.data
				return lin_layer







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )
				A	 =      mam_aaa['args']
				A	 =      mam_aaa['model']
				A	 =      state_dict['decoder.output_projection.weight']

				remove_ignore_keys_(UpperCAmelCase   )
				rename_keys(UpperCAmelCase   )

				A	 =      state_dict['decoder.embed_tokens.weight'].shape[0]

				A	 =      args.share_decoder_input_output_embed

				A	 =      [int(UpperCAmelCase   ) for i in args.conv_kernel_sizes.split(','   )]
				A	 =      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=2_00,						use_cache=UpperCAmelCase,						decoder_start_token_id=2,						early_stopping=UpperCAmelCase,						)

				A	 =      SpeechaTextForConditionalGeneration(UpperCAmelCase   )
				A ,		A	 =      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:
								A	 =      make_linear_from_emb(model.model.decoder.embed_tokens   )
				else:
								A	 =      lm_head_weights

				model.save_pretrained(UpperCAmelCase   )


if __name__ == "__main__":
					lowerCAmelCase_           = 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_           = parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)





