






'''simple docstring'''

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def 					lowercase__				(   __UpperCamelCase					)->				Any:
      UpperCamelCase       						=      [
          '''decoder.version''',
          '''decoder.output_projection.weight''',
          '''_float_tensor''',
          '''decoder.embed_positions._float_tensor''',
      ]
      for k in ignore_keys:
            state_dict.pop(a_	,		a_					)







def 					lowercase__				(   __UpperCamelCase					)->				str:
      UpperCamelCase       						=      emb.weight.shape
      UpperCamelCase       						=      nn.Linear(a_	,		a_	,		bias=a_					)
      UpperCamelCase       						=      emb.weight.data
      return lin_layer







def 					lowercase__				(   __UpperCamelCase					)->				int:
      UpperCamelCase       						=      torch.load(a_	,		map_location="""cpu"""					)
      UpperCamelCase       						=      Namespace(**checkpoint["""cfg"""]["""model"""]					)
      UpperCamelCase       						=      checkpoint['''model''']
      remove_ignore_keys_(a_					)
      UpperCamelCase       						=      state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

      UpperCamelCase       						=      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	,		)

      UpperCamelCase       						=      XGLMForCausalLM(a_					)
      UpperCamelCase       						=      model.load_state_dict(a_	,		strict=a_					)
      print(a_					)
      UpperCamelCase       						=      make_linear_from_emb(model.model.embed_tokens					)

      return model


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








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				str:
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					):
            raise ValueError("""iterations must be defined as integers"""					)
      if not isinstance(__UpperCamelCase	,		__UpperCamelCase					) or not number >= 1:
            raise ValueError(
                """starting number must be
                         and integer and be more than 0"""					)
      if not iterations >= 1:
            raise ValueError("""Iterations must be done more than 0 times to play FizzBuzz"""					)

      UpperCamelCase       						=      """"""
      while number <= iterations:
            if number % 3 == 0:
                  out += "Fizz"
            if number % 5 == 0:
                  out += "Buzz"
            if 0 not in (number % 3, number % 5):
                  out += str(__UpperCamelCase					)

            # print(out)
            number += 1
            out += " "
      return out


if __name__ == "__main__":
     import doctest

     doctest.testmod()
