
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import TvltImageProcessor


def     a__			(    snake_case   ,	snake_case=10   ,	snake_case=10   ,	snake_case=False   ,	snake_case=False    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						[]
  for i in range(image_processor_tester.num_frames    ):
    video.append(np.random.randint(255   ,	size=(image_processor_tester.num_channels, width, height)   ,	dtype=np.uinta    )    )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    __SCREAMING_SNAKE_CASE     :					List[Any]			=						[Image.fromarray(np.moveaxis(snake_case   ,	0   ,	-1    )    ) for frame in video]

  if torchify:
    __SCREAMING_SNAKE_CASE     :					List[str]			=						[torch.from_numpy(snake_case    ) for frame in video]

  return video


def     a__			(    snake_case   ,	snake_case=False   ,	snake_case=False   ,	snake_case=False    ):





  """simple docstring"""
  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  __SCREAMING_SNAKE_CASE     :					str			=						[]
  for i in range(image_processor_tester.batch_size    ):
    if equal_resolution:
      __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor_tester.max_resolution
    else:
      __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						np.random.choice(
          np.arange(image_processor_tester.min_resolution   ,	image_processor_tester.max_resolution    )   ,	2    )
      __SCREAMING_SNAKE_CASE     :					List[str]			=						prepare_video(
          image_processor_tester=snake_case   ,	width=snake_case   ,	height=snake_case   ,	numpify=snake_case   ,	torchify=snake_case   ,	)
    video_inputs.append(snake_case    )

  return video_inputs





class   __UpperCamelCase				(    unittest.TestCase   ):






     """simple docstring"""







     def __init__(							self    :    Tuple      ,		_A    :    str      ,		_A    :    Optional[int]=7      ,		_A    :    Tuple=3      ,		_A    :    List[str]=4      ,		_A    :    Union[str, Any]=18      ,		_A    :    Any=30      ,		_A    :    Dict=400      ,		_A    :    Dict=True      ,		_A    :    List[str]=None      ,		_A    :    Optional[Any]=True      ,		_A    :    List[Any]=[0.5, 0.5, 0.5]      ,		_A    :    List[str]=[0.5, 0.5, 0.5]      ,		_A    :    Optional[Any]=True      ,		_A    :    int=None      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						size if size is not None else {'''shortest_edge''': 18}
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						parent
       __SCREAMING_SNAKE_CASE     :					int			=						batch_size
       __SCREAMING_SNAKE_CASE     :					Tuple			=						num_channels
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						num_frames
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						image_size
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						min_resolution
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						max_resolution
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						do_resize
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						size
       __SCREAMING_SNAKE_CASE     :					int			=						do_normalize
       __SCREAMING_SNAKE_CASE     :					Tuple			=						image_mean
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image_std
       __SCREAMING_SNAKE_CASE     :					Dict			=						do_center_crop
       __SCREAMING_SNAKE_CASE     :					str			=						crop_size




     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """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,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }






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






     """simple docstring"""







     lowerCAmelCase_     =	TvltImageProcessor if is_vision_available() else None



     def 				UpperCAmelCase__    (							self    :    Any				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						TvltImageProcessorTester(self				)



     @property
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """simple docstring"""




       return self.image_processor_tester.prepare_image_processor_dict()



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						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      ,		'''do_center_crop'''				)				)
       self.assertTrue(hasattr(_A      ,		'''size'''				)				)



     def 				UpperCAmelCase__    (							self    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						self.image_processing_class(**self.image_processor_dict				)
       # create random PIL videos
       __SCREAMING_SNAKE_CASE     :					Tuple			=						prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=_A				)
       for video in video_inputs:
         self.assertIsInstance(_A      ,		_A				)
         self.assertIsInstance(video[0]      ,		Image.Image				)

       # Test not batched input
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image_processor(video_inputs[0]      ,		return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_videos.shape      ,		(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )      ,		)

       # Test batched
       __SCREAMING_SNAKE_CASE     :					List[str]			=						image_processor(_A      ,		return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_videos.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )      ,		)



     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					int			=						self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       __SCREAMING_SNAKE_CASE     :					List[str]			=						prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		numpify=_A				)
       for video in video_inputs:
         self.assertIsInstance(_A      ,		_A				)
         self.assertIsInstance(video[0]      ,		np.ndarray				)

       # Test not batched input
       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						image_processor(video_inputs[0]      ,		return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_videos.shape      ,		(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )      ,		)

       # Test batched
       __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor(_A      ,		return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_videos.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )      ,		)




     def 				UpperCAmelCase__    (							self    :    Tuple				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE     :					Dict			=						prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=_A      ,		torchify=_A				)
       for video in video_inputs:
         self.assertIsInstance(_A      ,		_A				)
         self.assertIsInstance(video[0]      ,		torch.Tensor				)

       # Test not batched input
       __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor(video_inputs[0]      ,		return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_videos.shape      ,		(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )      ,		)

       # Test batched
       __SCREAMING_SNAKE_CASE     :					Dict			=						image_processor(_A      ,		return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_videos.shape      ,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )      ,		)


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"""







     lowerCAmelCase_     =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase_     =	'''LayoutLMv2ImageProcessor'''
     lowerCAmelCase_     =	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')



     def __init__(							self    :    Optional[Any]      ,		_A    :    List[Any]=None      ,		_A    :    List[str]=None      ,		**_A    :    int				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						None
       if "feature_extractor" in kwargs:
         warnings.warn(
             '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
             ''' instead.'''      ,		_A      ,		)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						kwargs.pop('''feature_extractor'''				)

       __SCREAMING_SNAKE_CASE     :					List[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    :    Optional[int]      ,		_A    :    Tuple      ,		_A    :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,		_A    :    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,		_A    :    Union[List[List[int]], List[List[List[int]]]] = None      ,		_A    :    Optional[Union[List[int], List[List[int]]]] = None      ,		_A    :    bool = True      ,		_A    :    Union[bool, str, PaddingStrategy] = False      ,		_A    :    Union[bool, str, TruncationStrategy] = False      ,		_A    :    Optional[int] = None      ,		_A    :    int = 0      ,		_A    :    Optional[int] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    Optional[bool] = None      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = False      ,		_A    :    bool = True      ,		_A    :    Optional[Union[str, TensorType]] = None      ,		**_A    :    Tuple      ,		):







       """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
       __SCREAMING_SNAKE_CASE     :					Optional[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				):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
         __SCREAMING_SNAKE_CASE     :					Tuple			=						features['''words''']

       __SCREAMING_SNAKE_CASE     :					str			=						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
       __SCREAMING_SNAKE_CASE     :					str			=						features.pop('''pixel_values'''				)
       if return_overflowing_tokens is True:
         __SCREAMING_SNAKE_CASE     :					str			=						self.get_overflowing_images(_A      ,		encoded_inputs['''overflow_to_sample_mapping''']				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						images

       return encoded_inputs



     def 				UpperCAmelCase__    (							self    :    List[str]      ,		_A    :    List[Any]      ,		_A    :    Dict				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						[]
       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 				UpperCAmelCase__    (							self    :    int      ,		*_A    :    Dict      ,		**_A    :    Optional[Any]				):







       """simple docstring"""




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



     def 				UpperCAmelCase__    (							self    :    Union[str, Any]      ,		*_A    :    Union[str, Any]      ,		**_A    :    List[str]				):







       """simple docstring"""




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



     @property
     def 				UpperCAmelCase__    (							self    :    Optional[int]				):







       """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.'''      ,		_A      ,		)
       return self.image_processor_class




     @property
     def 				UpperCAmelCase__    (							self    :    List[str]				):







       """simple docstring"""




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