




import copy
import os
from typing import Union

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


__A						:							Tuple							=  logging.get_logger(__name__)

__A						:							List[str]							=  {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''pix2struct_text_model'''
							a__						  =     ['''past_key_values''']
							a__						  =     {
							    '''hidden_size''': '''hidden_size''',
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}





							def __init__(					self       :	Any       ,     a       :	str=50_244       ,     a       :	Optional[int]=768       ,     a       :	int=64       ,     a       :	Any=2_048       ,     a       :	List[str]=12       ,     a       :	Optional[int]=12       ,     a       :	List[str]=32       ,     a       :	Union[str, Any]=128       ,     a       :	Any=0.1       ,     a       :	str=1E-6       ,     a       :	int=1.0       ,     a       :	Optional[int]="gelu_new"       ,     a       :	List[Any]=0       ,     a       :	Dict=False       ,     a       :	Union[str, Any]=0       ,     a       :	Union[str, Any]=1       ,     a       :	List[Any]=False       ,     a       :	List[Any]=True       ,     **a       :	Optional[int]       ,     )			->					Dict:
										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					d_kv
										SCREAMING_SNAKE_CASE           =					d_ff
										SCREAMING_SNAKE_CASE           =					num_layers
										SCREAMING_SNAKE_CASE           =					num_heads
										SCREAMING_SNAKE_CASE           =					relative_attention_num_buckets
										SCREAMING_SNAKE_CASE           =					relative_attention_max_distance
										SCREAMING_SNAKE_CASE           =					dropout_rate
										SCREAMING_SNAKE_CASE           =					layer_norm_epsilon
										SCREAMING_SNAKE_CASE           =					initializer_factor
										SCREAMING_SNAKE_CASE           =					use_cache

										SCREAMING_SNAKE_CASE           =					eos_token_id
										SCREAMING_SNAKE_CASE           =					decoder_start_token_id

										# for backwards compatibility
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					cls       :	Tuple       ,     a       :	Union[str, os.PathLike]       ,     **a       :	Optional[Any]			)			->					"PretrainedConfig":
										cls._set_token_in_kwargs(a			)

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

										# get the text config dict if we are loading from Pix2StructConfig
										if config_dict.get("""model_type"""			) == "pix2struct":
													SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''pix2struct_vision_model'''





							def __init__(					self       :	Optional[Any]       ,     a       :	int=768       ,     a       :	Optional[int]=768       ,     a       :	int=2_048       ,     a       :	List[Any]=64       ,     a       :	str=12       ,     a       :	Any=12       ,     a       :	Optional[int]="gelu_new"       ,     a       :	List[Any]=1E-6       ,     a       :	Optional[int]=0.0       ,     a       :	Dict=0.0       ,     a       :	Any=1E-1_0       ,     a       :	str=1.0       ,     a       :	Optional[int]=4_096       ,     a       :	Any=32       ,     a       :	Optional[int]=128       ,     **a       :	Tuple       ,     )			->					Optional[Any]:
										super().__init__(**a			)

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







							@classmethod
							def    _UpperCAmelCase     (					cls       :	int       ,     a       :	Union[str, os.PathLike]       ,     **a       :	Any			)			->					"PretrainedConfig":
										cls._set_token_in_kwargs(a			)

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

										# get the vision config dict if we are loading from Pix2StructConfig
										if config_dict.get("""model_type"""			) == "pix2struct":
													SCREAMING_SNAKE_CASE           =					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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''pix2struct'''
							a__						  =     True





							def __init__(					self       :	Tuple       ,     a       :	List[str]=None       ,     a       :	int=None       ,     a       :	Union[str, Any]=1.0       ,     a       :	Optional[int]=0.02       ,     a       :	int=False       ,     a       :	List[str]=False       ,     a       :	Any=True       ,     **a       :	Optional[Any]       ,     )			->					List[str]:
										super().__init__(tie_word_embeddings=a       ,     is_encoder_decoder=a       ,     **a			)

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

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

										SCREAMING_SNAKE_CASE           =					PixaStructTextConfig(**a			)
										SCREAMING_SNAKE_CASE           =					PixaStructVisionConfig(**a			)

										SCREAMING_SNAKE_CASE           =					self.text_config.decoder_start_token_id
										SCREAMING_SNAKE_CASE           =					self.text_config.pad_token_id
										SCREAMING_SNAKE_CASE           =					self.text_config.eos_token_id

										SCREAMING_SNAKE_CASE           =					initializer_factor
										SCREAMING_SNAKE_CASE           =					initializer_range

										SCREAMING_SNAKE_CASE           =					self.initializer_range
										SCREAMING_SNAKE_CASE           =					self.initializer_range

										SCREAMING_SNAKE_CASE           =					is_vqa





							@classmethod
							def    _UpperCAmelCase     (					cls       :	Optional[int]       ,     a       :	PixaStructTextConfig       ,     a       :	PixaStructVisionConfig       ,     **a       :	Dict			)			->					Optional[int]:
										return cls(text_config=text_config.to_dict()       ,     vision_config=vision_config.to_dict()       ,     **a			)







							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					copy.deepcopy(self.__dict__			)
										SCREAMING_SNAKE_CASE           =					self.text_config.to_dict()
										SCREAMING_SNAKE_CASE           =					self.vision_config.to_dict()
										SCREAMING_SNAKE_CASE           =					self.__class__.model_type
										return output












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''', '''tokenizer''']
							a__						  =     '''ViTImageProcessor'''
							a__						  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





							def __init__(					self       :	List[str]       ,     a       :	Dict=None       ,     a       :	List[str]=None       ,     **a       :	List[str]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead."""       ,     a       ,     )
													SCREAMING_SNAKE_CASE           =					kwargs.pop("""feature_extractor"""			)

										SCREAMING_SNAKE_CASE           =					image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("""You need to specify an `image_processor`."""			)
										if tokenizer is None:
													raise ValueError("""You need to specify a `tokenizer`."""			)

										super().__init__(a       ,     a			)





							def __call__(					self       :	List[Any]       ,     a       :	int=None       ,     a       :	Dict=None       ,     a       :	int=None       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					Any:
										if text is None and visual_prompt is None and images is None:
													raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
													raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
													SCREAMING_SNAKE_CASE           =					self.tokenizer(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if images is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """pixel_values""": image_features.pixel_values,
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a			)       ,     tensor_type=a			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     *a       :	Dict       ,     **a       :	Union[str, Any]			)			->					Any:
										return self.tokenizer.batch_decode(*a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     *a       :	List[Any]       ,     **a       :	Union[str, Any]			)			->					Dict:
										return self.tokenizer.decode(*a       ,     **a			)





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,     a       ,     )
										return self.image_processor_class







							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,     a       ,     )
										return self.image_processor






