



import copy
import os
from typing import Union

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


A_		:     Tuple        =     logging.get_logger(__name__)

A_		:     Any        =     {
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''pix2struct_text_model'''
   _UpperCAmelCase     	=			['''past_key_values''']
   _UpperCAmelCase     	=			{
       '''hidden_size''': '''hidden_size''',
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }

   def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       int=5_0244							,   __lowerCAmelCase			:       str=768							,   __lowerCAmelCase			:       List[Any]=64							,   __lowerCAmelCase			:       Union[str, Any]=2048							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Optional[int]=32							,   __lowerCAmelCase			:       str=128							,   __lowerCAmelCase			:       int=0.1							,   __lowerCAmelCase			:       int=1E-6							,   __lowerCAmelCase			:       str=1.0							,   __lowerCAmelCase			:       Optional[int]="gelu_new"							,   __lowerCAmelCase			:       List[str]=0							,   __lowerCAmelCase			:       Union[str, Any]=False							,   __lowerCAmelCase			:       Optional[int]=0							,   __lowerCAmelCase			:       List[Any]=1							,   __lowerCAmelCase			:       Dict=False							,   __lowerCAmelCase			:       int=True							,   **__lowerCAmelCase			:       List[str]							,   )       ->				Any:


    """simple docstring"""






    a						=		vocab_size
    a						=		hidden_size
    a						=		d_kv
    a						=		d_ff
    a						=		num_layers
    a						=		num_heads
    a						=		relative_attention_num_buckets
    a						=		relative_attention_max_distance
    a						=		dropout_rate
    a						=		layer_norm_epsilon
    a						=		initializer_factor
    a						=		use_cache

    a						=		eos_token_id
    a						=		decoder_start_token_id

    # for backwards compatibility
    a						=		dense_act_fn

    super().__init__(
        pad_token_id=__lowerCAmelCase							,   eos_token_id=__lowerCAmelCase							,   decoder_start_token_id=__lowerCAmelCase							,   tie_word_embeddings=__lowerCAmelCase							,   is_decoder=__lowerCAmelCase							,   **__lowerCAmelCase							,   )




   @classmethod
   def 		A							(      cls			:       Dict							,   __lowerCAmelCase			:       Union[str, os.PathLike]							,   **__lowerCAmelCase			:       Dict							)       ->				"PretrainedConfig":


    """simple docstring"""






    cls._set_token_in_kwargs(__lowerCAmelCase							)

    a		,  a						=		cls.get_config_dict(__lowerCAmelCase							,   **__lowerCAmelCase							)

    # get the text config dict if we are loading from Pix2StructConfig
    if config_dict.get("model_type"							) == "pix2struct":
     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(__lowerCAmelCase							,   **__lowerCAmelCase							)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''pix2struct_vision_model'''

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       List[Any]=768							,   __lowerCAmelCase			:       str=768							,   __lowerCAmelCase			:       Optional[int]=2048							,   __lowerCAmelCase			:       int=64							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       str=12							,   __lowerCAmelCase			:       Optional[Any]="gelu_new"							,   __lowerCAmelCase			:       Any=1E-6							,   __lowerCAmelCase			:       Optional[Any]=0.0							,   __lowerCAmelCase			:       Tuple=0.0							,   __lowerCAmelCase			:       int=1E-10							,   __lowerCAmelCase			:       int=1.0							,   __lowerCAmelCase			:       Optional[Any]=4096							,   __lowerCAmelCase			:       str=32							,   __lowerCAmelCase			:       Any=128							,   **__lowerCAmelCase			:       Optional[Any]							,   )       ->				Tuple:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase							)

    a						=		hidden_size
    a						=		patch_embed_hidden_size
    a						=		d_ff
    a						=		dropout_rate
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		initializer_range
    a						=		initializer_factor
    a						=		attention_dropout
    a						=		layer_norm_eps
    a						=		dense_act_fn
    a						=		seq_len
    a						=		relative_attention_num_buckets
    a						=		relative_attention_max_distance
    a						=		d_kv




   @classmethod
   def 		A							(      cls			:       List[str]							,   __lowerCAmelCase			:       Union[str, os.PathLike]							,   **__lowerCAmelCase			:       str							)       ->				"PretrainedConfig":


    """simple docstring"""






    cls._set_token_in_kwargs(__lowerCAmelCase							)

    a		,  a						=		cls.get_config_dict(__lowerCAmelCase							,   **__lowerCAmelCase							)

    # get the vision config dict if we are loading from Pix2StructConfig
    if config_dict.get("model_type"							) == "pix2struct":
     a						=		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 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''pix2struct'''
   _UpperCAmelCase     	=			True

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Union[str, Any]=None							,   __lowerCAmelCase			:       Union[str, Any]=None							,   __lowerCAmelCase			:       Optional[Any]=1.0							,   __lowerCAmelCase			:       str=0.0_2							,   __lowerCAmelCase			:       Optional[Any]=False							,   __lowerCAmelCase			:       Dict=False							,   __lowerCAmelCase			:       Tuple=True							,   **__lowerCAmelCase			:       Any							,   )       ->				List[Any]:


    """simple docstring"""






    super().__init__(tie_word_embeddings=__lowerCAmelCase							,   is_encoder_decoder=__lowerCAmelCase							,   **__lowerCAmelCase							)

    if text_config is None:
     a						=		{}
     logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."							)

    if vision_config is None:
     a						=		{}
     logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."							)

    a						=		PixaStructTextConfig(**__lowerCAmelCase							)
    a						=		PixaStructVisionConfig(**__lowerCAmelCase							)

    a						=		self.text_config.decoder_start_token_id
    a						=		self.text_config.pad_token_id
    a						=		self.text_config.eos_token_id

    a						=		initializer_factor
    a						=		initializer_range

    a						=		self.initializer_range
    a						=		self.initializer_range

    a						=		is_vqa

   @classmethod
   def 		A							(      cls			:       Optional[Any]							,   __lowerCAmelCase			:       PixaStructTextConfig							,   __lowerCAmelCase			:       PixaStructVisionConfig							,   **__lowerCAmelCase			:       List[Any]							)       ->				Optional[Any]:


    """simple docstring"""






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




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

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	UpperCAmelCase__      (      UpperCAmelCase__ :Optional[int] ,					UpperCAmelCase__ :List[str] ,					UpperCAmelCase__ :Any					):






 '''simple docstring'''

 a						=		TaConfig.from_json_file(UpperCAmelCase__					)
 print(F"""Building PyTorch model from configuration: {config}"""					)
 a						=		TaForConditionalGeneration(UpperCAmelCase__					)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)

 # Save pytorch-model
 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 model.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     A_		:     Union[str, Any]        =     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(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help=(
             '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
         ),
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     A_		:     Tuple        =     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

