


'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   str							= ["image_processor", "tokenizer"]
				__magic_name__       :   Union[str, Any]							= "LayoutLMv3ImageProcessor"
				__magic_name__       :   List[str]							= ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")




				def __init__(	self    :				Optional[int]   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[int]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			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__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,					lowerCAmelCase    :				Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,					lowerCAmelCase    :				Union[List[List[int]], List[List[List[int]]]] = None   ,					lowerCAmelCase    :				Optional[Union[List[int], List[List[int]]]] = None   ,					lowerCAmelCase    :				bool = True   ,					lowerCAmelCase    :				Union[bool, str, PaddingStrategy] = False   ,					lowerCAmelCase    :				Union[bool, str, TruncationStrategy] = None   ,					lowerCAmelCase    :				Optional[int] = None   ,					lowerCAmelCase    :				int = 0   ,					lowerCAmelCase    :				Optional[int] = None   ,					lowerCAmelCase    :				Optional[bool] = None   ,					lowerCAmelCase    :				Optional[bool] = None   ,					lowerCAmelCase    :				bool = False   ,					lowerCAmelCase    :				bool = False   ,					lowerCAmelCase    :				bool = False   ,					lowerCAmelCase    :				bool = False   ,					lowerCAmelCase    :				bool = True   ,					lowerCAmelCase    :				Optional[Union[str, TensorType]] = None   ,					**lowerCAmelCase    :				Optional[int]   ,					)->  BatchEncoding:

										"""simple docstring"""




										if self.image_processor.apply_ocr and (boxes is not None):
																raise ValueError(
																    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''   )

										if self.image_processor.apply_ocr and (word_labels is not None):
																raise ValueError(
																    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''   )

										# first, apply the image processor
										UpperCAmelCase         =			self.image_processor(images=lowerCAmelCase   ,					return_tensors=lowerCAmelCase   )

										# second, apply the tokenizer
										if text is not None and self.image_processor.apply_ocr and text_pair is None:
																if isinstance(lowerCAmelCase   ,					lowerCAmelCase   ):
																						UpperCAmelCase         =			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																UpperCAmelCase         =			features['''words''']

										UpperCAmelCase         =			self.tokenizer(
										    text=text if text is not None else features['''words''']   ,					text_pair=text_pair if text_pair is not None else None   ,					boxes=boxes if boxes is not None else features['''boxes''']   ,					word_labels=lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   ,					padding=lowerCAmelCase   ,					truncation=lowerCAmelCase   ,					max_length=lowerCAmelCase   ,					stride=lowerCAmelCase   ,					pad_to_multiple_of=lowerCAmelCase   ,					return_token_type_ids=lowerCAmelCase   ,					return_attention_mask=lowerCAmelCase   ,					return_overflowing_tokens=lowerCAmelCase   ,					return_special_tokens_mask=lowerCAmelCase   ,					return_offsets_mapping=lowerCAmelCase   ,					return_length=lowerCAmelCase   ,					verbose=lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   ,					)

										# add pixel values
										UpperCAmelCase         =			features.pop('''pixel_values'''   )
										if return_overflowing_tokens is True:
																UpperCAmelCase         =			self.get_overflowing_images(lowerCAmelCase   ,					encoded_inputs['''overflow_to_sample_mapping''']   )
										UpperCAmelCase         =			images

										return encoded_inputs




				def   a__(	self    :				str   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[int]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			[]
										for sample_idx in overflow_to_sample_mapping:
																images_with_overflow.append(images[sample_idx]   )

										if len(lowerCAmelCase   ) != len(lowerCAmelCase   ):
																raise ValueError(
																    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
																    F""" {len(lowerCAmelCase   )} and {len(lowerCAmelCase   )}"""   )

										return images_with_overflow




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				Optional[int]   ,					**lowerCAmelCase    :				List[Any]   )->  List[Any]:

										"""simple docstring"""




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




				def   a__(	self    :				List[Any]   ,					*lowerCAmelCase    :				Optional[int]   ,					**lowerCAmelCase    :				Tuple   )->  Dict:

										"""simple docstring"""




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




				@property
				def   a__(	self    :				List[Any]   )->  Dict:

										"""simple docstring"""




										return ["input_ids", "bbox", "attention_mask", "pixel_values"]




				@property
				def   a__(	self    :				int   )->  Tuple:

										"""simple docstring"""




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





				@property
				def   a__(	self    :				Union[str, Any]   )->  Optional[int]:

										"""simple docstring"""




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









'''simple docstring'''



import warnings

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






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			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__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[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:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

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




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

										"""simple docstring"""




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




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




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




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

										"""simple docstring"""




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





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




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





