
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     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Any    		=	['image_processor', 'tokenizer']
	UpperCamelCase_  :					Optional[int]    		=	'LayoutLMv3ImageProcessor'
	UpperCamelCase_  :					Optional[Any]    		=	('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







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







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= 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__(lowercase   ,     lowercase			)







	def __call__(     self      :Optional[Any]   ,     lowercase      :List[Any]   ,     lowercase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     lowercase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,     lowercase      :Union[List[List[int]], List[List[List[int]]]] = None   ,     lowercase      :Optional[Union[List[int], List[List[int]]]] = None   ,     lowercase      :bool = True   ,     lowercase      :Union[bool, str, PaddingStrategy] = False   ,     lowercase      :Union[bool, str, TruncationStrategy] = None   ,     lowercase      :Optional[int] = None   ,     lowercase      :int = 0   ,     lowercase      :Optional[int] = None   ,     lowercase      :Optional[bool] = None   ,     lowercase      :Optional[bool] = None   ,     lowercase      :bool = False   ,     lowercase      :bool = False   ,     lowercase      :bool = False   ,     lowercase      :bool = False   ,     lowercase      :bool = True   ,     lowercase      :Optional[Union[str, TensorType]] = None   ,     **lowercase      :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.'''			)

				# first, apply the image processor
				SCREAMING_SNAKE_CASE     						= self.image_processor(images=lowercase   ,     return_tensors=lowercase			)

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

				SCREAMING_SNAKE_CASE     						= 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=lowercase   ,     add_special_tokens=lowercase   ,     padding=lowercase   ,     truncation=lowercase   ,     max_length=lowercase   ,     stride=lowercase   ,     pad_to_multiple_of=lowercase   ,     return_token_type_ids=lowercase   ,     return_attention_mask=lowercase   ,     return_overflowing_tokens=lowercase   ,     return_special_tokens_mask=lowercase   ,     return_offsets_mapping=lowercase   ,     return_length=lowercase   ,     verbose=lowercase   ,     return_tensors=lowercase   ,     **lowercase   ,     )

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

				return encoded_inputs







	def  snake_case__     (     self      :int   ,     lowercase      :str   ,     lowercase      :Union[str, Any]			)		->      List[str]:







				"""simple docstring"""






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

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

				return images_with_overflow







	def  snake_case__     (     self      :Optional[Any]   ,     *lowercase      :Union[str, Any]   ,     **lowercase      :Optional[int]			)		->      Union[str, Any]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :int   ,     *lowercase      :Optional[int]   ,     **lowercase      :Optional[Any]			)		->      Optional[Any]:







				"""simple docstring"""






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







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







				"""simple docstring"""






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







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







				"""simple docstring"""






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







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







				"""simple docstring"""






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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase					   =       {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BloomTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BloomForCausalLM',
							    'BloomModel',
							    'BloomPreTrainedModel',
							    'BloomForSequenceClassification',
							    'BloomForTokenClassification',
							    'BloomForQuestionAnswering',
							]

if TYPE_CHECKING:
							from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bloom_fast import BloomTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bloom import (
														    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BloomForCausalLM,
														    BloomForQuestionAnswering,
														    BloomForSequenceClassification,
														    BloomForTokenClassification,
														    BloomModel,
														    BloomPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)