




'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

   if args.model_type == "roberta":
      UpperCamelCase_		:     Any          =		RobertaForMaskedLM.from_pretrained(args.model_name)
      UpperCamelCase_		:     Union[str, Any]          =		"""roberta"""
   elif args.model_type == "gpt2":
      UpperCamelCase_		:     Optional[Any]          =		GPTaLMHeadModel.from_pretrained(args.model_name)
      UpperCamelCase_		:     Optional[Any]          =		"""transformer"""

   UpperCamelCase_		:     Optional[Any]          =		model.state_dict()
   UpperCamelCase_		:     List[str]          =		{}

   # Embeddings #
   if args.model_type == "gpt2":
      for param_name in ["wte.weight", "wpe.weight"]:
         UpperCamelCase_		:     Dict          =		state_dict[F'{prefix}.{param_name}']
   else:
      for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
         UpperCamelCase_		:     int          =		F'{prefix}.embeddings.{w}.weight'
         UpperCamelCase_		:     int          =		state_dict[param_name]
      for w in ["weight", "bias"]:
         UpperCamelCase_		:     Optional[int]          =		F'{prefix}.embeddings.LayerNorm.{w}'
         UpperCamelCase_		:     Dict          =		state_dict[param_name]

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







def        __a			(   __lowerCAmelCase   )     ->     List[str]:
					stooge(__lowerCAmelCase      ,	0      ,	len(__lowerCAmelCase   ) - 1   )
					return arr

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     int:
					if i >= h:
										return

					# If first element is smaller than the last then swap them
					if arr[i] > arr[h]:
										SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				arr[h], arr[i]

					# If there are more than 2 elements in the array
					if h - i + 1 > 2:
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(int)((h - i + 1) / 3   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )

										# Recursively sort last 2/3 elements
										stooge(__lowerCAmelCase      ,	i + t      ,	(__lowerCAmelCase)   )

										# Recursively sort first 2/3 elements
										stooge(__lowerCAmelCase      ,	__lowerCAmelCase      ,	(h - t)   )


if __name__ == "__main__":
			_lowerCamelCase    :		List[str]		      =    input("""Enter numbers separated by a comma:\n""").strip()
			_lowerCamelCase    :		List[Any]		      =    [int(item) for item in user_input.split(""",""")]
			print(stooge_sort(unsorted))