






import copy
import os
from typing import Union

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


__a      :       List[str]				  =   logging.get_logger(__name__)

__a      :       Optional[int]				  =   {
    "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json",
    "BridgeTower/bridgetower-base-itm-mlm": (
        "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"
    ),
}


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"bridgetower_vision_model"




     def __init__(				self       :			Any ,	UpperCamelCase_       :			Tuple=768 ,	UpperCamelCase_       :			List[str]=12 ,	UpperCamelCase_       :			Optional[int]=3 ,	UpperCamelCase_       :			Optional[Any]=16 ,	UpperCamelCase_       :			Union[str, Any]=288 ,	UpperCamelCase_       :			Any=1 ,	UpperCamelCase_       :			List[str]=1e-05 ,	UpperCamelCase_       :			Any=False ,	UpperCamelCase_       :			List[Any]=True ,	UpperCamelCase_       :			Union[str, Any]=False ,	**UpperCamelCase_       :			int ,	):


         """simple docstring"""



         super().__init__(**UpperCamelCase_				)
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_channels
         __A         =	patch_size
         __A         =	image_size
         __A         =	initializer_factor
         __A         =	layer_norm_eps
         __A         =	stop_gradient
         __A         =	share_layernorm
         __A         =	remove_last_layer





     @classmethod
     def 		lowerCAmelCase_     (				cls       :			str ,	UpperCamelCase_       :			Union[str, os.PathLike] ,	**UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         __A    ,	__A         =	cls.get_config_dict(UpperCamelCase_ ,	**UpperCamelCase_				)

         if config_dict.get("""model_type"""				) == "bridgetower":
             __A         =	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(UpperCamelCase_ ,	**UpperCamelCase_				)


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"bridgetower_text_model"




     def __init__(				self       :			int ,	UpperCamelCase_       :			int=50_265 ,	UpperCamelCase_       :			int=768 ,	UpperCamelCase_       :			Optional[Any]=12 ,	UpperCamelCase_       :			Union[str, Any]=12 ,	UpperCamelCase_       :			List[Any]=1 ,	UpperCamelCase_       :			Any=3_072 ,	UpperCamelCase_       :			Dict="gelu" ,	UpperCamelCase_       :			Dict=0.1 ,	UpperCamelCase_       :			Dict=0.1 ,	UpperCamelCase_       :			Dict=514 ,	UpperCamelCase_       :			List[str]=1 ,	UpperCamelCase_       :			Any=1e-05 ,	UpperCamelCase_       :			Any=1 ,	UpperCamelCase_       :			Dict=0 ,	UpperCamelCase_       :			List[Any]=2 ,	UpperCamelCase_       :			Tuple="absolute" ,	UpperCamelCase_       :			List[Any]=True ,	**UpperCamelCase_       :			List[Any] ,	):


         """simple docstring"""



         super().__init__(**UpperCamelCase_				)

         __A         =	vocab_size
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	hidden_act
         __A         =	initializer_factor
         __A         =	intermediate_size
         __A         =	hidden_dropout_prob
         __A         =	attention_probs_dropout_prob
         __A         =	max_position_embeddings
         __A         =	type_vocab_size
         __A         =	layer_norm_eps
         __A         =	position_embedding_type
         __A         =	use_cache
         __A         =	pad_token_id
         __A         =	bos_token_id
         __A         =	eos_token_id





     @classmethod
     def 		lowerCAmelCase_     (				cls       :			List[Any] ,	UpperCamelCase_       :			Union[str, os.PathLike] ,	**UpperCamelCase_       :			Any				):


         """simple docstring"""



         __A    ,	__A         =	cls.get_config_dict(UpperCamelCase_ ,	**UpperCamelCase_				)

         if config_dict.get("""model_type"""				) == "bridgetower":
             __A         =	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(UpperCamelCase_ ,	**UpperCamelCase_				)


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"bridgetower"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Optional[int]=True ,	UpperCamelCase_       :			Any="gelu" ,	UpperCamelCase_       :			Optional[int]=768 ,	UpperCamelCase_       :			Any=1 ,	UpperCamelCase_       :			Optional[int]=1e-05 ,	UpperCamelCase_       :			List[str]=False ,	UpperCamelCase_       :			int="add" ,	UpperCamelCase_       :			Optional[Any]=12 ,	UpperCamelCase_       :			Tuple=6 ,	UpperCamelCase_       :			List[str]=False ,	UpperCamelCase_       :			Tuple=False ,	UpperCamelCase_       :			Optional[int]=None ,	UpperCamelCase_       :			Optional[Any]=None ,	**UpperCamelCase_       :			List[str] ,	):


         """simple docstring"""



         __A         =	kwargs.pop("""text_config_dict""" ,	UpperCamelCase_				)
         __A         =	kwargs.pop("""vision_config_dict""" ,	UpperCamelCase_				)

         super().__init__(**UpperCamelCase_				)
         __A         =	share_cross_modal_transformer_layers
         __A         =	hidden_act
         __A         =	hidden_size
         __A         =	initializer_factor
         __A         =	layer_norm_eps
         __A         =	share_link_tower_layers
         __A         =	link_tower_type
         __A         =	num_attention_heads
         __A         =	num_hidden_layers
         __A         =	tie_word_embeddings
         __A         =	init_layernorm_from_vision_encoder

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

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

         __A         =	BridgeTowerTextConfig(**UpperCamelCase_				)
         __A         =	BridgeTowerVisionConfig(**UpperCamelCase_				)




     @classmethod
     def 		lowerCAmelCase_     (				cls       :			Any ,	UpperCamelCase_       :			BridgeTowerTextConfig ,	UpperCamelCase_       :			BridgeTowerVisionConfig ,	**UpperCamelCase_       :			int				):


         """simple docstring"""



         return cls(text_config=text_config.to_dict() ,	vision_config=vision_config.to_dict() ,	**UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



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












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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	checkpoints.load_tax_checkpoint(__lowercase    )
    __A         =	flatten_dict(__lowercase    )
    return flax_params



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      List[str]:





    """simple docstring"""

    __A         =	{}

    __A         =	{
        """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""",
    }

    __A         =	{
        """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
            __A         =	""".""".join(key[1:]    )

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

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )
                __A         =	new_key.replace("""encoder"""			,				"""encoder.encoder"""    )

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

            __A         =	flax_dict[key]

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

    return converted_torch_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]			,				__lowercase   :			Dict=False			,				__lowercase   :			List[str]=False    )						->      Any:





    """simple docstring"""

    __A         =	get_flax_param(__lowercase    )

    if not use_large:
        __A         =	PixaStructVisionConfig()
        __A         =	PixaStructTextConfig()
    else:
        __A         =	PixaStructVisionConfig(
            hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_attention_heads=2_4			,				num_hidden_layers=1_8    )
        __A         =	PixaStructTextConfig(hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_heads=2_4			,				num_layers=1_8    )
    __A         =	PixaStructConfig(
        vision_config=encoder_config.to_dict()			,				text_config=decoder_config.to_dict()			,				is_vqa=__lowercase    )

    __A         =	PixaStructForConditionalGeneration(__lowercase    )

    __A         =	rename_and_convert_flax_params(__lowercase    )
    model.load_state_dict(__lowercase    )

    __A         =	AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
    __A         =	PixaStructImageProcessor()
    __A         =	PixaStructProcessor(image_processor=__lowercase			,				tokenizer=__lowercase    )

    if use_large:
        __A         =	4_0_9_6

    __A         =	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__":
  __a      :       Union[str, Any]				  =   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.")
  __a      :       Dict				  =   parser.parse_args()

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




