
"""simple docstring"""




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor



class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=18   ,       SCREAMING_SNAKE_CASE__=30   ,       SCREAMING_SNAKE_CASE__=4_00   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]   ,       SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]   ,       SCREAMING_SNAKE_CASE__=False   ,       )					->		Union[str, Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




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






def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Any           =   load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,split="""test"""						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   Image.open(dataset[0]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       int           =   Image.open(dataset[1]["""file"""]						)

							return image, map

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       str           =   load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,split="""test"""						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   Image.open(ds[0]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   Image.open(ds[1]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   Image.open(ds[2]["""file"""]						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   Image.open(ds[3]["""file"""]						)

							return [imagea, imagea], [mapa, mapa]



@require_torch
@require_vision
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Union[str, Any]					       =   BeitImageProcessor if is_vision_available() else None



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   BeitImageProcessingTester(self    )



				@property
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_resize"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """size"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_center_crop"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """center_crop"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """do_normalize"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """image_mean"""    )    )
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,       """image_std"""    )    )



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.image_processing_class.from_dict(self.image_processor_dict    )
											self.assertEqual(image_processor.size   ,       {"""height""": 20, """width""": 20}    )
											self.assertEqual(image_processor.crop_size   ,       {"""height""": 18, """width""": 18}    )
											self.assertEqual(image_processor.do_reduce_labels   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.image_processing_class.from_dict(
											    self.image_processor_dict   ,       size=42   ,       crop_size=84   ,       reduce_labels=SCREAMING_SNAKE_CASE__    )
											self.assertEqual(image_processor.size   ,       {"""height""": 42, """width""": 42}    )
											self.assertEqual(image_processor.crop_size   ,       {"""height""": 84, """width""": 84}    )
											self.assertEqual(image_processor.do_reduce_labels   ,       SCREAMING_SNAKE_CASE__    )



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



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=SCREAMING_SNAKE_CASE__    )
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       Image.Image    )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       Tuple           =   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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       int           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )



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



											"""simple docstring"""




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

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Any           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )



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



											"""simple docstring"""




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

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       Union[str, 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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       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.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__						:       List[str]           =   prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=SCREAMING_SNAKE_CASE__   ,       torchify=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,       torch.Tensor    )
																		maps.append(torch.zeros(image.shape[-2:]    ).long()    )

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(image_inputs[0]   ,       maps[0]   ,       return_tensors="""pt"""    )
											self.assertEqual(
											    encoding["""pixel_values"""].shape   ,       (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(
											    encoding["""labels"""].shape   ,       (
											        1,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(encoding["""labels"""].dtype   ,       torch.long    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertEqual(
											    encoding["""pixel_values"""].shape   ,       (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(
											    encoding["""labels"""].shape   ,       (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(encoding["""labels"""].dtype   ,       torch.long    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

											# Test not batched input (PIL images)
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   prepare_semantic_single_inputs()

											SCREAMING_SNAKE_CASE__						:       Dict           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertEqual(
											    encoding["""pixel_values"""].shape   ,       (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(
											    encoding["""labels"""].shape   ,       (
											        1,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(encoding["""labels"""].dtype   ,       torch.long    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )

											# Test batched input (PIL images)
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   prepare_semantic_batch_inputs()

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertEqual(
											    encoding["""pixel_values"""].shape   ,       (
											        2,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(
											    encoding["""labels"""].shape   ,       (
											        2,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )   ,       )
											self.assertEqual(encoding["""labels"""].dtype   ,       torch.long    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.image_processing_class(**self.image_processor_dict    )

											# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Dict           =   prepare_semantic_single_inputs()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 1_50    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   True
											SCREAMING_SNAKE_CASE__						:       int           =   image_processing(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											self.assertTrue(encoding["""labels"""].min().item() >= 0    )
											self.assertTrue(encoding["""labels"""].max().item() <= 2_55    )





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   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				["image_processor", "tokenizer"]
   __A	:		Tuple         =				"LayoutLMv3ImageProcessor"
   __A	:		List[Any]         =				("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






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

       lowercase__ :   int    					=				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__(lowercase_	,						lowercase_  )






   def __call__(      self				:     Dict	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,						lowercase_				:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,						lowercase_				:     Union[List[List[int]], List[List[List[int]]]] = None	,						lowercase_				:     Optional[Union[List[int], List[List[int]]]] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[bool, str, PaddingStrategy] = False	,						lowercase_				:     Union[bool, str, TruncationStrategy] = None	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     int = 0	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						**lowercase_				:     Dict	,						)					->						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."  )

       # first, apply the image processor
       lowercase__ :   Union[str, Any]    					=				self.image_processor(images=lowercase_	,						return_tensors=lowercase_  )

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

       lowercase__ :   Tuple    					=				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=lowercase_	,						add_special_tokens=lowercase_	,						padding=lowercase_	,						truncation=lowercase_	,						max_length=lowercase_	,						stride=lowercase_	,						pad_to_multiple_of=lowercase_	,						return_token_type_ids=lowercase_	,						return_attention_mask=lowercase_	,						return_overflowing_tokens=lowercase_	,						return_special_tokens_mask=lowercase_	,						return_offsets_mapping=lowercase_	,						return_length=lowercase_	,						verbose=lowercase_	,						return_tensors=lowercase_	,						**lowercase_	,						)

       # add pixel values
       lowercase__ :   Optional[int]    					=				features.pop("pixel_values"  )
       if return_overflowing_tokens is True:
           lowercase__ :   Dict    					=				self.get_overflowing_images(lowercase_	,						encoded_inputs["overflow_to_sample_mapping"]  )
       lowercase__ :   str    					=				images

       return encoded_inputs






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

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

       return images_with_overflow






   def       __UpperCamelCase     (      self				:     int	,						*lowercase_				:     Union[str, Any]	,						**lowercase_				:     List[str]  )					->						Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						*lowercase_				:     str	,						**lowercase_				:     int  )					->						Dict:
       return self.tokenizer.decode(*lowercase_	,						**lowercase_  )






   @property
   def       __UpperCamelCase     (      self				:     Any  )					->						Any:
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[Any]:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,						lowercase_	,						)
       return self.image_processor_class







   @property
   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Tuple:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,						lowercase_	,						)
       return self.image_processor



