


'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration





def      UpperCAmelCase      (		lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						[
		    """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      UpperCAmelCase      (		lowerCamelCase_  :Dict ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						list(s_dict.keys() )
		for key in keys:
				if "transformer_layers" in key:
						snake_case_		:							Tuple			       =						s_dict.pop(__UpperCamelCase )
				elif "subsample" in key:
						snake_case_		:							Dict			       =						s_dict.pop(__UpperCamelCase )





def      UpperCAmelCase      (		lowerCamelCase_  :Any ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						emb.weight.shape
		snake_case_		:							List[Any]			       =						nn.Linear(__UpperCamelCase				,      __UpperCamelCase				,      bias=__UpperCamelCase )
		snake_case_		:							str			       =						emb.weight.data
		return lin_layer





def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Any ):







		'''simple docstring'''
		snake_case_		:							int			       =						torch.load(__UpperCamelCase				,      map_location="""cpu""" )
		snake_case_		:							Optional[int]			       =						mam_aaa["""args"""]
		snake_case_		:							int			       =						mam_aaa["""model"""]
		snake_case_		:							List[str]			       =						state_dict["""decoder.output_projection.weight"""]

		remove_ignore_keys_(__UpperCamelCase )
		rename_keys(__UpperCamelCase )

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

		snake_case_		:							List[str]			       =						args.share_decoder_input_output_embed

		snake_case_		:							int			       =						[int(__UpperCamelCase ) for i in args.conv_kernel_sizes.split(""",""" )]
		snake_case_		:							Optional[Any]			       =						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				,      )

		snake_case_		:							int			       =						SpeechaTextForConditionalGeneration(__UpperCamelCase )
		snake_case_		:							Dict			       =						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:
				snake_case_		:							Optional[int]			       =						make_linear_from_emb(model.model.decoder.embed_tokens )
		else:
				snake_case_		:							Dict			       =						lm_head_weights

		model.save_pretrained(__UpperCamelCase )


if __name__ == "__main__":
				__A	:	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.')
				__A	:	List[str]     =     parser.parse_args()
				convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)



'''simple docstring'''






from __future__ import annotations





def      UpperCAmelCase      (		lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							List[str]			       =						[True] * limit
		snake_case_		:							Union[str, Any]			       =						False
		snake_case_		:							List[str]			       =						False
		snake_case_		:							Any			       =						True

		for i in range(3				,      int(limit**0.5 + 1 )				,      2 ):
				snake_case_		:							Tuple			       =						i * 2
				while index < limit:
						snake_case_		:							Any			       =						False
						snake_case_		:							Optional[Any]			       =						index + i

		snake_case_		:							List[str]			       =						[2]

		for i in range(3				,      lowerCamelCase_				,      2 ):
				if is_prime[i]:
						primes.append(lowerCamelCase_ )

		return primes





def      UpperCAmelCase      (		lowerCamelCase_  :int = 1_00_00_00 ):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						prime_sieve(lowerCamelCase_ )
		snake_case_		:							Optional[Any]			       =						0
		snake_case_		:							Optional[int]			       =						0

		for i in range(len(lowerCamelCase_ ) ):
				for j in range(i + length				,      len(lowerCamelCase_ ) ):
						snake_case_		:							Optional[int]			       =						sum(primes[i:j] )
						if sol >= ceiling:
								break

						if sol in primes:
								snake_case_		:							Optional[Any]			       =						j - i
								snake_case_		:							Tuple			       =						sol

		return largest


if __name__ == "__main__":
							print(F'{solution() = }')