
'''simple docstring'''

import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
    __snake_case:  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")
    __snake_case:  Union[str, Any]   							=			parser.parse_args()

    if args.model_type == "roberta":
        __snake_case:  Optional[int]   							=			RobertaForMaskedLM.from_pretrained(args.model_name)
        __snake_case:  str   							=			"roberta"
    elif args.model_type == "gpt2":
        __snake_case:  str   							=			GPTaLMHeadModel.from_pretrained(args.model_name)
        __snake_case:  List[str]   							=			"transformer"

    __snake_case:  Tuple   							=			model.state_dict()
    __snake_case:  Optional[Any]   							=			{}

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

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

from math import factorial

__snake_case:  List[Any]								=			{str(d): factorial(d) for d in range(10)}






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  return sum(DIGIT_FACTORIAL[d] for d in str(A_   )   )






def 	_snake_case						(      ):
  """simple docstring"""







  a_      :	Union[str, Any]      =							7 * factorial(9   ) + 1
  return sum(i for i in range(3     ,		A_   ) if sum_of_digit_factorial(A_   ) == i   )


if __name__ == "__main__":
    print(F"""{solution() = }""")


