"""simple docstring"""




import copy
import os
from typing import Union

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


_a   :	str 					=     logging.get_logger(__name__)

_a   :	Tuple 					=     {
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}
class 		__A							(				SCREAMING_SNAKE_CASE_	):
					_UpperCamelCase							:    Tuple	      =	"pix2struct_text_model"
					_UpperCamelCase							:    Optional[Any]	      =	["past_key_values"]
					_UpperCamelCase							:    Optional[int]	      =	{
					    "hidden_size": "hidden_size",
					    "num_attention_heads": "num_heads",
					    "num_hidden_layers": "num_layers",
					}






					def __init__(	self       ,  a__=50244       ,  a__=768       ,  a__=64       ,  a__=2048       ,  a__=12       ,  a__=12       ,  a__=32       ,  a__=128       ,  a__=0.1       ,  a__=1e-6       ,  a__=1.0       ,  a__="gelu_new"       ,  a__=0       ,  a__=False       ,  a__=0       ,  a__=1       ,  a__=False       ,  a__=True       ,  **a__       ,  ):
							_lowerCAmelCase		: str						    =  vocab_size
							_lowerCAmelCase		: str						    =  hidden_size
							_lowerCAmelCase		: str						    =  d_kv
							_lowerCAmelCase		: Optional[Any]						    =  d_ff
							_lowerCAmelCase		: Optional[Any]						    =  num_layers
							_lowerCAmelCase		: Tuple						    =  num_heads
							_lowerCAmelCase		: Union[str, Any]						    =  relative_attention_num_buckets
							_lowerCAmelCase		: Union[str, Any]						    =  relative_attention_max_distance
							_lowerCAmelCase		: str						    =  dropout_rate
							_lowerCAmelCase		: Optional[int]						    =  layer_norm_epsilon
							_lowerCAmelCase		: Tuple						    =  initializer_factor
							_lowerCAmelCase		: str						    =  use_cache

							_lowerCAmelCase		: List[Any]						    =  eos_token_id
							_lowerCAmelCase		: str						    =  decoder_start_token_id

							# for backwards compatibility
							_lowerCAmelCase		: Tuple						    =  dense_act_fn

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






					@classmethod
					def 					__A						(	cls       ,  a__       ,  **a__						):
							cls._set_token_in_kwargs(a__						)

							_lowerCAmelCase		: Any						    =  cls.get_config_dict(a__       ,  **a__						)

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







class 		__A							(				SCREAMING_SNAKE_CASE_	):
					_UpperCamelCase							:    List[str]	      =	"pix2struct_vision_model"






					def __init__(	self       ,  a__=768       ,  a__=768       ,  a__=2048       ,  a__=64       ,  a__=12       ,  a__=12       ,  a__="gelu_new"       ,  a__=1e-6       ,  a__=0.0       ,  a__=0.0       ,  a__=1e-10       ,  a__=1.0       ,  a__=4096       ,  a__=32       ,  a__=128       ,  **a__       ,  ):
							super().__init__(**a__						)

							_lowerCAmelCase		: List[str]						    =  hidden_size
							_lowerCAmelCase		: Optional[Any]						    =  patch_embed_hidden_size
							_lowerCAmelCase		: Any						    =  d_ff
							_lowerCAmelCase		: Optional[Any]						    =  dropout_rate
							_lowerCAmelCase		: int						    =  num_hidden_layers
							_lowerCAmelCase		: Optional[int]						    =  num_attention_heads
							_lowerCAmelCase		: Tuple						    =  initializer_range
							_lowerCAmelCase		: Optional[Any]						    =  initializer_factor
							_lowerCAmelCase		: Optional[Any]						    =  attention_dropout
							_lowerCAmelCase		: Tuple						    =  layer_norm_eps
							_lowerCAmelCase		: int						    =  dense_act_fn
							_lowerCAmelCase		: Union[str, Any]						    =  seq_len
							_lowerCAmelCase		: Optional[Any]						    =  relative_attention_num_buckets
							_lowerCAmelCase		: Dict						    =  relative_attention_max_distance
							_lowerCAmelCase		: Union[str, Any]						    =  d_kv






					@classmethod
					def 					__A						(	cls       ,  a__       ,  **a__						):
							cls._set_token_in_kwargs(a__						)

							_lowerCAmelCase		: Tuple						    =  cls.get_config_dict(a__       ,  **a__						)

							# get the vision config dict if we are loading from Pix2StructConfig
							if config_dict.get("""model_type"""						) == "pix2struct":
									_lowerCAmelCase		: int						    =  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(a__       ,  **a__						)







class 		__A							(				SCREAMING_SNAKE_CASE_	):
					_UpperCamelCase							:    Any	      =	"pix2struct"
					_UpperCamelCase							:    int	      =	True






					def __init__(	self       ,  a__=None       ,  a__=None       ,  a__=1.0       ,  a__=0.0_2       ,  a__=False       ,  a__=False       ,  a__=True       ,  **a__       ,  ):
							super().__init__(tie_word_embeddings=a__       ,  is_encoder_decoder=a__       ,  **a__						)

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

							if vision_config is None:
									_lowerCAmelCase		: List[str]						    =  {}
									logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""						)

							_lowerCAmelCase		: Tuple						    =  PixaStructTextConfig(**a__						)
							_lowerCAmelCase		: List[str]						    =  PixaStructVisionConfig(**a__						)

							_lowerCAmelCase		: Optional[int]						    =  self.text_config.decoder_start_token_id
							_lowerCAmelCase		: List[str]						    =  self.text_config.pad_token_id
							_lowerCAmelCase		: Dict						    =  self.text_config.eos_token_id

							_lowerCAmelCase		: Optional[int]						    =  initializer_factor
							_lowerCAmelCase		: Optional[Any]						    =  initializer_range

							_lowerCAmelCase		: Optional[Any]						    =  self.initializer_range
							_lowerCAmelCase		: Union[str, Any]						    =  self.initializer_range

							_lowerCAmelCase		: List[Any]						    =  is_vqa






					@classmethod
					def 					__A						(	cls       ,  a__       ,  a__       ,  **a__						):
							return cls(text_config=text_config.to_dict()       ,  vision_config=vision_config.to_dict()       ,  **a__						)






					def 					__A						(	self						):
							_lowerCAmelCase		: Optional[int]						    =  copy.deepcopy(self.__dict__						)
							_lowerCAmelCase		: Optional[Any]						    =  self.text_config.to_dict()
							_lowerCAmelCase		: Union[str, Any]						    =  self.vision_config.to_dict()
							_lowerCAmelCase		: Any						    =  self.__class__.model_type
							return output





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_a   :	Union[str, Any] 					=     {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Tuple 					=     ['GPTSw3Tokenizer']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_gpt_swa import GPTSwaTokenizer

else:
     import sys

     _a   :	Optional[int] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




