



import argparse

import torch

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


logging.set_verbosity_info()
_snake_case  :   str						   =  logging.get_logger(__name__)

_snake_case  :   str						   =  [
    ['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_				:      Dict     ):
   if k == "embeddings.weight":
      return "shared.weight"

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

   if k.startswith('encoder'     ):
      __lowerCAmelCase							     =     k.replace('.attn',     '.self_attn'     )
      __lowerCAmelCase							     =     k.replace('norm1',     'self_attn_layer_norm'     )
      __lowerCAmelCase							     =     k.replace('norm2',     'final_layer_norm'     )
   elif k.startswith('decoder'     ):
      __lowerCAmelCase							     =     k.replace('norm1',     'self_attn_layer_norm'     )
      __lowerCAmelCase							     =     k.replace('norm2',     'encoder_attn_layer_norm'     )
      __lowerCAmelCase							     =     k.replace('norm3',     'final_layer_norm'     )
   return k

def   a_   (			lowerCAmelCase_				:      Dict     ):
   __lowerCAmelCase							     =     [
       'model.encoder.layernorm_embedding.weight',
       'model.encoder.layernorm_embedding.bias',
       'model.decoder.layernorm_embedding.weight',
       'model.decoder.layernorm_embedding.bias',
   ]
   for k in keys:
      __lowerCAmelCase							     =     sd.pop(lowerCAmelCase_     )
      __lowerCAmelCase							     =     k.replace('layernorm_embedding',     'layer_norm'     )
      assert new_k not in sd
      __lowerCAmelCase							     =     v


_snake_case  :   Dict						   =  ['START']

@torch.no_grad()
def   a_   (			lowerCAmelCase_				:      Union[str, Any],     lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Tuple     ):
   __lowerCAmelCase							     =     torch.load(lowerCAmelCase_,     map_location='cpu'     )
   __lowerCAmelCase							     =     model['model']
   __lowerCAmelCase							     =     BlenderbotConfig.from_json_file(lowerCAmelCase_     )
   __lowerCAmelCase							     =     BlenderbotForConditionalGeneration(lowerCAmelCase_     )
   __lowerCAmelCase							     =     m.model.state_dict().keys()
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     {}
   for k, v in sd.items():
      if k in IGNORE_KEYS:
         continue

      __lowerCAmelCase							     =     rename_state_dict_key(lowerCAmelCase_     )
      if new_k not in valid_keys:
         failures.append([k, new_k]     )
      else:
         __lowerCAmelCase							     =     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__":
     _snake_case  :   List[Any]						   =  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'
     )
     _snake_case  :   Optional[Any]						   =  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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FNetForMaskedLM',
         'FNetForMultipleChoice',
         'FNetForNextSentencePrediction',
         'FNetForPreTraining',
         'FNetForQuestionAnswering',
         'FNetForSequenceClassification',
         'FNetForTokenClassification',
         'FNetLayer',
         'FNetModel',
         'FNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

     _snake_case  :   str						   =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

