
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 							lowercase   (						_snake_case  ):
		__lowercase			:			Optional[int]      	=							["image_processor", "tokenizer"]
		__lowercase			:			Any      	=							"LayoutLMv2ImageProcessor"
		__lowercase			:			Any      	=							("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")



		def __init__(					self							, A_=None							, A_=None							, **A_			)    ->   Union[str, 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							, A_							, A_ = None							, A_ = None							, A_ = None							, A_ = None							, A_ = True							, A_ = False							, A_ = False							, A_ = None							, A_ = 0							, A_ = None							, A_ = None							, A_ = None							, A_ = False							, A_ = False							, A_ = False							, A_ = False							, A_ = True							, A_ = None							, **A_							, )    ->   BatchEncoding:
				"""simple docstring"""
				# verify input
				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
				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 __UpperCamelCase					(					self							, A_							, A_			)    ->   int:
				"""simple docstring"""
				# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
				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 __UpperCamelCase					(					self							, *A_							, **A_			)    ->   List[str]:
				"""simple docstring"""
				return self.tokenizer.batch_decode(*lowerCAmelCase__							, **lowerCAmelCase__			)



		def __UpperCamelCase					(					self							, *A_							, **A_			)    ->   List[Any]:
				"""simple docstring"""
				return self.tokenizer.decode(*lowerCAmelCase__							, **lowerCAmelCase__			)



		@property
		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]



		@property
		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""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 __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				warnings.warn(
				    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							, lowerCAmelCase__							, )
				return self.image_processor



from typing import TYPE_CHECKING

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


lowerCAmelCase__							=			{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			["XLMProphetNetTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowerCAmelCase__							=			[
          "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMProphetNetDecoder",
          "XLMProphetNetEncoder",
          "XLMProphetNetForCausalLM",
          "XLMProphetNetForConditionalGeneration",
          "XLMProphetNetModel",
          "XLMProphetNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_prophetnet import (
                XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMProphetNetDecoder,
                XLMProphetNetEncoder,
                XLMProphetNetForCausalLM,
                XLMProphetNetForConditionalGeneration,
                XLMProphetNetModel,
                XLMProphetNetPreTrainedModel,
            )

else:
      import sys

      lowerCAmelCase__							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



