






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
    lowerCamelCase__							=     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__							=     parser.parse_args()

    if args.model_type == "roberta":
        lowerCamelCase__							=     RobertaForMaskedLM.from_pretrained(args.model_name)
        lowerCamelCase__							=     """roberta"""
    elif args.model_type == "gpt2":
        lowerCamelCase__							=     GPTaLMHeadModel.from_pretrained(args.model_name)
        lowerCamelCase__							=     """transformer"""

    lowerCamelCase__							=     model.state_dict()
    lowerCamelCase__							=     {}

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

    # Transformer Blocks #
    lowerCamelCase__							=     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__							=     state_dict[
                        f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                    ]
            lowerCamelCase__							=     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__							=     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__							=     state_dict[f'''{layer}''']
        if args.vocab_transform:
            for w in ["weight", "bias"]:
                lowerCamelCase__							=     state_dict[f'''lm_head.dense.{w}''']
                lowerCamelCase__							=     state_dict[f'''lm_head.layer_norm.{w}''']
    elif args.model_type == "gpt2":
        for w in ["weight", "bias"]:
            lowerCamelCase__							=     state_dict[f'''{prefix}.ln_f.{w}''']
        lowerCamelCase__							=     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   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]
     __snake_case			:Union[str, Any]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1 or len(snake_case__  ) <= key:
          return input_string

     for position, character in enumerate(snake_case__  ):
          __snake_case			:Any          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:Optional[int]          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append(snake_case__  )
     __snake_case			:List[Any]          =						["""""".join(snake_case__  ) for row in temp_grid]
     __snake_case			:Any          =						"""""".join(snake_case__  )

     return output_string






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						[]
     __snake_case			:Optional[int]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1:
          return input_string

     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]  # generates template
     for position in range(len(snake_case__  )  ):
          __snake_case			:List[str]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append("""*"""  )

     __snake_case			:str          =						0
     for row in temp_grid:  # fills in the characters
          __snake_case			:str          =						input_string[counter : counter + len(snake_case__  )]
          grid.append(list(snake_case__  )  )
          counter += len(snake_case__  )

     __snake_case			:Any          =						""""""  # reads as zigzag
     for position in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          output_string += grid[num][0]
          grid[num].pop(0  )
     return output_string






def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						{}
     for key_guess in range(1	,len(snake_case__  )  ):  # tries every key
          __snake_case			:Dict          =						decrypt(snake_case__	,snake_case__  )
     return results


if __name__ == "__main__":
     import doctest

     doctest.testmod()


