'''simple docstring'''






import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_	   =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	   =			[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	Dict:


  if k == "embeddings.weight":
    return "shared.weight"

  for parlai_name, hf_name in PATTERNS:
    __UpperCAmelCase:					Optional[Any]		= k.replace(__snake_case   ,    __snake_case     )

  if k.startswith("""encoder"""     ):
    __UpperCAmelCase:					int		= k.replace(""".attn"""   ,    """.self_attn"""     )
    __UpperCAmelCase:					int		= k.replace("""norm1"""   ,    """self_attn_layer_norm"""     )
    __UpperCAmelCase:					List[str]		= k.replace("""norm2"""   ,    """final_layer_norm"""     )
  elif k.startswith("""decoder"""     ):
    __UpperCAmelCase:					Optional[Any]		= k.replace("""norm1"""   ,    """self_attn_layer_norm"""     )
    __UpperCAmelCase:					Optional[int]		= k.replace("""norm2"""   ,    """encoder_attn_layer_norm"""     )
    __UpperCAmelCase:					Dict		= k.replace("""norm3"""   ,    """final_layer_norm"""     )
  return k






def  UpperCamelCase__					(					_lowercase    :				Optional[Any]     )							->	List[Any]:
  __UpperCAmelCase:					str		= [
      "model.encoder.layernorm_embedding.weight",
      "model.encoder.layernorm_embedding.bias",
      "model.decoder.layernorm_embedding.weight",
      "model.decoder.layernorm_embedding.bias",
  ]
  for k in keys:
    __UpperCAmelCase:					int		= sd.pop(__snake_case     )
    __UpperCAmelCase:					int		= k.replace("""layernorm_embedding"""   ,    """layer_norm"""     )
    assert new_k not in sd
    __UpperCAmelCase:					str		= v


SCREAMING_SNAKE_CASE_	   =			["START"]






@torch.no_grad()
def  UpperCamelCase__					(					_lowercase    :				int   ,    _lowercase    :				Dict   ,    _lowercase    :				List[Any]     )							->	Union[str, Any]:
  __UpperCAmelCase:					int		= torch.load(__snake_case   ,    map_location="""cpu"""     )
  __UpperCAmelCase:					int		= model["model"]
  __UpperCAmelCase:					List[Any]		= BlenderbotConfig.from_json_file(__snake_case     )
  __UpperCAmelCase:					Optional[Any]		= BlenderbotForConditionalGeneration(__snake_case     )
  __UpperCAmelCase:					List[Any]		= m.model.state_dict().keys()
  __UpperCAmelCase:					int		= []
  __UpperCAmelCase:					List[str]		= {}
  for k, v in sd.items():
    if k in IGNORE_KEYS:
      continue

    __UpperCAmelCase:					Any		= rename_state_dict_key(__snake_case     )
    if new_k not in valid_keys:
      failures.append([k, new_k]     )
    else:
      __UpperCAmelCase:					List[Any]		= v
  if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
    rename_layernorm_keys(__snake_case     )
  m.model.load_state_dict(__snake_case   ,    strict=__snake_case     )
  m.half()
  m.save_pretrained(__snake_case     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
    parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
    parser.add_argument(
        '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
    )
    SCREAMING_SNAKE_CASE_	   =			parser.parse_args()
    convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)
'''simple docstring'''






import itertools
import math






def  UpperCamelCase__					(					_lowercase    :				int     )							->	bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5   ,    int(math.sqrt(_lowercase     ) + 1     )   ,    6     ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True






def  UpperCamelCase__					(					)							->	Optional[int]:
  __UpperCAmelCase:					Union[str, Any]		= 2
  while True:
    if is_prime(_lowercase     ):
      yield num
    num += 1






def  UpperCamelCase__					(					_lowercase    :				int = 1_0_0_0_1     )							->	int:
  return next(itertools.islice(prime_generator()   ,    nth - 1   ,    _lowercase     )     )


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