






import argparse

import torch

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


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

UpperCamelCase    							=      [
    ['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 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]							)     ->  Any:
   if k == "embeddings.weight":
      return "shared.weight"

   for parlai_name, hf_name in PATTERNS:
      __UpperCamelCase	:			Dict					=					k.replace(__lowerCAmelCase					,							__lowerCAmelCase							)

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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]							)     ->  Any:
   __UpperCamelCase	:			int					=					[
       """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	:			Optional[Any]					=					sd.pop(__lowerCAmelCase							)
      __UpperCamelCase	:			List[str]					=					k.replace("""layernorm_embedding"""					,							"""layer_norm"""							)
      assert new_k not in sd
      __UpperCamelCase	:			Optional[int]					=					v


UpperCamelCase    							=      ['START']


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

      __UpperCamelCase	:			Optional[int]					=					rename_state_dict_key(__lowerCAmelCase							)
      if new_k not in valid_keys:
         failures.append([k, new_k]							)
      else:
         __UpperCamelCase	:			int					=					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__":
       UpperCamelCase    							=      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'
       )
       UpperCamelCase    							=      parser.parse_args()
       convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)











def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  bool:
   if not all(x.isalpha() for x in string							):
      raise ValueError("""String must only contain alphabetic characters."""							)

   __UpperCamelCase	:			List[str]					=					sorted(string.lower()							)
   return len(__lowerCAmelCase							) == len(set(__lowerCAmelCase							)							)


if __name__ == "__main__":
       UpperCamelCase    							=      input('Enter a string ').strip()

       UpperCamelCase    							=      is_isogram(input_str)
       print(F"""{input_str} is {'an' if isogram else 'not an'} isogram.""")



