


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def      __lowerCamelCase		(				lowerCamelCase__						:		str	):





			'''simple docstring'''




			lowerCamelCase  						=   [
			    '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      __lowerCamelCase		(				lowerCamelCase__						:		Tuple	):





			'''simple docstring'''




			lowerCamelCase  						=   emb.weight.shape
			lowerCamelCase  						=   nn.Linear(_UpperCAmelCase   ,				_UpperCAmelCase   ,				bias=_UpperCAmelCase	)
			lowerCamelCase  						=   emb.weight.data
			return lin_layer






def      __lowerCamelCase		(				lowerCamelCase__						:		Any	):





			'''simple docstring'''




			lowerCamelCase  						=   torch.load(_UpperCAmelCase   ,				map_location="""cpu"""	)
			lowerCamelCase  						=   mam_aaa['args'] or mam_aaa['cfg']['model']
			lowerCamelCase  						=   mam_aaa['model']
			remove_ignore_keys_(_UpperCAmelCase	)
			lowerCamelCase  						=   state_dict['encoder.embed_tokens.weight'].shape[0]

			lowerCamelCase  						=   MaMaaaConfig(
			    vocab_size=_UpperCAmelCase   ,				max_position_embeddings=1024   ,				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   ,				encoder_layerdrop=args.encoder_layerdrop   ,				decoder_layerdrop=args.decoder_layerdrop   ,				dropout=args.dropout   ,				attention_dropout=args.attention_dropout   ,				activation_dropout=args.activation_dropout   ,				activation_function="""relu"""   ,				)

			lowerCamelCase  						=   state_dict['decoder.embed_tokens.weight']
			lowerCamelCase  						=   MaMaaaForConditionalGeneration(_UpperCAmelCase	)
			model.model.load_state_dict(_UpperCAmelCase   ,				strict=_UpperCAmelCase	)
			lowerCamelCase  						=   make_linear_from_emb(model.model.shared	)

			return model


if __name__ == "__main__":
		UpperCAmelCase					:  Tuple			=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		UpperCAmelCase					:  str			=						parser.parse_args()
		UpperCAmelCase					:  Optional[Any]			=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)








def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 100_0000 )			->     int:
    lowerCamelCase__    :      int					 =		limit + 1
    lowerCamelCase__    :      Optional[Any]					 =		[0] * limit
    for first_term in range(1						,	_UpperCAmelCase ):
        for n in range(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[Any]					 =		first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    lowerCamelCase__    :      List[str]					 =		sum(1 for x in frequency[1:limit] if x == 10 )

    return count


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





