
def       A       (					lowercase )   ->  list:
		'''simple docstring'''

		UpperCamelCase		=     len(lowercase )
		for _ in range(lowercase ):
				for i in range(_ % 2    ,       arr_size - 1    ,       2 ):
						if arr[i + 1] < arr[i]:
								UpperCamelCase	,  UpperCamelCase		=     arr[i + 1], arr[i]

		return arr


if __name__ == "__main__":
					_UpperCAmelCase					: Tuple      =       list(range(10, 0, -1))
					print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def       A       (					lowercase )   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     [
		    '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(lowercase    ,       lowercase )


def       A       (					lowercase )   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     list(s_dict.keys() )
		for key in keys:
				if "transformer_layers" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )
				elif "subsample" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )


def       A       (					lowercase )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase	,  UpperCamelCase		=     emb.weight.shape
		UpperCamelCase		=     nn.Linear(lowercase    ,       lowercase    ,       bias=lowercase )
		UpperCamelCase		=     emb.weight.data
		return lin_layer


def       A       (					lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     torch.load(lowercase    ,       map_location='cpu' )
		UpperCamelCase		=     mam_aaa['args']
		UpperCamelCase		=     mam_aaa['model']
		UpperCamelCase		=     state_dict['decoder.output_projection.weight']

		remove_ignore_keys_(lowercase )
		rename_keys(lowercase )

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

		UpperCamelCase		=     args.share_decoder_input_output_embed

		UpperCamelCase		=     [int(lowercase ) for i in args.conv_kernel_sizes.split(',' )]
		UpperCamelCase		=     SpeechaTextConfig(
		    vocab_size=lowercase    ,       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(lowercase )    ,       conv_channels=args.conv_channels    ,       conv_kernel_sizes=lowercase    ,       input_feat_per_channel=args.input_feat_per_channel    ,       input_channels=args.input_channels    ,       tie_word_embeddings=lowercase    ,       num_beams=5    ,       max_length=200    ,       use_cache=lowercase    ,       decoder_start_token_id=2    ,       early_stopping=lowercase    ,       )

		UpperCamelCase		=     SpeechaTextForConditionalGeneration(lowercase )
		UpperCamelCase	,  UpperCamelCase		=     model.model.load_state_dict(lowercase    ,       strict=lowercase )
		if len(lowercase ) > 0 and not set(lowercase ) <= {
		    "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:
				UpperCamelCase		=     make_linear_from_emb(model.model.decoder.embed_tokens )
		else:
				UpperCamelCase		=     lm_head_weights

		model.save_pretrained(lowercase )


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