
'''simple docstring'''
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple )				->       str:





  """simple docstring"""

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







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any] )				->       Union[str, Any]:





  """simple docstring"""

  __A		:     Dict												=		emb.weight.shape
  __A		:     Optional[Any]												=		nn.Linear(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     bias=__SCREAMING_SNAKE_CASE )
  __A		:     Optional[int]												=		emb.weight.data
  return lin_layer







def        A_     (						__SCREAMING_SNAKE_CASE						:  str )				->       int:





  """simple docstring"""

  __A		:     Tuple												=		torch.load(__SCREAMING_SNAKE_CASE			,     map_location="""cpu""" )
  __A		:     Optional[Any]												=		Namespace(**checkpoint["""cfg"""]["""model"""] )
  __A		:     Any												=		checkpoint["""model"""]
  remove_ignore_keys_(__SCREAMING_SNAKE_CASE )
  __A		:     Union[str, Any]												=		state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

  __A		:     Optional[int]												=		XGLMConfig(
      vocab_size=__SCREAMING_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			,     )

  __A		:     Optional[Any]												=		XGLMForCausalLM(__SCREAMING_SNAKE_CASE )
  __A		:     List[str]												=		model.load_state_dict(__SCREAMING_SNAKE_CASE			,     strict=__SCREAMING_SNAKE_CASE )
  print(__SCREAMING_SNAKE_CASE )
  __A		:     Dict												=		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__   :		Optional[int] 		=parser.parse_args()
    A__   :		Union[str, Any] 		=convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       None:





  """simple docstring"""

  __A		:     Tuple												=		generate_pascal_triangle(__SCREAMING_SNAKE_CASE )
  for row_idx in range(__SCREAMING_SNAKE_CASE ):
    # Print left spaces
    for _ in range(num_rows - row_idx - 1 ):
      print(end=""" """ )
    # Print row values
    for col_idx in range(row_idx + 1 ):
      if col_idx != row_idx:
        print(triangle[row_idx][col_idx]			,     end=""" """ )
      else:
        print(triangle[row_idx][col_idx]			,     end="""""" )
    print()







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    raise TypeError("""The input value of 'num_rows' should be 'int'""" )

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        """The input value of 'num_rows' should be greater than or equal to 0""" )

  __A		:     list[list[int]]												=		[]
  for current_row_idx in range(__SCREAMING_SNAKE_CASE ):
    __A		:     Tuple												=		populate_current_row(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    triangle.append(__SCREAMING_SNAKE_CASE )
  return triangle







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  int )				->       list[int]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		[-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  __A							,					__A		:     Tuple												=		1, 1
  for current_col_idx in range(1			,     __SCREAMING_SNAKE_CASE ):
    calculate_current_element(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  return current_row







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  list[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     )				->       None:





  """simple docstring"""

  __A		:     str												=		triangle[current_row_idx - 1][current_col_idx - 1]
  __A		:     Union[str, Any]												=		triangle[current_row_idx - 1][current_col_idx]
  __A		:     Any												=		above_to_left_elt + above_to_right_elt







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    raise TypeError("""The input value of 'num_rows' should be 'int'""" )

  if num_rows == 0:
    return []
  elif num_rows < 0:
    raise ValueError(
        """The input value of 'num_rows' should be greater than or equal to 0""" )

  __A		:     list[list[int]]												=		[[1]]

  for row_index in range(1			,     __SCREAMING_SNAKE_CASE ):
    __A		:     Optional[Any]												=		[0] + result[-1] + [0]
    __A		:     Optional[Any]												=		row_index + 1
    # Calculate the number of distinct elements in a row
    __A		:     int												=		sum(divmod(__SCREAMING_SNAKE_CASE			,     2 ) )
    __A		:     str												=		[
        temp_row[i - 1] + temp_row[i] for i in range(1			,     distinct_elements + 1 )
    ]
    __A		:     Tuple												=		row_first_half[: (row_index + 1) // 2]
    row_second_half.reverse()
    __A		:     Any												=		row_first_half + row_second_half
    result.append(__SCREAMING_SNAKE_CASE )

  return result







def        A_     (						)				->       None:





  """simple docstring"""

  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(__SCREAMING_SNAKE_CASE						:  Callable			,     __SCREAMING_SNAKE_CASE						:  int ) -> None:
    __A		:     List[Any]												=		F"{func.__name__}({value})"
    __A		:     Dict												=		timeit(F"__main__.{call}"			,     setup="""import __main__""" )
    # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
    print(F"{call:38} -- {timing:.4f} seconds" )

  for value in range(15 ):  # (1, 7, 14):
    for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
      benchmark_a_function(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



