
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 							_lowercase							(     UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= [
					    '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 							_lowercase							(     UpperCamelCase_		)		->		str:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= emb.weight.shape
					SCREAMING_SNAKE_CASE__													= nn.Linear(UpperCamelCase_	,	UpperCamelCase_	,	bias=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= emb.weight.data
					return lin_layer






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


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

					remove_ignore_keys_(UpperCamelCase_		)
					rename_keys(UpperCamelCase_		)

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

					SCREAMING_SNAKE_CASE__													= args.share_decoder_input_output_embed

					SCREAMING_SNAKE_CASE__													= [int(UpperCamelCase_		) for i in args.conv_kernel_sizes.split(','		)]
					SCREAMING_SNAKE_CASE__													= 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=200	,	use_cache=UpperCamelCase_	,	decoder_start_token_id=2	,	early_stopping=UpperCamelCase_	,	)

					SCREAMING_SNAKE_CASE__													= SpeechaTextForConditionalGeneration(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= 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:
										SCREAMING_SNAKE_CASE__													= make_linear_from_emb(model.model.decoder.embed_tokens		)
					else:
										SCREAMING_SNAKE_CASE__													= lm_head_weights

					model.save_pretrained(UpperCamelCase_		)


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







import random






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= [], [], []
					for element in data:
										if element < pivot:
															less.append(UpperCamelCase_		)
										elif element > pivot:
															greater.append(UpperCamelCase_		)
										else:
															equal.append(UpperCamelCase_		)
					return less, equal, greater






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					if index >= len(UpperCamelCase_		) or index < 0:
										return None

					SCREAMING_SNAKE_CASE__													= items[random.randint(0	,	len(UpperCamelCase_		) - 1		)]
					SCREAMING_SNAKE_CASE__													= 0
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= _partition(UpperCamelCase_	,	UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= len(UpperCamelCase_		)

					# index is the pivot
					if m <= index < m + count:
										return pivot
					# must be in smaller
					elif m > index:
										return quick_select(UpperCamelCase_	,	UpperCamelCase_		)
					# must be in larger
					else:
										return quick_select(UpperCamelCase_	,	index - (m + count)		)





