


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    snake_case_     (  snake_case     )  ->				Optional[int]:
       if k == "embeddings.weight":
              return "shared.weight"

       for parlai_name, hf_name in PATTERNS:
              lowercase__:   Union[str, Any]  			=   k.replace(snake_case			,							snake_case     )

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




def    snake_case_     (  snake_case     )  ->				Dict:
       lowercase__:   List[Any]  			=   [
           'model.encoder.layernorm_embedding.weight',
           'model.encoder.layernorm_embedding.bias',
           'model.decoder.layernorm_embedding.weight',
           'model.decoder.layernorm_embedding.bias',
       ]
       for k in keys:
              lowercase__:   List[Any]  			=   sd.pop(snake_case     )
              lowercase__:   List[str]  			=   k.replace('layernorm_embedding'			,							'layer_norm'     )
              assert new_k not in sd
              lowercase__:   int  			=   v


__lowerCAmelCase		       =							['''START''']




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

              lowercase__:   Union[str, Any]  			=   rename_state_dict_key(snake_case     )
              if new_k not in valid_keys:
                     failures.append([k, new_k]     )
              else:
                     lowercase__:   str  			=   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__":
 __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)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase		       =							{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CanineForMultipleChoice''',
     '''CanineForQuestionAnswering''',
     '''CanineForSequenceClassification''',
     '''CanineForTokenClassification''',
     '''CanineLayer''',
     '''CanineModel''',
     '''CaninePreTrainedModel''',
     '''load_tf_weights_in_canine''',
 ]


if TYPE_CHECKING:
 from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
 from .tokenization_canine import CanineTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_canine import (
      CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
      CanineForMultipleChoice,
      CanineForQuestionAnswering,
      CanineForSequenceClassification,
      CanineForTokenClassification,
      CanineLayer,
      CanineModel,
      CaninePreTrainedModel,
      load_tf_weights_in_canine,
  )


else:
 import sys

 __lowerCAmelCase		       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


