




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    'Salesforce/blip-vqa-base': 'https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json',
    'Salesforce/blip-vqa-capfit-large': (
        'https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-base': (
        'https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json'
    ),
    'Salesforce/blip-image-captioning-large': (
        'https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json'
    ),
    'Salesforce/blip-itm-base-coco': 'https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json',
    'Salesforce/blip-itm-large-coco': 'https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json',
    'Salesforce/blip-itm-base-flikr': 'https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json',
    'Salesforce/blip-itm-large-flikr': (
        'https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json'
    ),
}





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = "blip_text_model"


    def __init__(			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=30_524  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=768  ,      SCREAMING_SNAKE_CASE__	:	Any=768  ,      SCREAMING_SNAKE_CASE__	:	Any=3_072  ,      SCREAMING_SNAKE_CASE__	:	Dict=768  ,      SCREAMING_SNAKE_CASE__	:	int=12  ,      SCREAMING_SNAKE_CASE__	:	int=8  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=512  ,      SCREAMING_SNAKE_CASE__	:	Dict="gelu"  ,      SCREAMING_SNAKE_CASE__	:	Dict=1e-1_2  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=0.0  ,      SCREAMING_SNAKE_CASE__	:	Tuple=0.0  ,      SCREAMING_SNAKE_CASE__	:	str=0.02  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=30_522  ,      SCREAMING_SNAKE_CASE__	:	str=2  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=0  ,      SCREAMING_SNAKE_CASE__	:	int=102  ,      SCREAMING_SNAKE_CASE__	:	int=True  ,      SCREAMING_SNAKE_CASE__	:	int=True  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			Dict:
         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__  ,      bos_token_id=SCREAMING_SNAKE_CASE__  ,      eos_token_id=SCREAMING_SNAKE_CASE__  ,      sep_token_id=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  ,      )

         lowerCAmelCase__            =					vocab_size
         lowerCAmelCase__            =					hidden_size
         lowerCAmelCase__            =					encoder_hidden_size
         lowerCAmelCase__            =					intermediate_size
         lowerCAmelCase__            =					projection_dim
         lowerCAmelCase__            =					hidden_dropout_prob
         lowerCAmelCase__            =					num_hidden_layers
         lowerCAmelCase__            =					num_attention_heads
         lowerCAmelCase__            =					max_position_embeddings
         lowerCAmelCase__            =					layer_norm_eps
         lowerCAmelCase__            =					hidden_act
         lowerCAmelCase__            =					initializer_range
         lowerCAmelCase__            =					attention_probs_dropout_prob
         lowerCAmelCase__            =					is_decoder
         lowerCAmelCase__            =					use_cache


    @classmethod
    def   a (			cls	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Union[str, os.PathLike]  ,      **SCREAMING_SNAKE_CASE__	:	Any  ) ->			"PretrainedConfig":
         cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__      ,						lowerCAmelCase__            =					cls.get_config_dict(SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )

         # get the text config dict if we are loading from BlipConfig
         if config_dict.get("model_type"  ) == "blip":
              lowerCAmelCase__            =					config_dict["text_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(SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = "blip_vision_model"


    def __init__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	Dict=768  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=3_072  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=512  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=12  ,      SCREAMING_SNAKE_CASE__	:	Any=12  ,      SCREAMING_SNAKE_CASE__	:	str=384  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=16  ,      SCREAMING_SNAKE_CASE__	:	List[str]="gelu"  ,      SCREAMING_SNAKE_CASE__	:	Dict=1e-5  ,      SCREAMING_SNAKE_CASE__	:	Any=0.0  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=1e-1_0  ,      **SCREAMING_SNAKE_CASE__	:	List[str]  ,      ) ->			int:
         super().__init__(**SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					hidden_size
         lowerCAmelCase__            =					intermediate_size
         lowerCAmelCase__            =					projection_dim
         lowerCAmelCase__            =					num_hidden_layers
         lowerCAmelCase__            =					num_attention_heads
         lowerCAmelCase__            =					patch_size
         lowerCAmelCase__            =					image_size
         lowerCAmelCase__            =					initializer_range
         lowerCAmelCase__            =					attention_dropout
         lowerCAmelCase__            =					layer_norm_eps
         lowerCAmelCase__            =					hidden_act


    @classmethod
    def   a (			cls	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, os.PathLike]  ,      **SCREAMING_SNAKE_CASE__	:	str  ) ->			"PretrainedConfig":
         cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__      ,						lowerCAmelCase__            =					cls.get_config_dict(SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )

         # get the vision config dict if we are loading from BlipConfig
         if config_dict.get("model_type"  ) == "blip":
              lowerCAmelCase__            =					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(SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = "blip"
    snake_case__   = True


    def __init__(			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	str=None  ,      SCREAMING_SNAKE_CASE__	:	Tuple=None  ,      SCREAMING_SNAKE_CASE__	:	Any=512  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=2.6_592  ,      SCREAMING_SNAKE_CASE__	:	Tuple=256  ,      **SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      ) ->			List[str]:
         super().__init__(**SCREAMING_SNAKE_CASE__  )

         if text_config is None:
              lowerCAmelCase__            =					{}
              logger.info("`text_config` is `None`. Initializing the `BlipTextConfig` with default values."  )

         if vision_config is None:
              lowerCAmelCase__            =					{}
              logger.info("`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values."  )

         lowerCAmelCase__            =					BlipTextConfig(**SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					BlipVisionConfig(**SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					self.vision_config.hidden_size

         lowerCAmelCase__            =					projection_dim
         lowerCAmelCase__            =					logit_scale_init_value
         lowerCAmelCase__            =					1.0
         lowerCAmelCase__            =					0.02
         lowerCAmelCase__            =					image_text_hidden_size


    @classmethod
    def   a (			cls	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	BlipTextConfig  ,      SCREAMING_SNAKE_CASE__	:	BlipVisionConfig  ,      **SCREAMING_SNAKE_CASE__	:	str  ) ->			Union[str, Any]:
         return cls(text_config=text_config.to_dict()  ,      vision_config=vision_config.to_dict()  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Tuple  ) ->			Optional[int]:
         lowerCAmelCase__            =					copy.deepcopy(self.__dict__  )
         lowerCAmelCase__            =					self.text_config.to_dict()
         lowerCAmelCase__            =					self.vision_config.to_dict()
         lowerCAmelCase__            =					self.__class__.model_type
         return output








'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

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

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


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   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."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)