




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 					snake_case_   (     _UpperCamelCase	):



				'''simple docstring'''
				lowerCamelCase       =  ["""image_processor""", """tokenizer"""]
				lowerCamelCase       =  """LayoutLMv3ImageProcessor"""
				lowerCamelCase       =  ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")





				def __init__(      self							:   Optional[int]      ,							__magic_name__							:   Dict=None      ,							__magic_name__							:   List[str]=None      ,							**__magic_name__							:   Any							)							->			List[str]:
											lowerCamelCase_			:    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."      ,							__magic_name__      ,							)
																		lowerCamelCase_			:    Any   						=      kwargs.pop("feature_extractor"							)

											lowerCamelCase_			:    Tuple   						=      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__(__magic_name__      ,							__magic_name__							)





				def __call__(      self							:   int      ,							__magic_name__							:   str      ,							__magic_name__							:   Union[str, Any] = None      ,							__magic_name__							:   str = None      ,							__magic_name__							:   int = None      ,							__magic_name__							:   Dict = None      ,							__magic_name__							:   Tuple = True      ,							__magic_name__							:   Tuple = False      ,							__magic_name__							:   Optional[Any] = None      ,							__magic_name__							:   Tuple = None      ,							__magic_name__							:   List[str] = 0      ,							__magic_name__							:   Any = None      ,							__magic_name__							:   str = None      ,							__magic_name__							:   Optional[Any] = None      ,							__magic_name__							:   str = False      ,							__magic_name__							:   Tuple = False      ,							__magic_name__							:   Optional[Any] = False      ,							__magic_name__							:   Any = False      ,							__magic_name__							:   Tuple = True      ,							__magic_name__							:   Tuple = None      ,							**__magic_name__							:   List[Any]      ,							)							->			BatchEncoding:





											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
											lowerCamelCase_			:    str   						=      self.image_processor(images=__magic_name__      ,							return_tensors=__magic_name__							)

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

											lowerCamelCase_			:    Optional[int]   						=      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=__magic_name__      ,							add_special_tokens=__magic_name__      ,							padding=__magic_name__      ,							truncation=__magic_name__      ,							max_length=__magic_name__      ,							stride=__magic_name__      ,							pad_to_multiple_of=__magic_name__      ,							return_token_type_ids=__magic_name__      ,							return_attention_mask=__magic_name__      ,							return_overflowing_tokens=__magic_name__      ,							return_special_tokens_mask=__magic_name__      ,							return_offsets_mapping=__magic_name__      ,							return_length=__magic_name__      ,							verbose=__magic_name__      ,							return_tensors=__magic_name__      ,							**__magic_name__      ,							)

											# add pixel values
											lowerCamelCase_			:    List[Any]   						=      features.pop("pixel_values"							)
											if return_overflowing_tokens is True:
																		lowerCamelCase_			:    List[Any]   						=      self.get_overflowing_images(__magic_name__      ,							encoded_inputs["overflow_to_sample_mapping"]							)
											lowerCamelCase_			:    Optional[int]   						=      images

											return encoded_inputs





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]      ,							__magic_name__							:   Tuple      ,							__magic_name__							:   Dict							)							->			int:
											lowerCamelCase_			:    int   						=      []
											for sample_idx in overflow_to_sample_mapping:
																		images_with_overflow.append(images[sample_idx]							)

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

											return images_with_overflow





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]      ,							*__magic_name__							:   int      ,							**__magic_name__							:   List[str]							)							->			Any:
											return self.tokenizer.batch_decode(*__magic_name__      ,							**__magic_name__							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]      ,							*__magic_name__							:   int      ,							**__magic_name__							:   str							)							->			int:
											return self.tokenizer.decode(*__magic_name__      ,							**__magic_name__							)





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			List[Any]:
											return ["input_ids", "bbox", "attention_mask", "pixel_values"]





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   int							)							->			int:
											warnings.warn(
											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,							__magic_name__      ,							)
											return self.image_processor_class





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			Any:
											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,							__magic_name__      ,							)
											return self.image_processor








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      VersatileDiffusionDualGuidedPipeline,
      VersatileDiffusionImageVariationPipeline,
      VersatileDiffusionPipeline,
      VersatileDiffusionTextToImagePipeline,
  )
else:
  from .modeling_text_unet import UNetFlatConditionModel
  from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
  from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
  from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
  from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline

