
'''simple docstring'''






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)

UpperCAmelCase_       :   List[Any]   =       {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'instructblip_vision_model'



    def __init__(	self      :       List[Any],__A      :       Dict=1_4_0_8,__A      :       Dict=6_1_4_4,__A      :       Any=3_9,__A      :       Optional[int]=1_6,__A      :       Any=2_2_4,__A      :       List[str]=1_4,__A      :       Any="gelu",__A      :       int=1e-6,__A      :       str=0.0,__A      :       Dict=1e-10,__A      :       List[Any]=True,**__A      :       List[Any],):
          super().__init__(**__A     )

          _lowerCamelCase				:   Dict								=   hidden_size
          _lowerCamelCase				:   Any								=   intermediate_size
          _lowerCamelCase				:   List[str]								=   num_hidden_layers
          _lowerCamelCase				:   str								=   num_attention_heads
          _lowerCamelCase				:   List[str]								=   patch_size
          _lowerCamelCase				:   Tuple								=   image_size
          _lowerCamelCase				:   int								=   initializer_range
          _lowerCamelCase				:   Any								=   attention_dropout
          _lowerCamelCase				:   Tuple								=   layer_norm_eps
          _lowerCamelCase				:   Optional[Any]								=   hidden_act
          _lowerCamelCase				:   Optional[int]								=   qkv_bias



    @classmethod
    def   lowerCamelCase_  (	cls      :       Dict,__A      :       Union[str, os.PathLike],**__A      :       List[Any]     ):
          cls._set_token_in_kwargs(__A     )

          _lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   cls.get_config_dict(__A,**__A     )

          # get the vision config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"     ) == "instructblip":
                _lowerCamelCase				:   Dict								=   config_dict["vision_config"]

          if "model_type" in config_dict and hasattr(cls,"model_type"     ) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'     )

          return cls.from_dict(__A,**__A     )


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'instructblip_qformer'



    def __init__(	self      :       int,__A      :       Union[str, Any]=3_0_5_2_2,__A      :       Any=7_6_8,__A      :       Union[str, Any]=1_2,__A      :       List[str]=1_2,__A      :       Optional[Any]=3_0_7_2,__A      :       Dict="gelu",__A      :       Tuple=0.1,__A      :       str=0.1,__A      :       Union[str, Any]=5_1_2,__A      :       Optional[int]=0.02,__A      :       List[Any]=1e-12,__A      :       str=0,__A      :       Union[str, Any]="absolute",__A      :       str=2,__A      :       List[Any]=1_4_0_8,**__A      :       Optional[Any],):
          super().__init__(pad_token_id=__A,**__A     )

          _lowerCamelCase				:   Optional[int]								=   vocab_size
          _lowerCamelCase				:   Optional[Any]								=   hidden_size
          _lowerCamelCase				:   Optional[int]								=   num_hidden_layers
          _lowerCamelCase				:   Tuple								=   num_attention_heads
          _lowerCamelCase				:   Union[str, Any]								=   hidden_act
          _lowerCamelCase				:   Any								=   intermediate_size
          _lowerCamelCase				:   List[Any]								=   hidden_dropout_prob
          _lowerCamelCase				:   Any								=   attention_probs_dropout_prob
          _lowerCamelCase				:   Dict								=   max_position_embeddings
          _lowerCamelCase				:   List[Any]								=   initializer_range
          _lowerCamelCase				:   Optional[Any]								=   layer_norm_eps
          _lowerCamelCase				:   Tuple								=   position_embedding_type
          _lowerCamelCase				:   Optional[int]								=   cross_attention_frequency
          _lowerCamelCase				:   Dict								=   encoder_hidden_size



    @classmethod
    def   lowerCamelCase_  (	cls      :       Dict,__A      :       Union[str, os.PathLike],**__A      :       Tuple     ):
          cls._set_token_in_kwargs(__A     )

          _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   cls.get_config_dict(__A,**__A     )

          # get the qformer config dict if we are loading from InstructBlipConfig
          if config_dict.get("model_type"     ) == "instructblip":
                _lowerCamelCase				:   Union[str, Any]								=   config_dict["qformer_config"]

          if "model_type" in config_dict and hasattr(cls,"model_type"     ) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'     )

          return cls.from_dict(__A,**__A     )


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'instructblip'
    lowerCAmelCase_				    =		True



    def __init__(	self      :       Any,__A      :       Tuple=None,__A      :       Dict=None,__A      :       Union[str, Any]=None,__A      :       Dict=3_2,**__A      :       Tuple     ):
          super().__init__(**__A     )

          if vision_config is None:
                _lowerCamelCase				:   Any								=   {}
                logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."     )

          if qformer_config is None:
                _lowerCamelCase				:   Optional[int]								=   {}
                logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."     )

          if text_config is None:
                _lowerCamelCase				:   int								=   {}
                logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."     )

          _lowerCamelCase				:   Dict								=   InstructBlipVisionConfig(**__A     )
          _lowerCamelCase				:   List[Any]								=   InstructBlipQFormerConfig(**__A     )
          _lowerCamelCase				:   List[str]								=   text_config["model_type"] if "model_type" in text_config else "opt"
          _lowerCamelCase				:   str								=   CONFIG_MAPPING[text_model_type](**__A     )

          _lowerCamelCase				:   Any								=   self.text_config.tie_word_embeddings
          _lowerCamelCase				:   Optional[int]								=   self.text_config.is_encoder_decoder

          _lowerCamelCase				:   Optional[int]								=   num_query_tokens
          _lowerCamelCase				:   Optional[int]								=   self.vision_config.hidden_size
          _lowerCamelCase				:   str								=   self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
          _lowerCamelCase				:   Optional[Any]								=   1.0
          _lowerCamelCase				:   int								=   0.02



    @classmethod
    def   lowerCamelCase_  (	cls      :       Optional[int],__A      :       InstructBlipVisionConfig,__A      :       InstructBlipQFormerConfig,__A      :       PretrainedConfig,**__A      :       Tuple,):
          return cls(
              vision_config=vision_config.to_dict(),qformer_config=qformer_config.to_dict(),text_config=text_config.to_dict(),**__A,)



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   copy.deepcopy(self.__dict__     )
          _lowerCamelCase				:   str								=   self.vision_config.to_dict()
          _lowerCamelCase				:   List[str]								=   self.qformer_config.to_dict()
          _lowerCamelCase				:   Tuple								=   self.text_config.to_dict()
          _lowerCamelCase				:   List[str]								=   self.__class__.model_type
          return output

'''simple docstring'''






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_       :   Optional[int]   =       logging.get_logger(__name__)







def 		A_       (	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)
      if "model" in sd.keys():
            _lowerCamelCase				:   Optional[Any]								=   torch.load(_lowerCAmelCase				,	map_location="cpu"							)["model"]

      # pop unnecessary weights
      _lowerCamelCase				:   Optional[Any]								=   [
          "decoder.version",
          "decoder.output_projection.weight",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Union[str, Any]								=   {
          "decoder.project_in_dim.weight": "decoder.project_in.weight",
          "decoder.project_out_dim.weight": "decoder.project_out.weight",
          "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
          "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
      }
      for old_key, new_key in keys_to_rename.items():
            if old_key in sd:
                  _lowerCamelCase				:   Optional[Any]								=   sd.pop(_lowerCAmelCase							)

      _lowerCamelCase				:   Any								=   list(sd.keys()							)
      for key in keys:
            if ".qkv_proj." in key:
                  _lowerCamelCase				:   Optional[Any]								=   sd[key]
                  # We split QKV in separate Q,K,V

                  _lowerCamelCase				:   int								=   key.replace(".qkv_proj."				,	".q_proj."							)
                  _lowerCamelCase				:   List[Any]								=   key.replace(".qkv_proj."				,	".k_proj."							)
                  _lowerCamelCase				:   Dict								=   key.replace(".qkv_proj."				,	".v_proj."							)

                  _lowerCamelCase				:   Any								=   value.shape[0]
                  assert depth % 3 == 0
                  # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                  # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                  _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   torch.split(_lowerCAmelCase				,	depth // 3				,	dim=0							)

                  _lowerCamelCase				:   str								=   q
                  _lowerCamelCase				:   Dict								=   k
                  _lowerCamelCase				:   int								=   v
                  del sd[key]

      return sd







@torch.no_grad()
def 		A_       (	_lowerCAmelCase   :    List[Any]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    List[str]=None							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   load_checkpoint(_lowerCAmelCase							)

      if config is not None:
            _lowerCamelCase				:   str								=   OPTConfig.from_pretrained(_lowerCAmelCase							)
      else:
            _lowerCamelCase				:   Any								=   OPTConfig()

      _lowerCamelCase				:   Optional[int]								=   OPTModel(_lowerCAmelCase							).half().eval()
      model.load_state_dict(_lowerCAmelCase							)

      # Check results
      Path(_lowerCAmelCase							).mkdir(exist_ok=_lowerCAmelCase							)
      model.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   List[str]   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--fairseq_path',
     type=str,
     help=(
         'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
         ' https://huggingface.co/models?other=opt_metasq'
     ),
 )
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
 UpperCAmelCase_       :   str   =       parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)