



"""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 SegformerImageProcessor






class 			UpperCAmelCase_						(       unittest.TestCase		):



      def __init__(  self  :			List[Any]  ,			A  :			List[str]  ,			A  :			str=7  ,			A  :			List[Any]=3  ,			A  :			List[Any]=3_0  ,			A  :			List[Any]=4_0_0  ,			A  :			List[str]=True  ,			A  :			List[str]=None  ,			A  :			str=True  ,			A  :			Tuple=[0.5, 0.5, 0.5]  ,			A  :			Dict=[0.5, 0.5, 0.5]  ,			A  :			int=False  ,			):
           _UpperCAmelCase    :			Dict              =					size if size is not None else {"height": 3_0, "width": 3_0}
           _UpperCAmelCase    :			Tuple              =					parent
           _UpperCAmelCase    :			Dict              =					batch_size
           _UpperCAmelCase    :			List[str]              =					num_channels
           _UpperCAmelCase    :			str              =					min_resolution
           _UpperCAmelCase    :			Dict              =					max_resolution
           _UpperCAmelCase    :			Dict              =					do_resize
           _UpperCAmelCase    :			List[Any]              =					size
           _UpperCAmelCase    :			Tuple              =					do_normalize
           _UpperCAmelCase    :			str              =					image_mean
           _UpperCAmelCase    :			Optional[int]              =					image_std
           _UpperCAmelCase    :			Union[str, Any]              =					do_reduce_labels



      def 		snake_case_      (  self  :			Any		):
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_reduce_labels": self.do_reduce_labels,
           }







def 		__snake_case		(   )						->     List[Any]:





     '''simple docstring'''

     _UpperCAmelCase    :			Dict              =					load_dataset("hf-internal-testing/fixtures_ade20k"		,						split="test"       )

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

     return image, map

def 		__snake_case		(   )						->     Dict:





     '''simple docstring'''

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

     _UpperCAmelCase    :			Optional[int]              =					Image.open(dataset[0]["file"]       )
     _UpperCAmelCase    :			int              =					Image.open(dataset[1]["file"]       )
     _UpperCAmelCase    :			Dict              =					Image.open(dataset[2]["file"]       )
     _UpperCAmelCase    :			Optional[int]              =					Image.open(dataset[3]["file"]       )

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






@require_torch
@require_vision
class 			UpperCAmelCase_						(       _UpperCamelCase ,    unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				SegformerImageProcessor if is_vision_available() else None



      def 		snake_case_      (  self  :			Any		):
           _UpperCAmelCase    :			List[str]              =					SegformerImageProcessingTester(self		)



      @property
      def 		snake_case_      (  self  :			Optional[Any]		):
           return self.image_processor_tester.prepare_image_processor_dict()



      def 		snake_case_      (  self  :			List[str]		):
           _UpperCAmelCase    :			Any              =					self.image_processing_class(**self.image_processor_dict		)
           self.assertTrue(hasattr(A  ,			"do_resize"		)		)
           self.assertTrue(hasattr(A  ,			"size"		)		)
           self.assertTrue(hasattr(A  ,			"do_normalize"		)		)
           self.assertTrue(hasattr(A  ,			"image_mean"		)		)
           self.assertTrue(hasattr(A  ,			"image_std"		)		)
           self.assertTrue(hasattr(A  ,			"do_reduce_labels"		)		)



      def 		snake_case_      (  self  :			Optional[Any]		):
           _UpperCAmelCase    :			List[Any]              =					self.image_processing_class.from_dict(self.image_processor_dict		)
           self.assertEqual(image_processor.size  ,			{"height": 3_0, "width": 3_0}		)
           self.assertEqual(image_processor.do_reduce_labels  ,			A		)

           _UpperCAmelCase    :			Optional[int]              =					self.image_processing_class.from_dict(self.image_processor_dict  ,			size=4_2  ,			reduce_labels=A		)
           self.assertEqual(image_processor.size  ,			{"height": 4_2, "width": 4_2}		)
           self.assertEqual(image_processor.do_reduce_labels  ,			A		)



      def 		snake_case_      (  self  :			Any		):
           pass



      def 		snake_case_      (  self  :			Tuple		):
           # Initialize image_processing
           _UpperCAmelCase    :			Dict              =					self.image_processing_class(**self.image_processor_dict		)
           # create random PIL images
           _UpperCAmelCase    :			Dict              =					prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=A		)
           for image in image_inputs:
                self.assertIsInstance(A  ,			Image.Image		)

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

           # Test batched
           _UpperCAmelCase    :			Dict              =					image_processing(A  ,			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.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)



      def 		snake_case_      (  self  :			Optional[Any]		):
           # Initialize image_processing
           _UpperCAmelCase    :			Union[str, Any]              =					self.image_processing_class(**self.image_processor_dict		)
           # create random numpy tensors
           _UpperCAmelCase    :			Optional[Any]              =					prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=A  ,			numpify=A		)
           for image in image_inputs:
                self.assertIsInstance(A  ,			np.ndarray		)

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

           # Test batched
           _UpperCAmelCase    :			Tuple              =					image_processing(A  ,			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.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)



      def 		snake_case_      (  self  :			Optional[Any]		):
           # Initialize image_processing
           _UpperCAmelCase    :			int              =					self.image_processing_class(**self.image_processor_dict		)
           # create random PyTorch tensors
           _UpperCAmelCase    :			List[Any]              =					prepare_image_inputs(self.image_processor_tester  ,			equal_resolution=A  ,			torchify=A		)
           for image in image_inputs:
                self.assertIsInstance(A  ,			torch.Tensor		)

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

           # Test batched
           _UpperCAmelCase    :			Optional[int]              =					image_processing(A  ,			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.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)



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

           # Test not batched input
           _UpperCAmelCase    :			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.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)
           self.assertEqual(
               encoding["labels"].shape  ,			(
                   1,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)
           self.assertEqual(encoding["labels"].dtype  ,			torch.long		)
           self.assertTrue(encoding["labels"].min().item() >= 0		)
           self.assertTrue(encoding["labels"].max().item() <= 2_5_5		)

           # Test batched
           _UpperCAmelCase    :			Any              =					image_processing(A  ,			A  ,			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.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)
           self.assertEqual(
               encoding["labels"].shape  ,			(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)
           self.assertEqual(encoding["labels"].dtype  ,			torch.long		)
           self.assertTrue(encoding["labels"].min().item() >= 0		)
           self.assertTrue(encoding["labels"].max().item() <= 2_5_5		)

           # Test not batched input (PIL images)
           _UpperCAmelCase       , _UpperCAmelCase    :			Optional[Any]              =					prepare_semantic_single_inputs()

           _UpperCAmelCase    :			Optional[int]              =					image_processing(A  ,			A  ,			return_tensors="pt"		)
           self.assertEqual(
               encoding["pixel_values"].shape  ,			(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)
           self.assertEqual(
               encoding["labels"].shape  ,			(
                   1,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)
           self.assertEqual(encoding["labels"].dtype  ,			torch.long		)
           self.assertTrue(encoding["labels"].min().item() >= 0		)
           self.assertTrue(encoding["labels"].max().item() <= 2_5_5		)

           # Test batched input (PIL images)
           _UpperCAmelCase       , _UpperCAmelCase    :			int              =					prepare_semantic_batch_inputs()

           _UpperCAmelCase    :			List[Any]              =					image_processing(A  ,			A  ,			return_tensors="pt"		)
           self.assertEqual(
               encoding["pixel_values"].shape  ,			(
                   2,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)
           self.assertEqual(
               encoding["labels"].shape  ,			(
                   2,
                   self.image_processor_tester.size["height"],
                   self.image_processor_tester.size["width"],
               )  ,			)
           self.assertEqual(encoding["labels"].dtype  ,			torch.long		)
           self.assertTrue(encoding["labels"].min().item() >= 0		)
           self.assertTrue(encoding["labels"].max().item() <= 2_5_5		)



      def 		snake_case_      (  self  :			Dict		):
           # Initialize image_processing
           _UpperCAmelCase    :			Optional[int]              =					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       , _UpperCAmelCase    :			Optional[int]              =					prepare_semantic_single_inputs()
           _UpperCAmelCase    :			Dict              =					image_processing(A  ,			A  ,			return_tensors="pt"		)
           self.assertTrue(encoding["labels"].min().item() >= 0		)
           self.assertTrue(encoding["labels"].max().item() <= 1_5_0		)

           _UpperCAmelCase    :			str              =					True
           _UpperCAmelCase    :			Any              =					image_processing(A  ,			A  ,			return_tensors="pt"		)
           self.assertTrue(encoding["labels"].min().item() >= 0		)
           self.assertTrue(encoding["labels"].max().item() <= 2_5_5		)





"""simple docstring"""




def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     bool:





     '''simple docstring'''

     if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
          raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("Input list must be a non empty list"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 1 and series[0] != 0:
          return True
     _UpperCAmelCase    :			Tuple              =					[]
     _UpperCAmelCase    :			Any              =					len(SCREAMING_SNAKE_CASE__       )
     for i in range(0		,						SCREAMING_SNAKE_CASE__       ):
          if series[i] == 0:
               raise ValueError("Input series cannot have 0 as an element"       )
          rec_series.append(1 / series[i]       )
     _UpperCAmelCase    :			Dict              =					rec_series[1] - rec_series[0]
     for index in range(2		,						SCREAMING_SNAKE_CASE__       ):
          if rec_series[index] - rec_series[index - 1] != common_diff:
               return False
     return True

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     float:





     '''simple docstring'''

     if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
          raise ValueError("Input series is not valid, valid series - [2, 4, 6]"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("Input list must be a non empty list"       )
     _UpperCAmelCase    :			Any              =					0
     for val in series:
          answer += 1 / val
     return len(SCREAMING_SNAKE_CASE__       ) / answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()
