



"""simple docstring"""





import argparse

import torch

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


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

lowerCamelCase      =		[
    ["""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      a__			(   lowerCAmelCase__   ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            UpperCAmelCase_    				=							k.replace(lowerCAmelCase__			,    lowerCAmelCase__   )

      if k.startswith("encoder"   ):
            UpperCAmelCase_    				=							k.replace(".attn"			,    ".self_attn"   )
            UpperCAmelCase_    				=							k.replace("norm1"			,    "self_attn_layer_norm"   )
            UpperCAmelCase_    				=							k.replace("norm2"			,    "final_layer_norm"   )
      elif k.startswith("decoder"   ):
            UpperCAmelCase_    				=							k.replace("norm1"			,    "self_attn_layer_norm"   )
            UpperCAmelCase_    				=							k.replace("norm2"			,    "encoder_attn_layer_norm"   )
            UpperCAmelCase_    				=							k.replace("norm3"			,    "final_layer_norm"   )
      return k
def      a__			(   lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[
          "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_    				=							sd.pop(lowerCAmelCase__   )
            UpperCAmelCase_    				=							k.replace("layernorm_embedding"			,    "layer_norm"   )
            assert new_k not in sd
            UpperCAmelCase_    				=							v


lowerCamelCase      =		["""START"""]
@torch.no_grad()
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							torch.load(lowerCAmelCase__			,    map_location="cpu"   )
      UpperCAmelCase_    				=							model["model"]
      UpperCAmelCase_    				=							BlenderbotConfig.from_json_file(lowerCAmelCase__   )
      UpperCAmelCase_    				=							BlenderbotForConditionalGeneration(lowerCAmelCase__   )
      UpperCAmelCase_    				=							m.model.state_dict().keys()
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_    				=							{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

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


if __name__ == "__main__":
  lowerCamelCase      =		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"""
  )
  lowerCamelCase      =		parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)









"""simple docstring"""





from __future__ import annotations

import math
def      a__			(   lowerCAmelCase__   ):

      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(lowerCAmelCase__   ) + 1   )			,    6   ):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True


lowerCamelCase      =		[num for num in range(3, 100_001, 2) if not is_prime(num)]
def      a__			(   lowerCAmelCase__   ):
      if not isinstance(lowerCAmelCase__			,    lowerCAmelCase__   ):
            raise ValueError("n must be an integer"   )
      if n <= 0:
            raise ValueError("n must be >= 0"   )

      UpperCAmelCase_    				=							[]
      for num in range(len(lowerCAmelCase__   )   ):
            UpperCAmelCase_    				=							0
            while 2 * i * i <= odd_composites[num]:
                  UpperCAmelCase_    				=							odd_composites[num] - 2 * i * i
                  if is_prime(lowerCAmelCase__   ):
                        break
                  i += 1
            else:
                  list_nums.append(odd_composites[num]   )
                  if len(lowerCAmelCase__   ) == n:
                        return list_nums

      return []
def      a__			(   ):
      return compute_nums(1   )[0]


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




