"""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    :       List[Any]					     =parser.parse_args()

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

       __lowerCAmelCase    :       Tuple					     =model.state_dict()
       __lowerCAmelCase    :       Optional[int]					     ={}

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

    # Transformer Blocks #
       __lowerCAmelCase    :       Tuple					     =0
       for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
              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    :       Optional[int]					     =state_dict[
                                       F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                                   ]
                     __lowerCAmelCase    :       Tuple					     =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    :       Tuple					     =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    :       List[Any]					     =state_dict[F"""lm_head.dense.{w}"""]
                            __lowerCAmelCase    :       List[str]					     =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    :       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)




"""simple docstring"""






from decimal import Decimal, getcontext
from math import ceil, factorial


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    str:
      '''simple docstring'''


      if not isinstance(lowerCAmelCase__					,		lowerCAmelCase__		):
            raise TypeError("""Undefined for non-integers"""		)
      elif precision < 1:
            raise ValueError("""Undefined for non-natural numbers"""		)

      lowercase														=	precision
      lowercase														=	ceil(precision / 1_4		)
      lowercase														=	4_2_6_8_8_0 * Decimal(1_0_0_0_5		).sqrt()
      lowercase														=	1
      lowercase														=	1_3_5_9_1_4_0_9
      lowercase														=	Decimal(lowerCAmelCase__		)
      for k in range(1					,		lowerCAmelCase__		):
            lowercase														=	factorial(6 * k		) // (factorial(3 * k		) * factorial(lowerCAmelCase__		) ** 3)
            linear_term += 5_4_5_1_4_0_1_3_4
            exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
            partial_sum += Decimal(multinomial_term * linear_term		) / exponential_term
      return str(constant_term / partial_sum		)[:-1]


if __name__ == "__main__":
       __lowerCAmelCase    :       List[Any]					     =5_0
       print(F"""The first {n} digits of pi is: {pi(n)}""")



