





"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

   if args.model_type == "roberta":
      lowerCAmelCase:						Dict				   =RobertaForMaskedLM.from_pretrained(args.model_name)
      lowerCAmelCase:						str				   ="roberta"
   elif args.model_type == "gpt2":
      lowerCAmelCase:						Dict				   =GPTaLMHeadModel.from_pretrained(args.model_name)
      lowerCAmelCase:						List[Any]				   ="transformer"

   lowerCAmelCase:						List[str]				   =model.state_dict()
   lowerCAmelCase:						Optional[int]				   ={}

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

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

lowerCamelCase		   =			{
    'MALAYALAM': True,
    'String': False,
    'rotor': True,
    'level': True,
    'A': True,
    'BB': True,
    'ABC': False,
    'amanaplanacanalpanama': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =0
       _lowerCamelCase			:						Union[str, Any]            =len(SCREAMING_SNAKE_CASE__	) - 1
       while start_i < end_i:
              if s[start_i] == s[end_i]:
                     start_i += 1
                     end_i -= 1
              else:
                     return False
       return True




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						List[str]            =len(SCREAMING_SNAKE_CASE__	) // 2
       _lowerCamelCase			:						Optional[Any]            =len(SCREAMING_SNAKE_CASE__	)

       # We need to traverse till half of the length of string
       # as we can get access of the i'th last element from
       # i'th index.
       # eg: [0,1,2,3,4,5] => 4th index can be accessed
       # with the help of 1st index (i==n-i-1)
       # where n is length of string
       return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__	)	)




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       if len(SCREAMING_SNAKE_CASE__	) <= 2:
              return True
       if s[0] == s[len(SCREAMING_SNAKE_CASE__	) - 1]:
              return is_palindrome_recursive(s[1:-1]	)
       else:
              return False




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       return s == s[::-1]




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =F'''all({name}(key) is value for key, value in test_data.items())'''
       _lowerCamelCase			:						List[Any]            =F'''from __main__ import test_data, {name}'''
       _lowerCamelCase			:						Any            =500_000
       _lowerCamelCase			:						Dict            =timeit(stmt=SCREAMING_SNAKE_CASE__						,     setup=SCREAMING_SNAKE_CASE__						,     number=SCREAMING_SNAKE_CASE__	)
       print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''	)


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(F"""{key:21} {value}""")
 print('a man a plan a canal panama')

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function('is_palindrome_slice')
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function('is_palindrome')
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function('is_palindrome_recursive')
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function('is_palindrome_traversal')


