



'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  lowercase_	 =						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")
  lowercase_	 =						parser.parse_args()

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

  lowercase_	 =						model.state_dict()
  lowercase_	 =						{}

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

    # Transformer Blocks #
  lowercase_	 =						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"]:
          lowercase_	 =						state_dict[
              F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
          ]
      lowercase_	 =						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"]:
          lowercase_	 =						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"]:
      lowercase_	 =						state_dict[F"""{layer}"""]
    if args.vocab_transform:
      for w in ["weight", "bias"]:
        lowercase_	 =						state_dict[F"""lm_head.dense.{w}"""]
        lowercase_	 =						state_dict[F"""lm_head.layer_norm.{w}"""]
  elif args.model_type == "gpt2":
    for w in ["weight", "bias"]:
      lowercase_	 =						state_dict[F"""{prefix}.ln_f.{w}"""]
    lowercase_	 =						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 __future__ import annotations

lowercase_	 =						[True] * 1_000_001
lowercase_	 =						2
while i * i <= 1_000_000:
  if seive[i]:
    for j in range(i * i, 1_000_001, i):
      lowercase_	 =						False
  i += 1




def lowerCAmelCase						(__A):



     """simple docstring"""



     return seive[n]




def lowerCAmelCase						(__A):



     """simple docstring"""



     return any(digit in '''02468''' for digit in str(__A))




def lowerCAmelCase						(__A = 1_000_000):



     """simple docstring"""



     _a											=						[2]  # result already includes the number 2.
     for num in range(3						,			limit + 1						,			2):
          if is_prime(__A) and not contains_an_even_digit(__A):
               _a											=						str(__A)
               _a											=						[int(str_num[j:] + str_num[:j]) for j in range(len(__A))]
               if all(is_prime(__A) for i in list_nums):
                    result.append(__A)
     return result




def lowerCAmelCase						():



     """simple docstring"""



     return len(find_circular_primes())


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



