





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				Any:






		"""simple docstring"""

		A__        =   [
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

		A__      ,			A__        =   emb.weight.shape
		A__        =   nn.Linear(__a    ,     __a    ,     bias=__a    )
		A__        =   emb.weight.data
		return lin_layer







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		A__        =   state_dict["""decoder.embed_tokens.weight"""].shape[0]

		A__        =   {key.replace("""decoder"""    ,     """model"""    ): val for key, val in state_dict.items()}

		A__        =   XGLMConfig(
		    vocab_size=__a    ,     max_position_embeddings=args.max_target_positions    ,     num_layers=args.decoder_layers    ,     attention_heads=args.decoder_attention_heads    ,     ffn_dim=args.decoder_ffn_embed_dim    ,     d_model=args.decoder_embed_dim    ,     layerdrop=args.decoder_layerdrop    ,     dropout=args.dropout    ,     attention_dropout=args.attention_dropout    ,     activation_dropout=args.activation_dropout    ,     activation_function="""gelu"""    ,     scale_embedding=not args.no_scale_embedding    ,     tie_word_embeddings=args.share_decoder_input_output_embed    ,     )

		A__        =   XGLMForCausalLM(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      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.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)










def    __lowerCamelCase					(							__a   :int = 1_0_0_0_0_0_0    )		->				int:






		"""simple docstring"""

		A__        =   limit + 1
		A__        =   [0] * limit
		for first_term in range(1    ,     __a    ):
				for n in range(__a    ,     __a    ,     __a    ):
						A__        =   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

		A__        =   sum(1 for x in frequency[1:limit] if x == 1_0    )

		return count


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



