

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


lowercase_             =     logging.get_logger(__name__)

lowercase_             =     {
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""blip_2_vision_model"""




  def __init__(       self							,     lowerCAmelCase=1_408							,     lowerCAmelCase=6_144							,     lowerCAmelCase=39							,     lowerCAmelCase=16							,     lowerCAmelCase=224							,     lowerCAmelCase=14							,     lowerCAmelCase="gelu"							,     lowerCAmelCase=0.00001							,     lowerCAmelCase=0.0							,     lowerCAmelCase=1e-10							,     lowerCAmelCase=True							,     **lowerCAmelCase							,     )						->				Dict:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase      )

        _lowercase			 =hidden_size
        _lowercase			 =intermediate_size
        _lowercase			 =num_hidden_layers
        _lowercase			 =num_attention_heads
        _lowercase			 =patch_size
        _lowercase			 =image_size
        _lowercase			 =initializer_range
        _lowercase			 =attention_dropout
        _lowercase			 =layer_norm_eps
        _lowercase			 =hidden_act
        _lowercase			 =qkv_bias




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     **lowerCAmelCase      )						->				"PretrainedConfig":
        '''simple docstring'''






        cls._set_token_in_kwargs(lowerCAmelCase      )

        _lowercase , _lowercase			 =cls.get_config_dict(lowerCAmelCase							,     **lowerCAmelCase      )

        # get the vision config dict if we are loading from Blip2Config
        if config_dict.get('model_type'      ) == "blip-2":
              _lowercase			 =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(lowerCAmelCase							,     **lowerCAmelCase      )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""blip_2_qformer"""




  def __init__(       self							,     lowerCAmelCase=30_522							,     lowerCAmelCase=768							,     lowerCAmelCase=12							,     lowerCAmelCase=12							,     lowerCAmelCase=3_072							,     lowerCAmelCase="gelu"							,     lowerCAmelCase=0.1							,     lowerCAmelCase=0.1							,     lowerCAmelCase=512							,     lowerCAmelCase=0.02							,     lowerCAmelCase=1e-12							,     lowerCAmelCase=0							,     lowerCAmelCase="absolute"							,     lowerCAmelCase=2							,     lowerCAmelCase=1_408							,     **lowerCAmelCase							,     )						->				Union[str, Any]:
        '''simple docstring'''






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

        _lowercase			 =vocab_size
        _lowercase			 =hidden_size
        _lowercase			 =num_hidden_layers
        _lowercase			 =num_attention_heads
        _lowercase			 =hidden_act
        _lowercase			 =intermediate_size
        _lowercase			 =hidden_dropout_prob
        _lowercase			 =attention_probs_dropout_prob
        _lowercase			 =max_position_embeddings
        _lowercase			 =initializer_range
        _lowercase			 =layer_norm_eps
        _lowercase			 =position_embedding_type
        _lowercase			 =cross_attention_frequency
        _lowercase			 =encoder_hidden_size




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     **lowerCAmelCase      )						->				"PretrainedConfig":
        '''simple docstring'''






        cls._set_token_in_kwargs(lowerCAmelCase      )

        _lowercase , _lowercase			 =cls.get_config_dict(lowerCAmelCase							,     **lowerCAmelCase      )

        # get the qformer config dict if we are loading from Blip2Config
        if config_dict.get('model_type'      ) == "blip-2":
              _lowercase			 =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(lowerCAmelCase							,     **lowerCAmelCase      )





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""blip-2"""
  _a		    =						True




  def __init__(       self							,     lowerCAmelCase=None							,     lowerCAmelCase=None							,     lowerCAmelCase=None							,     lowerCAmelCase=32							,     **lowerCAmelCase      )						->				int:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase      )

        if vision_config is None:
              _lowercase			 ={}
              logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.'      )

        if qformer_config is None:
              _lowercase			 ={}
              logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.'      )

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

        _lowercase			 =BlipaVisionConfig(**lowerCAmelCase      )
        _lowercase			 =BlipaQFormerConfig(**lowerCAmelCase      )
        _lowercase			 =text_config['model_type'] if 'model_type' in text_config else 'opt'
        _lowercase			 =CONFIG_MAPPING[text_model_type](**lowerCAmelCase      )

        _lowercase			 =self.text_config.tie_word_embeddings
        _lowercase			 =self.text_config.is_encoder_decoder

        _lowercase			 =num_query_tokens
        _lowercase			 =self.vision_config.hidden_size
        _lowercase			 =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        _lowercase			 =1.0
        _lowercase			 =0.02




  @classmethod
  def 						A__  (       cls							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     **lowerCAmelCase							,     )						->				List[str]:
        '''simple docstring'''






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




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =copy.deepcopy(self.__dict__      )
        _lowercase			 =self.vision_config.to_dict()
        _lowercase			 =self.qformer_config.to_dict()
        _lowercase			 =self.text_config.to_dict()
        _lowercase			 =self.__class__.model_type
        return output





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =True
            # hparam_utils.py hparams
            _lowercase			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =TapasModel(config=A__  )
      else:
            raise ValueError(F'''Task {task} not supported.'''  )

      print(F'''Building PyTorch model from configuration: {config}'''  )
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(A__   ,     A__   ,     A__  )

      # Save pytorch-model (weights and configuration)
      print(F'''Save PyTorch model to {pytorch_dump_path}'''  )
      model.save_pretrained(A__  )

      # Save tokenizer files
      print(F'''Save tokenizer files to {pytorch_dump_path}'''  )
      _lowercase			 =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'   ,     model_max_length=512  )
      tokenizer.save_pretrained(A__  )

      print('Used relative position embeddings:'   ,     model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
  )
  parser.add_argument(
      '--reset_position_index_per_cell',
      default=False,
      action='store_true',
      help='Whether to use relative position embeddings or not. Defaults to True.',
  )
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--tapas_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained TAPAS 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.'
  )
  lowercase_             =     parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )


