

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							[
      '''decoder.version''',
      '''decoder.output_projection.weight''',
      '''_float_tensor''',
      '''decoder.embed_positions._float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(_UpperCamelCase			,	_UpperCamelCase					)



def 				snake_case_(_UpperCamelCase					)							->     Tuple:



  """simple docstring"""






  _snake_case,					_snake_case						=							emb.weight.shape
  _snake_case						=							nn.Linear(_UpperCamelCase			,	_UpperCamelCase			,	bias=_UpperCamelCase					)
  _snake_case						=							emb.weight.data
  return lin_layer



def 				snake_case_(_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							torch.load(_UpperCamelCase			,	map_location='''cpu'''					)
  _snake_case						=							Namespace(**checkpoint['''cfg''']['''model''']					)
  _snake_case						=							checkpoint['''model''']
  remove_ignore_keys_(_UpperCamelCase					)
  _snake_case						=							state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

  _snake_case						=							XGLMConfig(
      vocab_size=_UpperCamelCase			,	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			,	)

  _snake_case						=							XGLMForCausalLM(_UpperCamelCase					)
  _snake_case						=							model.load_state_dict(_UpperCamelCase			,	strict=_UpperCamelCase					)
  print(_UpperCamelCase					)
  _snake_case						=							make_linear_from_emb(model.model.embed_tokens					)

  return model


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




import cmath
import math



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     complex:



  """simple docstring"""






  _snake_case						=							math.radians(_UpperCamelCase					)
  _snake_case						=							math.radians(_UpperCamelCase					)

  # Convert voltage and current to rectangular form
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)

  # Calculate apparent power
  return voltage_rect * current_rect


if __name__ == "__main__":
 import doctest

 doctest.testmod()

