

import argparse

import torch

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


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

snake_case_			  = [
    ['''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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]			):



     '''simple docstring'''





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

     for parlai_name, hf_name in PATTERNS:
          lowercase__    :   int      =       k.replace(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)

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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[int]			):



     '''simple docstring'''





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


snake_case_			  = ['''START''']

@torch.no_grad()
def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





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

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


if __name__ == "__main__":
       snake_case_			  = 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_			  = 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_torch_available


snake_case_			  = {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_			  = [
           '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''InstructBlipQFormerModel''',
           '''InstructBlipPreTrainedModel''',
           '''InstructBlipForConditionalGeneration''',
           '''InstructBlipVisionModel''',
       ]

if TYPE_CHECKING:
       from .configuration_instructblip import (
           INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           InstructBlipConfig,
           InstructBlipQFormerConfig,
           InstructBlipVisionConfig,
       )
       from .processing_instructblip import InstructBlipProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_instructblip import (
                  INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  InstructBlipForConditionalGeneration,
                  InstructBlipPreTrainedModel,
                  InstructBlipQFormerModel,
                  InstructBlipVisionModel,
              )

else:
       import sys

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

