



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    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





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






     def 						__snake_case     (							self			:				int					)->str:
      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 						_lowerCAmelCase    (					):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE							:     int							=						Image.open(dataset[0]["file"]  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						Image.open(dataset[1]["file"]  )

 return image, map






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[Any]							=						load_dataset("hf-internal-testing/fixtures_ade20k"		,						split="test"  )

 __SCREAMING_SNAKE_CASE							:     str							=						Image.open(dataset[0]["file"]  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						Image.open(dataset[1]["file"]  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						Image.open(dataset[2]["file"]  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						Image.open(dataset[3]["file"]  )

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






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      SegformerImageProcessor if is_vision_available() else None





     def 						__snake_case     (							self			:				int					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Any							=						SegformerImageProcessingTester(self					)





     @property
     def 						__snake_case     (							self			:				Optional[int]					)->List[Any]:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 						__snake_case     (							self			:				Union[str, Any]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_resize"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"size"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_normalize"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"image_mean"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"image_std"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_reduce_labels"					)					)





     def 						__snake_case     (							self			:				str					)->Any:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						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  ,	UpperCamelCase					)

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





     def 						__snake_case     (							self			:				List[Any]					)->Any:
      pass





     def 						__snake_case     (							self			:				Union[str, Any]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      __SCREAMING_SNAKE_CASE							:     List[str]							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase					)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase  ,	Image.Image					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     int							=						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
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processing(UpperCamelCase  ,	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			:				Any					)->str:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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
      __SCREAMING_SNAKE_CASE							:     str							=						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
      __SCREAMING_SNAKE_CASE							:     Dict							=						image_processing(UpperCamelCase  ,	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			:				Any					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE							:     str							=						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
      __SCREAMING_SNAKE_CASE							:     int							=						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
      __SCREAMING_SNAKE_CASE							:     Any							=						image_processing(UpperCamelCase  ,	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]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase  ,	torch.Tensor					)
       maps.append(torch.zeros(image.shape[-2:]					).long()					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     List[str]							=						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
      __SCREAMING_SNAKE_CASE							:     int							=						image_processing(UpperCamelCase  ,	UpperCamelCase  ,	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)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						prepare_semantic_single_inputs()

      __SCREAMING_SNAKE_CASE							:     Tuple							=						image_processing(UpperCamelCase  ,	UpperCamelCase  ,	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)
      __SCREAMING_SNAKE_CASE							:     str							=						prepare_semantic_batch_inputs()

      __SCREAMING_SNAKE_CASE							:     Any							=						image_processing(UpperCamelCase  ,	UpperCamelCase  ,	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			:				Any					)->Tuple:
      __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							:     Dict							=						prepare_semantic_single_inputs()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						image_processing(UpperCamelCase  ,	UpperCamelCase  ,	return_tensors="pt"					)
      self.assertTrue(encoding["labels"].min().item() >= 0					)
      self.assertTrue(encoding["labels"].max().item() <= 1_5_0					)

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













'''simple docstring'''







from __future__ import annotations





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      list[int | float] )			->						int | float:
						if len(SCREAMING_SNAKE_CASE__ ) == 0:
												raise ValueError('''find_min() arg is an empty sequence''' )
						UpperCAmelCase_      :   Optional[int]      =	nums[0]
						for num in nums:
												UpperCAmelCase_      :   Tuple      =	min(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
						return min_num


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)





