






"""simple docstring"""






import argparse

import torch

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


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

UpperCAmelCase_     :				Any         =				[
    ["""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    (__a							)				-> Union[str, Any]:
    """simple docstring"""






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

    for parlai_name, hf_name in PATTERNS:
        SCREAMING_SNAKE_CASE_   :						Any							=	k.replace(__a					,       __a							)

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



def       _A    (__a							)				-> List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	[
        '''model.encoder.layernorm_embedding.weight''',
        '''model.encoder.layernorm_embedding.bias''',
        '''model.decoder.layernorm_embedding.weight''',
        '''model.decoder.layernorm_embedding.bias''',
    ]
    for k in keys:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	sd.pop(__a							)
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	k.replace('''layernorm_embedding'''					,       '''layer_norm'''							)
        assert new_k not in sd
        SCREAMING_SNAKE_CASE_   :						Dict							=	v


UpperCAmelCase_     :				str         =				["""START"""]



@torch.no_grad()
def       _A    (__a					,       __a					,       __a							)				-> Optional[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	torch.load(__a					,       map_location='''cpu'''							)
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	model['''model''']
    SCREAMING_SNAKE_CASE_   :						Tuple							=	BlenderbotConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_   :						int							=	BlenderbotForConditionalGeneration(__a							)
    SCREAMING_SNAKE_CASE_   :						Dict							=	m.model.state_dict().keys()
    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	[]
    SCREAMING_SNAKE_CASE_   :						Any							=	{}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        SCREAMING_SNAKE_CASE_   :						Tuple							=	rename_state_dict_key(__a							)
        if new_k not in valid_keys:
            failures.append([k, new_k]							)
        else:
            SCREAMING_SNAKE_CASE_   :						str							=	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__":
  UpperCAmelCase_     :				int         =				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_     :				Optional[int]         =				parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)










"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Dict         =				{
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"data2vec-text"
      def __init__(		self  :						Any		,			lowercase_  :						Any=30522		,			lowercase_  :						Any=768		,			lowercase_  :						Union[str, Any]=12		,			lowercase_  :						Dict=12		,			lowercase_  :						List[Any]=3072		,			lowercase_  :						str="gelu"		,			lowercase_  :						int=0.1		,			lowercase_  :						Dict=0.1		,			lowercase_  :						str=512		,			lowercase_  :						Optional[int]=2		,			lowercase_  :						int=0.02		,			lowercase_  :						int=1e-12		,			lowercase_  :						Any=1		,			lowercase_  :						Any=0		,			lowercase_  :						List[Any]=2		,			lowercase_  :						Tuple="absolute"		,			lowercase_  :						Optional[int]=True		,			lowercase_  :						int=None		,			**lowercase_  :						Tuple		,			):




          '''simple docstring'''


          super().__init__(pad_token_id=lowercase_		,			bos_token_id=lowercase_		,			eos_token_id=lowercase_		,			**lowercase_)

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	vocab_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	num_hidden_layers
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	num_attention_heads
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	hidden_act
          SCREAMING_SNAKE_CASE_   :						Any							=	intermediate_size
          SCREAMING_SNAKE_CASE_   :						Tuple							=	hidden_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Tuple							=	attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	max_position_embeddings
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	type_vocab_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	initializer_range
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	layer_norm_eps
          SCREAMING_SNAKE_CASE_   :						str							=	position_embedding_type
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	use_cache
          SCREAMING_SNAKE_CASE_   :						str							=	classifier_dropout




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          if self.task == "multiple-choice":
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              SCREAMING_SNAKE_CASE_   :						Any							=	{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ])


