import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   __lowerCamelCase      :			Optional[int]							       =		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''')
   __lowerCamelCase      :			List[str]							       =		parser.parse_args()

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

   __lowerCamelCase      :			int							       =		model.state_dict()
   __lowerCamelCase      :			Tuple							       =		{}

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

    # Transformer Blocks #
   __lowerCamelCase      :			Optional[int]							       =		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"]:
               __lowerCamelCase      :			List[str]							       =		state_dict[
                   F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
               ]
         __lowerCamelCase      :			List[Any]							       =		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"]:
               __lowerCamelCase      :			List[Any]							       =		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"]:
         __lowerCamelCase      :			Union[str, Any]							       =		state_dict[F"""{layer}"""]
      if args.vocab_transform:
         for w in ["weight", "bias"]:
            __lowerCamelCase      :			Optional[int]							       =		state_dict[F"""lm_head.dense.{w}"""]
            __lowerCamelCase      :			Optional[int]							       =		state_dict[F"""lm_head.layer_norm.{w}"""]
   elif args.model_type == "gpt2":
      for w in ["weight", "bias"]:
         __lowerCamelCase      :			Optional[int]							       =		state_dict[F"""{prefix}.ln_f.{w}"""]
      __lowerCamelCase      :			Tuple							       =		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)

from __future__ import annotations

__lowerCamelCase      :			Dict							       =		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__lowerCamelCase      :			Union[str, Any]							       =		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for j in range(i + 1			,					__UpperCamelCase     ):
      if arr[i] < arr[j]:
        SCREAMING_SNAKE_CASE__      							=				arr[j]
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[]
  for i, outer in enumerate(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				-1
    for inner in arr[i + 1 :]:
      if outer < inner:
        SCREAMING_SNAKE_CASE__      							=				inner
        break
    result.append(__UpperCamelCase     )
  return result



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list[float]     )							-> list[float]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				[]
  SCREAMING_SNAKE_CASE__      							=				[-1] * arr_size

  for index in reversed(range(__UpperCamelCase     )     ):
    if stack:
      while stack[-1] <= arr[index]:
        stack.pop()
        if not stack:
          break
    if stack:
      SCREAMING_SNAKE_CASE__      							=				stack[-1]
    stack.append(arr[index]     )
  return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   __lowerCamelCase      :			List[Any]							       =		(
       '''from __main__ import arr, next_greatest_element_slow, '''
       '''next_greatest_element_fast, next_greatest_element'''
   )
   print(
       '''next_greatest_element_slow():''',
       timeit('''next_greatest_element_slow(arr)''', setup=setup),
   )
   print(
       '''next_greatest_element_fast():''',
       timeit('''next_greatest_element_fast(arr)''', setup=setup),
   )
   print(
       '''     next_greatest_element():''',
       timeit('''next_greatest_element(arr)''', setup=setup),
   )
