





"""simple docstring"""





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def  __A   (_SCREAMING_SNAKE_CASE			)					->List[Any]:





  """simple docstring"""


  lowerCAmelCase__			:int											=							[
      '''decoder.version''',
      '''decoder.output_projection.weight''',
      '''_float_tensor''',
      '''decoder.embed_positions._float_tensor''',
  ]
  for k in ignore_keys:
    state_dict.pop(A_     ,   A_			)
def  __A   (_SCREAMING_SNAKE_CASE			)					->str:





  """simple docstring"""


  lowerCAmelCase__			:List[Any]											=							emb.weight.shape
  lowerCAmelCase__			:Dict											=							nn.Linear(A_     ,   A_     ,   bias=A_			)
  lowerCAmelCase__			:int											=							emb.weight.data
  return lin_layer
def  __A   (_SCREAMING_SNAKE_CASE			)					->str:





  """simple docstring"""


  lowerCAmelCase__			:Optional[int]											=							torch.load(A_     ,   map_location='cpu'			)
  lowerCAmelCase__			:List[str]											=							Namespace(**checkpoint['cfg']['model']			)
  lowerCAmelCase__			:Any											=							checkpoint['''model''']
  remove_ignore_keys_(A_			)
  lowerCAmelCase__			:Tuple											=							state_dict['''decoder.embed_tokens.weight'''].shape[0]

  lowerCAmelCase__			:Optional[int]											=							{key.replace('decoder'     ,   'model'			): val for key, val in state_dict.items()}

  lowerCAmelCase__			:str											=							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     ,   )

  lowerCAmelCase__			:List[Any]											=							XGLMForCausalLM(A_			)
  lowerCAmelCase__			:Optional[Any]											=							model.load_state_dict(A_     ,   strict=A_			)
  print(A_			)
  lowerCAmelCase__			:Dict											=							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)



"""simple docstring"""





import random

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	[], [], []
	for element in data:
		if element < pivot:
			less.append(A_ )
		elif element > pivot:
			greater.append(A_ )
		else:
			equal.append(A_ )
	return less, equal, greater





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	# index = len(items) // 2 when trying to find the median
	#   (value of index when items is sorted)

	# invalid input
	if index >= len(A_ ) or index < 0:
		return None

	lowerCAmelCase__	:						str  				=	items[random.randint(0						,     len(A_ ) - 1 )]
	lowerCAmelCase__	:						Optional[Any]  				=	0
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	_partition(A_						,     A_ )
	lowerCAmelCase__	:						str  				=	len(A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	len(A_ )

	# index is the pivot
	if m <= index < m + count:
		return pivot
	# must be in smaller
	elif m > index:
		return quick_select(A_						,     A_ )
	# must be in larger
	else:
		return quick_select(A_						,     index - (m + count) )




