



'''simple docstring'''

import warnings

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


class 	a__     (				_lowercase	):
	__magic_name__		:    List[Any]	    =     ["image_processor", "tokenizer"]
	__magic_name__		:    str	    =     "ViTImageProcessor"
	__magic_name__		:    Optional[Any]	    =     ("CLIPTokenizer", "CLIPTokenizerFast")


	def __init__(self : Union[str, Any],					__UpperCAmelCase : Any=None,					__UpperCAmelCase : Any=None,					**__UpperCAmelCase : Union[str, Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.''',					__UpperCAmelCase,					)
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= kwargs.pop('''feature_extractor'''      )

					SCREAMING_SNAKE_CASE  :						Dict      					= 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__(__UpperCAmelCase,					__UpperCAmelCase      )


	def __call__(self : List[str],					__UpperCAmelCase : Optional[int]=None,					__UpperCAmelCase : int=None,					__UpperCAmelCase : List[Any]=None,					__UpperCAmelCase : Union[str, Any]=None,					**__UpperCAmelCase : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					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  :						Tuple      					= self.tokenizer(__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase      )

					if visual_prompt is not None:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= self.image_processor(__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase      )

					if images is not None:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.image_processor(__UpperCAmelCase,					return_tensors=__UpperCAmelCase,					**__UpperCAmelCase      )

					if visual_prompt is not None and images is not None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= {
									    '''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  :						str      					= image_features.pixel_values
									return encoding
					elif text is not None:
									return encoding
					elif visual_prompt is not None:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= {
									    '''conditional_pixel_values''': prompt_features.pixel_values,
									}
									return encoding
					else:
									return BatchEncoding(data=dict(**__UpperCAmelCase      ),					tensor_type=__UpperCAmelCase      )


	def 				lowercase__  (self : Tuple,					*__UpperCAmelCase : List[str],					**__UpperCAmelCase : List[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					return self.tokenizer.batch_decode(*__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Union[str, Any],					*__UpperCAmelCase : List[Any],					**__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

					return self.tokenizer.decode(*__UpperCAmelCase,					**__UpperCAmelCase      )


	@property
	def 				lowercase__  (self : Optional[int]      )     ->			List[Any]:

					"""simple docstring"""

					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',					__UpperCAmelCase,					)
					return self.image_processor_class






	@property
	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',					__UpperCAmelCase,					)
					return self.image_processor









'''simple docstring'''

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


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}


class 	a__     (				_lowercase	):
	__magic_name__		:    List[Any]	    =     "markuplm"


	def __init__(self : List[Any],					__UpperCAmelCase : int=30522,					__UpperCAmelCase : Tuple=768,					__UpperCAmelCase : str=12,					__UpperCAmelCase : List[Any]=12,					__UpperCAmelCase : Tuple=3072,					__UpperCAmelCase : Union[str, Any]="gelu",					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Union[str, Any]=512,					__UpperCAmelCase : Dict=2,					__UpperCAmelCase : int=0.02,					__UpperCAmelCase : Optional[Any]=1e-12,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : Optional[Any]=0,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : int=256,					__UpperCAmelCase : Tuple=1024,					__UpperCAmelCase : Optional[Any]=216,					__UpperCAmelCase : str=1001,					__UpperCAmelCase : Optional[Any]=32,					__UpperCAmelCase : Dict=50,					__UpperCAmelCase : Union[str, Any]="absolute",					__UpperCAmelCase : Tuple=True,					__UpperCAmelCase : List[str]=None,					**__UpperCAmelCase : Union[str, Any],					)     ->			Union[str, Any]:

					"""simple docstring"""

					super().__init__(
					    pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase,					**__UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Dict      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						Dict      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Tuple      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						List[str]      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						List[str]      					= max_position_embeddings
					SCREAMING_SNAKE_CASE  :						Dict      					= type_vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						int      					= position_embedding_type
					SCREAMING_SNAKE_CASE  :						List[Any]      					= use_cache
					SCREAMING_SNAKE_CASE  :						int      					= classifier_dropout
					# additional properties
					SCREAMING_SNAKE_CASE  :						List[Any]      					= max_depth
					SCREAMING_SNAKE_CASE  :						Any      					= max_xpath_tag_unit_embeddings
					SCREAMING_SNAKE_CASE  :						int      					= max_xpath_subs_unit_embeddings
					SCREAMING_SNAKE_CASE  :						Dict      					= tag_pad_id
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= subs_pad_id
					SCREAMING_SNAKE_CASE  :						int      					= xpath_unit_hidden_size




