





'''simple docstring'''

import argparse

import torch

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


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

UpperCAmelCase					:		List[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    (							lowerCAmelCase_	):





      """simple docstring"""



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

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

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




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	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:
            _snake_case		:	Optional[int]					     =     sd.pop(lowerCAmelCase_	)
            _snake_case		:	Tuple					     =     k.replace('''layernorm_embedding'''			,      '''layer_norm'''	)
            assert new_k not in sd
            _snake_case		:	Any					     =     v


UpperCAmelCase					:		Any		=							['START']




@torch.no_grad()
def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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

            _snake_case		:	Optional[int]					     =     rename_state_dict_key(lowerCAmelCase_	)
            if new_k not in valid_keys:
                  failures.append([k, new_k]	)
            else:
                  _snake_case		:	str					     =     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__":
   UpperCAmelCase					:		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'
   )
   UpperCAmelCase					:		str		=							parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
   from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


