
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       __A(				__A       ):
     SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''tokenizer''']
     SCREAMING_SNAKE_CASE			    =    '''LayoutLMv2ImageProcessor'''
     SCREAMING_SNAKE_CASE			    =    ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')

     def __init__(	self					:     List[str]   ,					__UpperCamelCase					:     Tuple=None   ,					__UpperCamelCase					:     str=None   ,					**__UpperCamelCase					:     Dict   ):

            if "feature_extractor" in kwargs:
                   warnings.warn(
                       """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                       """ instead."""   ,					__UpperCamelCase   ,					)
                   lowerCamelCase_							  =					kwargs.pop("""feature_extractor"""   )

            lowerCamelCase_							  =					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					:     Any   ,					__UpperCamelCase					:     Optional[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] = None   ,					__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					:     Any   ,					):

            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
            lowerCamelCase_							  =					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   ):
                          lowerCamelCase_							  =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
                   lowerCamelCase_							  =					features['''words''']

            lowerCamelCase_							  =					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
            lowerCamelCase_							  =					features.pop("""pixel_values"""   )
            if return_overflowing_tokens is True:
                   lowerCamelCase_							  =					self.get_overflowing_images(__UpperCamelCase   ,					encoded_inputs["""overflow_to_sample_mapping"""]   )
            lowerCamelCase_							  =					images

            return encoded_inputs

     def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ):
            lowerCamelCase_							  =					[]
            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    lowercase__      (	self					:     Union[str, Any]   ,					*__UpperCamelCase					:     Tuple   ,					**__UpperCamelCase					:     Dict   ):
            return self.tokenizer.batch_decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

     def    lowercase__      (	self					:     Dict   ,					*__UpperCamelCase					:     Any   ,					**__UpperCamelCase					:     Dict   ):
            return self.tokenizer.decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

     @property
     def    lowercase__      (	self					:     List[str]   ):
            return ["input_ids", "bbox", "attention_mask", "image"]

     @property
     def    lowercase__      (	self					:     int   ):
            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					:     Dict   ):
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,					__UpperCamelCase   ,					)
            return self.image_processor







from typing import TYPE_CHECKING

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


lowercase        =  {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''RagModel''',
		    '''RagPreTrainedModel''',
		    '''RagSequenceForGeneration''',
		    '''RagTokenForGeneration''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TFRagModel''',
		    '''TFRagPreTrainedModel''',
		    '''TFRagSequenceForGeneration''',
		    '''TFRagTokenForGeneration''',
		]


if TYPE_CHECKING:
		from .configuration_rag import RagConfig
		from .retrieval_rag import RagRetriever
		from .tokenization_rag import RagTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_rag import (
				    TFRagModel,
				    TFRagPreTrainedModel,
				    TFRagSequenceForGeneration,
				    TFRagTokenForGeneration,
				)

else:
		import sys

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





