



'''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 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  ['image_processor', 'tokenizer']
			A__								=  'LayoutLMv2ImageProcessor'
			A__								=  ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Dict=None    ,			**__UpperCamelCase		:						int			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   str      =    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    ,			)
											snake_case__      :   Optional[int]      =    kwargs.pop('''feature_extractor'''			)

							snake_case__      :   Union[str, Any]      =    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		:						Tuple    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,			__UpperCamelCase		:						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,			__UpperCamelCase		:						Union[List[List[int]], List[List[List[int]]]] = None    ,			__UpperCamelCase		:						Optional[Union[List[int], List[List[int]]]] = None    ,			__UpperCamelCase		:						bool = True    ,			__UpperCamelCase		:						Union[bool, str, PaddingStrategy] = False    ,			__UpperCamelCase		:						Union[bool, str, TruncationStrategy] = False    ,			__UpperCamelCase		:						Optional[int] = None    ,			__UpperCamelCase		:						int = 0    ,			__UpperCamelCase		:						Optional[int] = None    ,			__UpperCamelCase		:						Optional[bool] = None    ,			__UpperCamelCase		:						Optional[bool] = None    ,			__UpperCamelCase		:						bool = False    ,			__UpperCamelCase		:						bool = False    ,			__UpperCamelCase		:						bool = False    ,			__UpperCamelCase		:						bool = False    ,			__UpperCamelCase		:						bool = True    ,			__UpperCamelCase		:						Optional[Union[str, TensorType]] = None    ,			**__UpperCamelCase		:						Tuple    ,			)      ->      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.'''			)

							if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''			)

							# first, apply the image processor
							snake_case__      :   Tuple      =    self.image_processor(images=__UpperCamelCase    ,			return_tensors=__UpperCamelCase			)

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

							snake_case__      :   List[str]      =    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=__UpperCamelCase    ,			add_special_tokens=__UpperCamelCase    ,			padding=__UpperCamelCase    ,			truncation=__UpperCamelCase    ,			max_length=__UpperCamelCase    ,			stride=__UpperCamelCase    ,			pad_to_multiple_of=__UpperCamelCase    ,			return_token_type_ids=__UpperCamelCase    ,			return_attention_mask=__UpperCamelCase    ,			return_overflowing_tokens=__UpperCamelCase    ,			return_special_tokens_mask=__UpperCamelCase    ,			return_offsets_mapping=__UpperCamelCase    ,			return_length=__UpperCamelCase    ,			verbose=__UpperCamelCase    ,			return_tensors=__UpperCamelCase    ,			**__UpperCamelCase    ,			)

							# add pixel values
							snake_case__      :   Union[str, Any]      =    features.pop('''pixel_values'''			)
							if return_overflowing_tokens is True:
											snake_case__      :   Tuple      =    self.get_overflowing_images(__UpperCamelCase    ,			encoded_inputs['''overflow_to_sample_mapping''']			)
							snake_case__      :   List[Any]      =    images

							return encoded_inputs



			def 				lowerCAmelCase (     self		:						int    ,			__UpperCamelCase		:						List[str]    ,			__UpperCamelCase		:						Any			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   Optional[int]      =    []
							for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]			)

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

							return images_with_overflow



			def 				lowerCAmelCase (     self		:						Tuple    ,			*__UpperCamelCase		:						Optional[int]    ,			**__UpperCamelCase		:						int			)      ->      List[Any]:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			*__UpperCamelCase		:						str    ,			**__UpperCamelCase		:						Any			)      ->      Union[str, Any]:
							"""simple docstring"""





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



			@property
			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Tuple:
							"""simple docstring"""





							return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



			@property
			def 				lowerCAmelCase (     self		:						List[str]			)      ->      str:
							"""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 				lowerCAmelCase (     self		:						Dict			)      ->      str:
							"""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"""



import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
						from .testing_utils import (
						    floats_tensor,
						    load_hf_numpy,
						    load_image,
						    load_numpy,
						    load_pt,
						    nightly,
						    parse_flag_from_env,
						    print_tensor_test,
						    require_torch_a,
						    require_torch_gpu,
						    skip_mps,
						    slow,
						    torch_all_close,
						    torch_device,
						)
						from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Dict   )		->Union[str, Any]:
						if version.parse(UpperCAmelCase__   ) < version.parse(UpperCAmelCase__   ):
												if "dev" in min_version:
																		A__    :	Optional[int]			=	(
																		    """This example requires a source install from HuggingFace diffusers (see """
																		    """`https://huggingface.co/docs/diffusers/installation#install-from-source`),"""
																		)
												else:
																		A__    :	Union[str, Any]			=	f'This example requires a minimum version of {min_version},'
												error_message += f' but the version found is {__version__}.\n'
												raise ImportError(UpperCAmelCase__   )






