






'''simple docstring'''






import argparse

import torch

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


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

__A    	=   [
    ['''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        _SCREAMING_SNAKE_CASE  (				A  :       Optional[int]  )    -> Optional[int]:







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

   for parlai_name, hf_name in PATTERNS:
      __snake_case				:      List[str]     							= k.replace(A  ,		A  )

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




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  )    -> Optional[int]:







   """simple docstring"""
   __snake_case				:      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:
      __snake_case				:      Any     							= sd.pop(A  )
      __snake_case				:      str     							= k.replace('layernorm_embedding'  ,		'layer_norm'  )
      assert new_k not in sd
      __snake_case				:      int     							= v


__A    	=   ['''START''']




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       str  ,		A  :       List[Any]  ,		A  :       List[Any]  )    -> Any:







   """simple docstring"""
   __snake_case				:      str     							= torch.load(A  ,		map_location='cpu'  )
   __snake_case				:      int     							= model['model']
   __snake_case				:      str     							= BlenderbotConfig.from_json_file(A  )
   __snake_case				:      Dict     							= BlenderbotForConditionalGeneration(A  )
   __snake_case				:      Any     							= m.model.state_dict().keys()
   __snake_case				:      List[str]     							= []
   __snake_case				:      int     							= {}
   for k, v in sd.items():
      if k in IGNORE_KEYS:
         continue

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


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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A    	=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''DistilBertTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DistilBertForMaskedLM''',
          '''DistilBertForMultipleChoice''',
          '''DistilBertForQuestionAnswering''',
          '''DistilBertForSequenceClassification''',
          '''DistilBertForTokenClassification''',
          '''DistilBertModel''',
          '''DistilBertPreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFDistilBertForMaskedLM''',
          '''TFDistilBertForMultipleChoice''',
          '''TFDistilBertForQuestionAnswering''',
          '''TFDistilBertForSequenceClassification''',
          '''TFDistilBertForTokenClassification''',
          '''TFDistilBertMainLayer''',
          '''TFDistilBertModel''',
          '''TFDistilBertPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''FlaxDistilBertForMaskedLM''',
          '''FlaxDistilBertForMultipleChoice''',
          '''FlaxDistilBertForQuestionAnswering''',
          '''FlaxDistilBertForSequenceClassification''',
          '''FlaxDistilBertForTokenClassification''',
          '''FlaxDistilBertModel''',
          '''FlaxDistilBertPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_distilbert import (
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DistilBertConfig,
          DistilBertOnnxConfig,
      )
      from .tokenization_distilbert import DistilBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_distilbert_fast import DistilBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_distilbert import (
                DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DistilBertForMaskedLM,
                DistilBertForMultipleChoice,
                DistilBertForQuestionAnswering,
                DistilBertForSequenceClassification,
                DistilBertForTokenClassification,
                DistilBertModel,
                DistilBertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_distilbert import (
                TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDistilBertForMaskedLM,
                TFDistilBertForMultipleChoice,
                TFDistilBertForQuestionAnswering,
                TFDistilBertForSequenceClassification,
                TFDistilBertForTokenClassification,
                TFDistilBertMainLayer,
                TFDistilBertModel,
                TFDistilBertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_distilbert import (
                FlaxDistilBertForMaskedLM,
                FlaxDistilBertForMultipleChoice,
                FlaxDistilBertForQuestionAnswering,
                FlaxDistilBertForSequenceClassification,
                FlaxDistilBertForTokenClassification,
                FlaxDistilBertModel,
                FlaxDistilBertPreTrainedModel,
            )

else:
      import sys

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