


'''simple docstring'''

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, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import GLPNImageProcessor





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=7     ,							__UpperCamelCase=3     ,							__UpperCamelCase=18     ,							__UpperCamelCase=30     ,							__UpperCamelCase=400     ,							__UpperCamelCase=True     ,							__UpperCamelCase=32     ,							__UpperCamelCase=True     ,							)       ->    Optional[Any]:
          _a						=		parent
          _a						=		batch_size
          _a						=		num_channels
          _a						=		image_size
          _a						=		min_resolution
          _a						=		max_resolution
          _a						=		do_resize
          _a						=		size_divisor
          _a						=		do_rescale






     def  a_					(			self		)       ->    Any:
          return {
              "do_resize": self.do_resize,
              "size_divisor": self.size_divisor,
              "do_rescale": self.do_rescale,
          }







@require_torch
@require_vision
class 						__SCREAMING_SNAKE_CASE					(      __UpperCAmelCase ,			unittest.TestCase ):
     UpperCAmelCase 					=				GLPNImageProcessor if is_vision_available() else None






     def  a_					(			self		)       ->    str:
          _a						=		GLPNImageProcessingTester(self		)






     @property
     def  a_					(			self		)       ->    List[str]:
          return self.image_processor_tester.prepare_image_processor_dict()






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(UpperCAmelCase_     ,							"do_resize"		)		)
          self.assertTrue(hasattr(UpperCAmelCase_     ,							"size_divisor"		)		)
          self.assertTrue(hasattr(UpperCAmelCase_     ,							"resample"		)		)
          self.assertTrue(hasattr(UpperCAmelCase_     ,							"do_rescale"		)		)






     def  a_					(			self		)       ->    Dict:
          pass






     def  a_					(			self		)       ->    List[Any]:
          # Initialize image_processing
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          _a						=		prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=UpperCAmelCase_		)
          for image in image_inputs:
               self.assertIsInstance(UpperCAmelCase_     ,							Image.Image		)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          _a						=		image_processing(image_inputs[0]     ,							return_tensors="pt"		).pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)






     def  a_					(			self		)       ->    Union[str, Any]:
          # Initialize image_processing
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          _a						=		prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=UpperCAmelCase_     ,							numpify=UpperCAmelCase_		)
          for image in image_inputs:
               self.assertIsInstance(UpperCAmelCase_     ,							np.ndarray		)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          _a						=		image_processing(image_inputs[0]     ,							return_tensors="pt"		).pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)






     def  a_					(			self		)       ->    Optional[Any]:
          # Initialize image_processing
          _a						=		self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          _a						=		prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=UpperCAmelCase_     ,							torchify=UpperCAmelCase_		)
          for image in image_inputs:
               self.assertIsInstance(UpperCAmelCase_     ,							torch.Tensor		)

          # Test not batched input (GLPNImageProcessor doesn't support batching)
          _a						=		image_processing(image_inputs[0]     ,							return_tensors="pt"		).pixel_values
          self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
          self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)





'''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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				['''image_processor''', '''tokenizer''']
     UpperCAmelCase 					=				'''LayoutLMv2ImageProcessor'''
     UpperCAmelCase 					=				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






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

          _a						=		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__(__UpperCamelCase     ,							__UpperCamelCase		)






     def __call__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    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						=		self.image_processor(images=__UpperCamelCase     ,							return_tensors=__UpperCamelCase		)

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

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

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

          return encoded_inputs






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

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

          return images_with_overflow






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Tuple:
          return self.tokenizer.batch_decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    List[Any]:
          return self.tokenizer.decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    int:
          return ["input_ids", "bbox", "attention_mask", "image"]






     @property
     def  a_					(			self		)       ->    Dict:
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor_class






     @property
     def  a_					(			self		)       ->    int:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor

