



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

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


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase	,	_lowerCAmelCase						=    emb.weight.shape
					_lowerCAmelCase						=    nn.Linear(snake_case	,						snake_case	,						bias=snake_case			)
					_lowerCAmelCase						=    emb.weight.data
					return lin_layer


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

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

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

					_lowerCAmelCase						=    XGLMConfig(
					    vocab_size=snake_case	,						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						=    XGLMForCausalLM(snake_case			)
					_lowerCAmelCase						=    model.load_state_dict(snake_case	,						strict=snake_case			)
					print(snake_case			)
					_lowerCAmelCase						=    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 random

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> tuple:
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					[], [], []
       for element in data:
              if element < pivot:
                     less.append(_SCREAMING_SNAKE_CASE    )
              elif element > pivot:
                     greater.append(_SCREAMING_SNAKE_CASE    )
              else:
                     equal.append(_SCREAMING_SNAKE_CASE    )
       return less, equal, greater


def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

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

       lowerCamelCase   :      List[Any]			 =					items[random.randint(0 ,len(_SCREAMING_SNAKE_CASE    ) - 1    )]
       lowerCamelCase   :      Dict			 =					0
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					_partition(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					len(_SCREAMING_SNAKE_CASE    )

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






