
'''simple docstring'''
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
    A__   :		Any 		=argparse.ArgumentParser(
        description=(
            'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
            ' Distillation'
        )
    )
    parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
    parser.add_argument('--model_name', default='roberta-large', type=str)
    parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
    parser.add_argument('--vocab_transform', action='store_true')
    A__   :		List[Any] 		=parser.parse_args()

    if args.model_type == "roberta":
        A__   :		str 		=RobertaForMaskedLM.from_pretrained(args.model_name)
        A__   :		List[Any] 		='roberta'
    elif args.model_type == "gpt2":
        A__   :		Union[str, Any] 		=GPTaLMHeadModel.from_pretrained(args.model_name)
        A__   :		int 		='transformer'

    A__   :		Dict 		=model.state_dict()
    A__   :		Any 		={}

    # Embeddings #
    if args.model_type == "gpt2":
        for param_name in ["wte.weight", "wpe.weight"]:
            A__   :		Union[str, Any] 		=state_dict[F"{prefix}.{param_name}"]
    else:
        for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
            A__   :		Tuple 		=F"{prefix}.embeddings.{w}.weight"
            A__   :		List[str] 		=state_dict[param_name]
        for w in ["weight", "bias"]:
            A__   :		Tuple 		=F"{prefix}.embeddings.LayerNorm.{w}"
            A__   :		Any 		=state_dict[param_name]

    # Transformer Blocks #
    A__   :		Union[str, Any] 		=0
    for teacher_idx in [0, 2, 4, 7, 9, 11]:
        if args.model_type == "gpt2":
            for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                for w in ["weight", "bias"]:
                    A__   :		Union[str, Any] 		=state_dict[
                        F"{prefix}.h.{teacher_idx}.{layer}.{w}"
                    ]
            A__   :		int 		=state_dict[F"{prefix}.h.{teacher_idx}.attn.bias"]
        else:
            for layer in [
                "attention.self.query",
                "attention.self.key",
                "attention.self.value",
                "attention.output.dense",
                "attention.output.LayerNorm",
                "intermediate.dense",
                "output.dense",
                "output.LayerNorm",
            ]:
                for w in ["weight", "bias"]:
                    A__   :		Dict 		=state_dict[
                        F"{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"
                    ]
        std_idx += 1

    # Language Modeling Head ###s
    if args.model_type == "roberta":
        for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
            A__   :		Dict 		=state_dict[F"{layer}"]
        if args.vocab_transform:
            for w in ["weight", "bias"]:
                A__   :		str 		=state_dict[F"lm_head.dense.{w}"]
                A__   :		Any 		=state_dict[F"lm_head.layer_norm.{w}"]
    elif args.model_type == "gpt2":
        for w in ["weight", "bias"]:
            A__   :		Optional[int] 		=state_dict[F"{prefix}.ln_f.{w}"]
        A__   :		List[str] 		=state_dict['lm_head.weight']

    print(F"N layers selected for distillation: {std_idx}")
    print(F"Number of params transferred for distillation: {len(compressed_sd.keys())}")

    print(F"Save transferred checkpoint to {args.dump_checkpoint}.")
    torch.save(compressed_sd, args.dump_checkpoint)





'''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()



