





'''simple docstring'''




from typing import TYPE_CHECKING

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


lowercase__       : Any         =       {'''configuration_mbart''': ['''MBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MBartConfig''', '''MBartOnnxConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : Union[str, Any]         =       ['''MBartTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : Dict         =       ['''MBartTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : List[str]         =       [
      '''MBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MBartForCausalLM''',
      '''MBartForConditionalGeneration''',
      '''MBartForQuestionAnswering''',
      '''MBartForSequenceClassification''',
      '''MBartModel''',
      '''MBartPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : Dict         =       [
      '''TFMBartForConditionalGeneration''',
      '''TFMBartModel''',
      '''TFMBartPreTrainedModel''',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__       : str         =       [
      '''FlaxMBartForConditionalGeneration''',
      '''FlaxMBartForQuestionAnswering''',
      '''FlaxMBartForSequenceClassification''',
      '''FlaxMBartModel''',
      '''FlaxMBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart import MBartTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbart_fast import MBartTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mbart import (
        MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        MBartForCausalLM,
        MBartForConditionalGeneration,
        MBartForQuestionAnswering,
        MBartForSequenceClassification,
        MBartModel,
        MBartPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_mbart import (
        FlaxMBartForConditionalGeneration,
        FlaxMBartForQuestionAnswering,
        FlaxMBartForSequenceClassification,
        FlaxMBartModel,
        FlaxMBartPreTrainedModel,
    )

else:
  import sys

  lowercase__       : Any         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''




import argparse

import torch

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


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)

lowercase__       : 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 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )

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






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	Dict:
     __A		:					Optional[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:
          __A		:					Tuple						=			sd.pop(__snake_case     )
          __A		:					Union[str, Any]						=			k.replace('layernorm_embedding'					,				'layer_norm'     )
          assert new_k not in sd
          __A		:					str						=			v


lowercase__       : Tuple         =       ['''START''']






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

          __A		:					Union[str, Any]						=			rename_state_dict_key(__snake_case     )
          if new_k not in valid_keys:
               failures.append([k, new_k]     )
          else:
               __A		:					Optional[Any]						=			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__":
  lowercase__       : Dict         =       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'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)