






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


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE 							=						{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Tuple =					"instructblip_vision_model"


 def __init__(	self							,  _A=1408							,  _A=6144							,  _A=39							,  _A=16							,  _A=224							,  _A=14							,  _A="gelu"							,  _A=1e-6							,  _A=0.0							,  _A=1e-10							,  _A=True							,  **_A							,  ) ->		List[str]:

     """simple docstring"""



     super().__init__(**_A)

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


 @classmethod
 def  snake_case__				(	cls							,  _A							,  **_A) ->		"PretrainedConfig":

     """simple docstring"""



     cls._set_token_in_kwargs(_A)

     _UpperCAmelCase				:      List[Any]			  =				cls.get_config_dict(_A							,  **_A)

     # get the vision config dict if we are loading from InstructBlipConfig
     if config_dict.get('''model_type''') == "instructblip":
         _UpperCAmelCase				:      Any			  =				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 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			List[Any] =					"instructblip_qformer"


 def __init__(	self							,  _A=30522							,  _A=768							,  _A=12							,  _A=12							,  _A=3072							,  _A="gelu"							,  _A=0.1							,  _A=0.1							,  _A=512							,  _A=0.02							,  _A=1e-12							,  _A=0							,  _A="absolute"							,  _A=2							,  _A=1408							,  **_A							,  ) ->		List[Any]:

     """simple docstring"""



     super().__init__(pad_token_id=_A							,  **_A)

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


 @classmethod
 def  snake_case__				(	cls							,  _A							,  **_A) ->		"PretrainedConfig":

     """simple docstring"""



     cls._set_token_in_kwargs(_A)

     _UpperCAmelCase				:      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":
         _UpperCAmelCase				:      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 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			List[Any] =					"instructblip"
 _SCREAMING_SNAKE_CASE							:			Tuple =					True


 def __init__(	self							,  _A=None							,  _A=None							,  _A=None							,  _A=32							,  **_A) ->		str:

     """simple docstring"""



     super().__init__(**_A)

     if vision_config is None:
         _UpperCAmelCase				:      Dict			  =				{}
         logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.''')

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

     if text_config is None:
         _UpperCAmelCase				:      Any			  =				{}
         logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''')

     _UpperCAmelCase				:      Any			  =				InstructBlipVisionConfig(**_A)
     _UpperCAmelCase				:      List[Any]			  =				InstructBlipQFormerConfig(**_A)
     _UpperCAmelCase				:      Optional[int]			  =				text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
     _UpperCAmelCase				:      str			  =				CONFIG_MAPPING[text_model_type](**_A)

     _UpperCAmelCase				:      Dict			  =				self.text_config.tie_word_embeddings
     _UpperCAmelCase				:      Union[str, Any]			  =				self.text_config.is_encoder_decoder

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


 @classmethod
 def  snake_case__				(	cls							,  _A							,  _A							,  _A							,  **_A							,  ) ->		List[str]:

     """simple docstring"""



     return cls(
         vision_config=vision_config.to_dict()							,  qformer_config=qformer_config.to_dict()							,  text_config=text_config.to_dict()							,  **_A							,  )


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				copy.deepcopy(self.__dict__)
     _UpperCAmelCase				:      List[str]			  =				self.vision_config.to_dict()
     _UpperCAmelCase				:      Optional[int]			  =				self.qformer_config.to_dict()
     _UpperCAmelCase				:      List[str]			  =				self.text_config.to_dict()
     _UpperCAmelCase				:      int			  =				self.__class__.model_type
     return output










import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




