
"""simple docstring"""






import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Dict   	=checkpoints.load_tax_checkpoint(__lowercase	)
 UpperCamelCase_     :					List[str]   	=flatten_dict(__lowercase	)
 return flax_params




def       A_  (				__lowercase	):
 UpperCamelCase_     :					Union[str, Any]   	={}

 UpperCamelCase_     :					Optional[int]   	={
     'token_embedder': 'embeddings',
     'encoder_norm': 'layernorm',
     'kernel': 'weight',
     '.out': '.output',
     'scale': 'weight',
     'embedders_0.pos_embedding': 'row_embedder.weight',
     'embedders_1.pos_embedding': 'column_embedder.weight',
 }

 UpperCamelCase_     :					Any   	={
     'query': 'attention.query',
     'key': 'attention.key',
     'value': 'attention.value',
     'output.dense': 'output',
     'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
     'pre_self_attention_layer_norm': 'self_attention.layer_norm',
     'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
     'mlp.': 'mlp.DenseReluDense.',
     'pre_mlp_layer_norm': 'mlp.layer_norm',
     'self_attention.o': 'self_attention.attention.o',
     'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
     'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
     'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
     'decoder.logits_dense.weight': 'decoder.lm_head.weight',
 }

 for key in flax_dict.keys():
  if "target" in key:
   # remove the first prefix from the key
   UpperCamelCase_     :					str   	='.'.join(key[1:]	)

   # rename the key
   for old, new in CONVERSION_MAPPING.items():
    UpperCamelCase_     :					int   	=new_key.replace(__lowercase						,	__lowercase	)

   if "decoder" in new_key:
    for old, new in DECODER_CONVERSION_MAPPING.items():
     UpperCamelCase_     :					Union[str, Any]   	=new_key.replace(__lowercase						,	__lowercase	)

   if "layers" in new_key and "decoder" not in new_key:
    # use regex to replace the layer number
    UpperCamelCase_     :					Optional[Any]   	=re.sub(r'layers_(\d+)'						,	r'layer.\1'						,	__lowercase	)
    UpperCamelCase_     :					Optional[int]   	=new_key.replace('encoder'						,	'encoder.encoder'	)

   elif "layers" in new_key and "decoder" in new_key:
    # use regex to replace the layer number
    UpperCamelCase_     :					Dict   	=re.sub(r'layers_(\d+)'						,	r'layer.\1'						,	__lowercase	)

   UpperCamelCase_     :					Optional[int]   	=flax_dict[key]

 UpperCamelCase_     :					Tuple   	={}
 # convert converted_dict into torch format
 for key in converted_dict.keys():
  if ("embed_tokens" not in key) and ("embedder" not in key):
   UpperCamelCase_     :					Optional[Any]   	=torch.from_numpy(converted_dict[key].T	)
  else:
   UpperCamelCase_     :					List[Any]   	=torch.from_numpy(converted_dict[key]	)

 return converted_torch_dict




def       A_  (				__lowercase						,	__lowercase						,	__lowercase=False						,	__lowercase=False	):
 UpperCamelCase_     :					int   	=get_flax_param(__lowercase	)

 if not use_large:
  UpperCamelCase_     :					str   	=PixaStructVisionConfig()
  UpperCamelCase_     :					Dict   	=PixaStructTextConfig()
 else:
  UpperCamelCase_     :					Dict   	=PixaStructVisionConfig(
      hidden_size=15_36						,	d_ff=39_68						,	num_attention_heads=24						,	num_hidden_layers=18	)
  UpperCamelCase_     :					Optional[int]   	=PixaStructTextConfig(hidden_size=15_36						,	d_ff=39_68						,	num_heads=24						,	num_layers=18	)
 UpperCamelCase_     :					int   	=PixaStructConfig(
     vision_config=encoder_config.to_dict()						,	text_config=decoder_config.to_dict()						,	is_vqa=__lowercase	)

 UpperCamelCase_     :					Any   	=PixaStructForConditionalGeneration(__lowercase	)

 UpperCamelCase_     :					Dict   	=rename_and_convert_flax_params(__lowercase	)
 model.load_state_dict(__lowercase	)

 UpperCamelCase_     :					List[Any]   	=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'	)
 UpperCamelCase_     :					Dict   	=PixaStructImageProcessor()
 UpperCamelCase_     :					List[str]   	=PixaStructProcessor(image_processor=__lowercase						,	tokenizer=__lowercase	)

 if use_large:
  UpperCamelCase_     :					str   	=40_96

 UpperCamelCase_     :					List[str]   	=True

 # mkdir if needed
 os.makedirs(__lowercase						,	exist_ok=__lowercase	)

 model.save_pretrained(__lowercase	)
 processor.save_pretrained(__lowercase	)

 print('Model saved in {}'.format(__lowercase	)	)


if __name__ == "__main__":
 __SCREAMING_SNAKE_CASE							=     argparse.ArgumentParser()
 parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--use_large', action='store_true', help='Use large model.')
 parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
 __SCREAMING_SNAKE_CASE							=     parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )


"""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


__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__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_vision_model'''



     def __init__(							self :int      ,  _lowerCamelCase :Optional[int]=1_408      ,  _lowerCamelCase :int=6_144      ,  _lowerCamelCase :str=39      ,  _lowerCamelCase :Tuple=16      ,  _lowerCamelCase :List[Any]=224      ,  _lowerCamelCase :int=14      ,  _lowerCamelCase :int="gelu"      ,  _lowerCamelCase :Optional[Any]=1E-6      ,  _lowerCamelCase :str=0.0      ,  _lowerCamelCase :Optional[Any]=1E-1_0      ,  _lowerCamelCase :str=True      ,  **_lowerCamelCase :Optional[Any]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

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




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the vision config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					Tuple   	=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 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_qformer'''



     def __init__(							self :Dict      ,  _lowerCamelCase :List[Any]=30_522      ,  _lowerCamelCase :Optional[int]=768      ,  _lowerCamelCase :Tuple=12      ,  _lowerCamelCase :List[str]=12      ,  _lowerCamelCase :int=3_072      ,  _lowerCamelCase :Tuple="gelu"      ,  _lowerCamelCase :Dict=0.1      ,  _lowerCamelCase :List[Any]=0.1      ,  _lowerCamelCase :Optional[int]=512      ,  _lowerCamelCase :List[Any]=0.02      ,  _lowerCamelCase :int=1E-1_2      ,  _lowerCamelCase :str=0      ,  _lowerCamelCase :Dict="absolute"      ,  _lowerCamelCase :Optional[int]=2      ,  _lowerCamelCase :Union[str, Any]=1_408      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



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

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




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the qformer config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					List[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(_lowerCamelCase      ,  **_lowerCamelCase   )





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip'''
     UpperCAmelCase__         =      True



     def __init__(							self :str      ,  _lowerCamelCase :Optional[Any]=None      ,  _lowerCamelCase :List[str]=None      ,  _lowerCamelCase :List[Any]=None      ,  _lowerCamelCase :Union[str, Any]=32      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      if vision_config is None:
       UpperCamelCase_     :					Optional[int]   	={}
       logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'   )

      if qformer_config is None:
       UpperCamelCase_     :					Union[str, Any]   	={}
       logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'   )

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

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

      UpperCamelCase_     :					Tuple   	=self.text_config.tie_word_embeddings
      UpperCamelCase_     :					Any   	=self.text_config.is_encoder_decoder

      UpperCamelCase_     :					List[Any]   	=num_query_tokens
      UpperCamelCase_     :					Tuple   	=self.vision_config.hidden_size
      UpperCamelCase_     :					Tuple   	=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      UpperCamelCase_     :					int   	=1.0
      UpperCamelCase_     :					List[str]   	=0.02



     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :InstructBlipVisionConfig      ,  _lowerCamelCase :InstructBlipQFormerConfig      ,  _lowerCamelCase :PretrainedConfig      ,  **_lowerCamelCase :Dict      ,  ):







      '''simple docstring'''



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




     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=copy.deepcopy(self.__dict__   )
      UpperCamelCase_     :					Dict   	=self.vision_config.to_dict()
      UpperCamelCase_     :					Tuple   	=self.qformer_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.text_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.__class__.model_type
      return output
