


'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCamelCase (		lowercase__							):



		'''simple docstring'''







		A_			:    List[str]   					=       ["""image_processor""", """tokenizer"""]
		A_			:    Optional[int]   					=       """AutoImageProcessor"""
		A_			:    Optional[Any]   					=       """AutoTokenizer"""







		def __init__(      self	:		Tuple						,  _A	:		Dict=None						,  _A	:		Optional[int]=None						,  **_A	:		Optional[int]    )						->				Any:
			__magic_name__ :					Dict  			=  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						,  )
				__magic_name__ :					Union[str, Any]  			=  kwargs.pop('feature_extractor'    )

			__magic_name__ :					str  			=  image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
				raise ValueError('You have to specify an image_processor.'    )
			if tokenizer is None:
				raise ValueError('You have to specify a tokenizer.'    )

			super().__init__(_A						,  _A    )
			__magic_name__ :					str  			=  self.image_processor







		def __call__(      self	:		str						,  _A	:		Dict=None						,  _A	:		Optional[int]=None						,  _A	:		Any=None						,  **_A	:		str    )						->				Any:

			if text is None and images is None:
				raise ValueError('You have to specify either text or images. Both cannot be none.'    )

			if text is not None:
				__magic_name__ :					Optional[Any]  			=  self.tokenizer(_A						,  return_tensors=_A						,  **_A    )

			if images is not None:
				__magic_name__ :					Any  			=  self.image_processor(_A						,  return_tensors=_A						,  **_A    )

			if text is not None and images is not None:
				__magic_name__ :					int  			=  image_features.pixel_values
				return encoding
			elif text is not None:
				return encoding
			else:
				return BatchEncoding(data=dict(**_A    )						,  tensor_type=_A    )







		def      __lowerCAmelCase	(      self	:		Optional[int]						,  *_A	:		List[str]						,  **_A	:		str    )						->				Dict:
			return self.tokenizer.batch_decode(*_A						,  **_A    )







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







		@property
		def      __lowerCAmelCase	(      self	:		Tuple    )						->				Optional[Any]:
			__magic_name__ :					Union[str, Any]  			=  self.tokenizer.model_input_names
			__magic_name__ :					Any  			=  self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







		@property
		def      __lowerCAmelCase	(      self	:		List[str]    )						->				List[str]:
			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      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Optional[Any]:
			warnings.warn(
			    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,  _A						,  )
			return self.image_processor



'''simple docstring'''

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


lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)

lowerCAmelCase   :Dict								=    {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}
class _lowerCamelCase (		lowercase__							):



		'''simple docstring'''







		A_			:    Optional[Any]   					=       """yoso"""







		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )

			__magic_name__ :					List[Any]  			=  vocab_size
			__magic_name__ :					int  			=  max_position_embeddings
			__magic_name__ :					int  			=  hidden_size
			__magic_name__ :					Any  			=  num_hidden_layers
			__magic_name__ :					List[str]  			=  num_attention_heads
			__magic_name__ :					str  			=  intermediate_size
			__magic_name__ :					Any  			=  hidden_act
			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
			__magic_name__ :					str  			=  attention_probs_dropout_prob
			__magic_name__ :					int  			=  initializer_range
			__magic_name__ :					List[Any]  			=  type_vocab_size
			__magic_name__ :					Tuple  			=  layer_norm_eps
			__magic_name__ :					Tuple  			=  position_embedding_type
			__magic_name__ :					Optional[Any]  			=  use_expectation
			__magic_name__ :					Union[str, Any]  			=  hash_code_len
			__magic_name__ :					int  			=  num_hash
			__magic_name__ :					List[Any]  			=  conv_window
			__magic_name__ :					Optional[Any]  			=  use_fast_hash
			__magic_name__ :					int  			=  lsh_backward