

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_						(     __lowercase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       ["image_processor", "tokenizer"]
							UpperCAmelCase__			:		List[str]             =       "LayoutLMv2ImageProcessor"
							UpperCAmelCase__			:		Any             =       ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


							def __init__(     self			,				_a=None			,				_a=None			,				**_a			)       ->       str:
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''			,				_a			,				)
																	_a      :       Union[str, Any]   =						kwargs.pop('''feature_extractor'''			)

												_a      :       str   =						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__(_a			,				_a			)


							def __call__(     self			,				_a			,				_a = None			,				_a = None			,				_a = None			,				_a = None			,				_a = True			,				_a = False			,				_a = None			,				_a = None			,				_a = 0			,				_a = None			,				_a = None			,				_a = None			,				_a = False			,				_a = False			,				_a = False			,				_a = False			,				_a = True			,				_a = None			,				**_a			,				)       ->       BatchEncoding:
												# 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
												_a      :       List[Any]   =						self.image_processor(images=_a			,				return_tensors=_a			)

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

												_a      :       int   =						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=_a			,				add_special_tokens=_a			,				padding=_a			,				truncation=_a			,				max_length=_a			,				stride=_a			,				pad_to_multiple_of=_a			,				return_token_type_ids=_a			,				return_attention_mask=_a			,				return_overflowing_tokens=_a			,				return_special_tokens_mask=_a			,				return_offsets_mapping=_a			,				return_length=_a			,				verbose=_a			,				return_tensors=_a			,				**_a			,				)

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

												return encoded_inputs


							def 							__lowercase							(     self			,				_a			,				_a			)       ->       Any:
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												_a      :       Optional[int]   =						[]
												for sample_idx in overflow_to_sample_mapping:
																	images_with_overflow.append(images[sample_idx]			)

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

												return images_with_overflow


							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       List[str]:
												return self.tokenizer.batch_decode(*_a			,				**_a			)


							def 							__lowercase							(     self			,				*_a			,				**_a			)       ->       Union[str, Any]:
												return self.tokenizer.decode(*_a			,				**_a			)


							@property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return ["input_ids", "bbox", "attention_mask", "image"]


							@property
							def 							__lowercase							(     self			)       ->       str:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,				_a			,				)
												return self.image_processor_class




							@property
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,				_a			,				)
												return self.image_processor








'''simple docstring'''





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



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

      # Test not batched input
      _a           =    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           =    image_processing(A					,  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'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    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           =    image_processing(A					,  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'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    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           =    image_processing(A					,  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'''],
          )					,  )



