


'''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 	UpperCamelCase__(       unittest.TestCase	):




    def __init__(		self      :			List[str]				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			List[Any]=7				,   lowerCAmelCase      :			List[str]=3				,   lowerCAmelCase      :			Dict=18				,   lowerCAmelCase      :			Any=30				,   lowerCAmelCase      :			List[str]=400				,   lowerCAmelCase      :			Tuple=True				,   lowerCAmelCase      :			str=None				,   lowerCAmelCase      :			str=True				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			Dict=True				,   lowerCAmelCase      :			Dict=[0.5, 0.5, 0.5]				,   lowerCAmelCase      :			Tuple=[0.5, 0.5, 0.5]				,   lowerCAmelCase      :			Any=False				,   )->					int:

          """simple docstring"""




          UpperCAmelCase					    =     size if size is not None else {'''height''': 20, '''width''': 20}
          UpperCAmelCase					    =     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          UpperCAmelCase					    =     parent
          UpperCAmelCase					    =     batch_size
          UpperCAmelCase					    =     num_channels
          UpperCAmelCase					    =     image_size
          UpperCAmelCase					    =     min_resolution
          UpperCAmelCase					    =     max_resolution
          UpperCAmelCase					    =     do_resize
          UpperCAmelCase					    =     size
          UpperCAmelCase					    =     do_center_crop
          UpperCAmelCase					    =     crop_size
          UpperCAmelCase					    =     do_normalize
          UpperCAmelCase					    =     image_mean
          UpperCAmelCase					    =     image_std
          UpperCAmelCase					    =     do_reduce_labels





    def 					a__(		self      :			Any				)->					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 				lowerCamelCase__				(	):




      '''simple docstring'''






      UpperCAmelCase					    =     load_dataset('''hf-internal-testing/fixtures_ade20k'''				,       split='''test'''					)

      UpperCAmelCase					    =     Image.open(dataset[0]['''file''']					)
      UpperCAmelCase					    =     Image.open(dataset[1]['''file''']					)

      return image, map


def 				lowerCamelCase__				(	):




      '''simple docstring'''






      UpperCAmelCase					    =     load_dataset('''hf-internal-testing/fixtures_ade20k'''				,       split='''test'''					)

      UpperCAmelCase					    =     Image.open(ds[0]['''file''']					)
      UpperCAmelCase					    =     Image.open(ds[1]['''file''']					)
      UpperCAmelCase					    =     Image.open(ds[2]['''file''']					)
      UpperCAmelCase					    =     Image.open(ds[3]['''file''']					)

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






@require_torch
@require_vision
class 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
    __magic_name__    :				List[Any]  		=	BeitImageProcessor if is_vision_available() else None




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

          """simple docstring"""




          UpperCAmelCase					    =     BeitImageProcessingTester(self				)




    @property
    def 					a__(		self      :			Dict				)->					Any:

          """simple docstring"""




          return self.image_processor_tester.prepare_image_processor_dict()




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

          """simple docstring"""




          UpperCAmelCase					    =     self.image_processing_class(**self.image_processor_dict				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''do_resize'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''size'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''do_center_crop'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''center_crop'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''do_normalize'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''image_mean'''				)				)
          self.assertTrue(hasattr(lowerCAmelCase				,   '''image_std'''				)				)




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

          """simple docstring"""




          UpperCAmelCase					    =     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				,   lowerCAmelCase				)

          UpperCAmelCase					    =     self.image_processing_class.from_dict(
              self.image_processor_dict				,   size=42				,   crop_size=84				,   reduce_labels=lowerCAmelCase				)
          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				,   lowerCAmelCase				)




    def 					a__(		self      :			Dict				)->					List[Any]:

          """simple docstring"""




          pass




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

          """simple docstring"""




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

          # Test not batched input
          UpperCAmelCase					    =     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
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   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 					a__(		self      :			Union[str, Any]				)->					Dict:

          """simple docstring"""




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

          # Test not batched input
          UpperCAmelCase					    =     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
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   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 					a__(		self      :			str				)->					Optional[Any]:

          """simple docstring"""




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

          # Test not batched input
          UpperCAmelCase					    =     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
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   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 					a__(		self      :			List[str]				)->					Union[str, Any]:

          """simple docstring"""




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

          # Test not batched input
          UpperCAmelCase					    =     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() <= 255				)

          # Test batched
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   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() <= 255				)

          # Test not batched input (PIL images)
          UpperCAmelCase					    =     prepare_semantic_single_inputs()

          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   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() <= 255				)

          # Test batched input (PIL images)
          UpperCAmelCase					    =     prepare_semantic_batch_inputs()

          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   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() <= 255				)





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

          """simple docstring"""




          UpperCAmelCase					    =     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
          UpperCAmelCase					    =     prepare_semantic_single_inputs()
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   return_tensors='''pt'''				)
          self.assertTrue(encoding['''labels'''].min().item() >= 0				)
          self.assertTrue(encoding['''labels'''].max().item() <= 150				)

          UpperCAmelCase					    =     True
          UpperCAmelCase					    =     image_processing(lowerCAmelCase				,   lowerCAmelCase				,   return_tensors='''pt'''				)
          self.assertTrue(encoding['''labels'''].min().item() >= 0				)
          self.assertTrue(encoding['''labels'''].max().item() <= 255				)









'''simple docstring'''



from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[str]
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="Translation"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




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

										"""simple docstring"""




										return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)





				def 					a__(		self      :			int				)->					Union["FeatureType", Dict[str, "FeatureType"]]:

										"""simple docstring"""




										from .features import Value

										return {k: Value('''string'''				) for k in sorted(self.languages				)}







@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				Optional[List]  		=	None
				__magic_name__    :				Optional[int]  		=	None
				__magic_name__    :				Optional[str]  		=	None
				# Automatically constructed
				__magic_name__    :				ClassVar[str]  		=	"dict"
				__magic_name__    :				ClassVar[Any]  		=	None
				__magic_name__    :				str  		=	field(default="TranslationVariableLanguages"						,    init=lowerCAmelCase						,    repr=lowerCAmelCase	)




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

										"""simple docstring"""




										UpperCAmelCase					    =     sorted(set(self.languages				)				) if self.languages else None
										UpperCAmelCase					    =     len(self.languages				) if self.languages else None




				def __call__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return pa.struct({'''language''': pa.list_(pa.string()				), '''translation''': pa.list_(pa.string()				)}				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     set(self.languages				)
										if self.languages and set(lowerCAmelCase				) - lang_set:
																raise ValueError(
																    F"""Some languages in example ({", ".join(sorted(set(lowerCAmelCase				) - lang_set				)				)}) are not in valid set ({", ".join(lowerCAmelCase				)})."""				)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										UpperCAmelCase					    =     []
										for lang, text in translation_dict.items():
																if isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																						translation_tuples.append((lang, text)				)
																else:
																						translation_tuples.extend([(lang, el) for el in text]				)

        # Ensure translations are in ascending order by language code.
										UpperCAmelCase    ,    UpperCAmelCase					    =     zip(*sorted(lowerCAmelCase				)				)

										return {"language": languages, "translation": translations}





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

										"""simple docstring"""




										from .features import Sequence, Value

										return {
										    "language": Sequence(Value('''string'''				)				),
										    "translation": Sequence(Value('''string'''				)				),
										}





