





'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Dict     )   ->    Dict:
			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(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Optional[Any]     )   ->    Optional[Any]:
			a__     = list(s_dict.keys()     )
			for key in keys:
						if "transformer_layers" in key:
									a__     = s_dict.pop(__lowerCamelCase     )
						elif "subsample" in key:
									a__     = s_dict.pop(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    Dict:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     List[str]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"]
			a__     = mam_aaa["model"]
			a__     = state_dict["decoder.output_projection.weight"]

			remove_ignore_keys_(__lowerCamelCase     )
			rename_keys(__lowerCamelCase     )

			a__     = state_dict["decoder.embed_tokens.weight"].shape[0]

			a__     = args.share_decoder_input_output_embed

			a__     = [int(__lowerCamelCase     ) for i in args.conv_kernel_sizes.split(","     )]
			a__     = 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					,  )

			a__     = SpeechaTextForConditionalGeneration(__lowerCamelCase     )
			a__   ,				a__     = 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__     = make_linear_from_emb(model.model.decoder.embed_tokens     )
			else:
						a__     = lm_head_weights

			model.save_pretrained(__lowerCamelCase     )


if __name__ == "__main__":
		lowerCAmelCase_   :   Tuple       =					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'''


def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[list[float]]:
			a__     = []
			for data in source_data:
						for i, el in enumerate(__lowerCamelCase     ):
									if len(__lowerCamelCase     ) < i + 1:
												data_lists.append([]     )
									data_lists[i].append(float(__lowerCamelCase     )     )
			return data_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = []
			for dlist, weight in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = min(__lowerCamelCase     )
						a__     = max(__lowerCamelCase     )

						a__     = []
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))     )
												except ZeroDivisionError:
															score.append(1     )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)     )
												except ZeroDivisionError:
															score.append(0     )

        # weight not 0 or 1
						else:
									a__     = f'''Invalid weight of {weight:f} provided'''
									raise ValueError(__lowerCamelCase     )

						score_lists.append(__lowerCamelCase     )

			return score_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[float]:
			a__     = [0 for i in range(len(score_lists[0]     )     )]

			for slist in score_lists:
						for j, ele in enumerate(__lowerCamelCase     ):
									a__     = final_scores[j] + ele

			return final_scores







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = get_data(__lowerCamelCase     )
			a__     = calculate_each_score(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = generate_final_scores(__lowerCamelCase     )

			# append scores to source data
			for i, ele in enumerate(__lowerCamelCase     ):
						source_data[i].append(__lowerCamelCase     )

			return source_data



