




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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    ["image_processor", "tokenizer"]
      lowerCAmelCase__	:       Any			=    "LayoutLMv2ImageProcessor"
      lowerCAmelCase__	:       List[Any]			=    ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")







      def __init__(   self    :       List[str]       ,				snake_case    :       int=None       ,				snake_case    :       Optional[Any]=None       ,				**snake_case    :       List[str] ):
          __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.'''       ,				snake_case       ,				)
              __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__(snake_case       ,				snake_case )







      def __call__(   self    :       Optional[int]       ,				snake_case    :       Dict       ,				snake_case    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,				snake_case    :       Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None       ,				snake_case    :       Union[List[List[int]], List[List[List[int]]]] = None       ,				snake_case    :       Optional[Union[List[int], List[List[int]]]] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Union[bool, str, PaddingStrategy] = False       ,				snake_case    :       Union[bool, str, TruncationStrategy] = False       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       int = 0       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       bool = False       ,				snake_case    :       bool = False       ,				snake_case    :       bool = False       ,				snake_case    :       bool = False       ,				snake_case    :       bool = True       ,				snake_case    :       Optional[Union[str, TensorType]] = None       ,				**snake_case    :       Dict       ,				):
          # 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=snake_case       ,				return_tensors=snake_case )

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(snake_case       ,				snake_case ):
                  __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=snake_case       ,				add_special_tokens=snake_case       ,				padding=snake_case       ,				truncation=snake_case       ,				max_length=snake_case       ,				stride=snake_case       ,				pad_to_multiple_of=snake_case       ,				return_token_type_ids=snake_case       ,				return_attention_mask=snake_case       ,				return_overflowing_tokens=snake_case       ,				return_special_tokens_mask=snake_case       ,				return_offsets_mapping=snake_case       ,				return_length=snake_case       ,				verbose=snake_case       ,				return_tensors=snake_case       ,				**snake_case       ,				)

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

          return encoded_inputs







      def 					snake_case		(   self    :       Any       ,				snake_case    :       int       ,				snake_case    :       Optional[int] ):
          # 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(snake_case ) != len(snake_case ):
              raise ValueError(
                  '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                  F" {len(snake_case )} and {len(snake_case )}" )

          return images_with_overflow







      def 					snake_case		(   self    :       int       ,				*snake_case    :       List[Any]       ,				**snake_case    :       Union[str, Any] ):
          return self.tokenizer.batch_decode(*snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Any       ,				*snake_case    :       List[str]       ,				**snake_case    :       str ):
          return self.tokenizer.decode(*snake_case       ,				**snake_case )







      @property
      def 					snake_case		(   self    :       Optional[Any] ):
          return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







      @property
      def 					snake_case		(   self    :       Union[str, Any] ):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,				snake_case       ,				)
          return self.image_processor_class







      @property
      def 					snake_case		(   self    :       Tuple ):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       ,				snake_case       ,				)
          return self.image_processor












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     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=10    ,   lowercase_=10    ,   lowercase_=False    ,   lowercase_=False					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.num_frames					):
        video.append(np.random.randint(2_55    ,   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
        __UpperCamelCase     						=  [Image.fromarray(np.moveaxis(lowercase_    ,   0    ,   -1					)					) for frame in video]

    if torchify:
        __UpperCamelCase     						=  [torch.from_numpy(lowercase_					) for frame in video]

    return video




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=False    ,   lowercase_=False    ,   lowercase_=False					)				->		Tuple:

    """simple docstring"""


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

    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.batch_size					):
        if equal_resolution:
            __UpperCamelCase     						=  __UpperCamelCase     						=  image_processor_tester.max_resolution
        else:
            __UpperCamelCase						,					__UpperCamelCase     						=  np.random.choice(
                np.arange(image_processor_tester.min_resolution    ,   image_processor_tester.max_resolution					)    ,   2					)
            __UpperCamelCase     						=  prepare_video(
                image_processor_tester=lowercase_    ,   width=lowercase_    ,   height=lowercase_    ,   numpify=lowercase_    ,   torchify=lowercase_    ,   )
        video_inputs.append(lowercase_					)

    return video_inputs

class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       int       ,				snake_case    :       int=7       ,				snake_case    :       int=3       ,				snake_case    :       Any=4       ,				snake_case    :       Tuple=18       ,				snake_case    :       Optional[int]=30       ,				snake_case    :       List[Any]=400       ,				snake_case    :       str=True       ,				snake_case    :       str=None       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       str=[0.5, 0.5, 0.5]       ,				snake_case    :       Optional[int]=[0.5, 0.5, 0.5]       ,				snake_case    :       Any=True       ,				snake_case    :       str=None       ,				):
          __UpperCamelCase     						=  size if size is not None else {'''shortest_edge''': 18}
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  num_frames
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  min_resolution
          __UpperCamelCase     						=  max_resolution
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean
          __UpperCamelCase     						=  image_std
          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size







      def 					snake_case		(   self    :       Any ):
          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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    TvltImageProcessor if is_vision_available() else None







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  TvltImageProcessorTester(self )







      @property
      def 					snake_case		(   self    :       Dict ):
          return self.image_processor_tester.prepare_image_processor_dict()







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(snake_case       ,				'''image_mean''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''image_std''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_normalize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_resize''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''do_center_crop''' ) )
          self.assertTrue(hasattr(snake_case       ,				'''size''' ) )







      def 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PIL videos
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				Image.Image )

          # Test not batched input
          __UpperCamelCase     						=  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
          __UpperCamelCase     						=  image_processor(snake_case       ,				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 					snake_case		(   self    :       List[Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				numpify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				np.ndarray )

          # Test not batched input
          __UpperCamelCase     						=  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
          __UpperCamelCase     						=  image_processor(snake_case       ,				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 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          __UpperCamelCase     						=  prepare_video_inputs(self.image_processor_tester       ,				equal_resolution=snake_case       ,				torchify=snake_case )
          for video in video_inputs:
              self.assertIsInstance(snake_case       ,				snake_case )
              self.assertIsInstance(video[0]       ,				torch.Tensor )

          # Test not batched input
          __UpperCamelCase     						=  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
          __UpperCamelCase     						=  image_processor(snake_case       ,				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'''],
              )       ,				)






