'''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  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					["image_processor", "tokenizer"]
     a_    	=					"LayoutLMv2ImageProcessor"
     a_    	=					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


     def __init__(      self							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

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

       a_      :	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__(lowerCAmelCase_							,		lowerCAmelCase_ )


     def __call__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = False							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = 0							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = False							,		lowerCAmelCase_ = False							,		lowerCAmelCase_ = False							,		lowerCAmelCase_ = False							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		**lowerCAmelCase_							,		):

       '''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
       a_      :	str      =							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_ ):
           a_      :	Any      =							[text]  # add batch dimension (as the image processor always adds a batch dimension)
         a_      :	List[str]      =							features["""words"""]

       a_      :	List[Any]      =							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
       a_      :	Optional[int]      =							features.pop("""pixel_values""" )
       if return_overflowing_tokens is True:
         a_      :	List[str]      =							self.get_overflowing_images(lowerCAmelCase_							,		encoded_inputs["""overflow_to_sample_mapping"""] )
       a_      :	Union[str, Any]      =							images

       return encoded_inputs


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[Any]      =							[]
       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 							_lowerCAmelCase  (      self							,		*lowerCAmelCase_							,		**lowerCAmelCase_ ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self							,		*lowerCAmelCase_							,		**lowerCAmelCase_ ):

       '''simple docstring'''

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


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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


     @property
     def 							_lowerCAmelCase  (      self ):

       '''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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor


class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=7							,		lowerCAmelCase_=3							,		lowerCAmelCase_=18							,		lowerCAmelCase_=30							,		lowerCAmelCase_=4_00							,		lowerCAmelCase_=True							,		lowerCAmelCase_=None							,		lowerCAmelCase_=True							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		):

       '''simple docstring'''

       a_      :	Optional[int]      =							parent
       a_      :	Union[str, Any]      =							batch_size
       a_      :	Any      =							num_channels
       a_      :	Tuple      =							image_size
       a_      :	Any      =							min_resolution
       a_      :	Dict      =							max_resolution
       a_      :	Optional[Any]      =							do_resize
       a_      :	Tuple      =							size if size is not None else {"""height""": 18, """width""": 20}
       a_      :	int      =							do_thumbnail
       a_      :	Optional[int]      =							do_align_axis
       a_      :	Tuple      =							do_pad
       a_      :	int      =							do_normalize
       a_      :	Tuple      =							image_mean
       a_      :	Optional[Any]      =							image_std






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					DonutImageProcessor if is_vision_available() else None


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							DonutImageProcessingTester(self )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_resize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""size""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_thumbnail""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_align_long_axis""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_pad""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_normalize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_mean""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_std""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size							,		{"""height""": 18, """width""": 20} )

       a_      :	str      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=42 )
       self.assertEqual(image_processor.size							,		{"""height""": 42, """width""": 42} )

       # Previous config had dimensions in (width, height) order
       a_      :	Any      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=(42, 84) )
       self.assertEqual(image_processor.size							,		{"""height""": 84, """width""": 42} )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       a_      :	str      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		Image.Image )

       # Test not batched input
       a_      :	Dict      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	int      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       a_      :	int      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_							,		numpify=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		np.ndarray )

       # Test not batched input
       a_      :	Optional[Any]      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Dict      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)






     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       a_      :	Optional[Any]      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_							,		torchify=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )

       # Test not batched input
       a_      :	Optional[int]      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Any      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


