





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        lowercase__ (      unittest.TestCase  ):

			"""simple docstring"""







			def __init__(			self	,      _A	,      _A=7	,      _A=3	,      _A=3_0	,      _A=4_0_0	,      _A=True	,      _A=None	,      _A=True	,      _A=[0.5, 0.5, 0.5]	,      _A=[0.5, 0.5, 0.5]	,      _A=False	,      ):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = size if size is not None else {"height": 3_0, "width": 3_0}
									UpperCamelCase							:		str							      = parent
									UpperCamelCase							:		Tuple							      = batch_size
									UpperCamelCase							:		int							      = num_channels
									UpperCamelCase							:		List[str]							      = min_resolution
									UpperCamelCase							:		int							      = max_resolution
									UpperCamelCase							:		str							      = do_resize
									UpperCamelCase							:		Optional[Any]							      = size
									UpperCamelCase							:		Dict							      = do_normalize
									UpperCamelCase							:		Union[str, Any]							      = image_mean
									UpperCamelCase							:		List[Any]							      = image_std
									UpperCamelCase							:		int							      = do_reduce_labels







			def      _a			(			self						):






									'''simple docstring'''







									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     UpperCamelCase       ():
						UpperCamelCase							:		Optional[int]							      = load_dataset("""hf-internal-testing/fixtures_ade20k"""       ,    split="""test"""    )

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

						return image, map

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

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

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






@require_torch
@require_vision
class        lowercase__ (      _a				,    unittest.TestCase  ):

			"""simple docstring"""
			__lowerCAmelCase    :							List[Any]								=     SegformerImageProcessor if is_vision_available() else None







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		List[Any]							      = SegformerImageProcessingTester(self						)







			@property
			def      _a			(			self						):






									'''simple docstring'''







									return self.image_processor_tester.prepare_image_processor_dict()







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		str							      = self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(lowerCamelCase__	,      """do_resize"""						)						)
									self.assertTrue(hasattr(lowerCamelCase__	,      """size"""						)						)
									self.assertTrue(hasattr(lowerCamelCase__	,      """do_normalize"""						)						)
									self.assertTrue(hasattr(lowerCamelCase__	,      """image_mean"""						)						)
									self.assertTrue(hasattr(lowerCamelCase__	,      """image_std"""						)						)
									self.assertTrue(hasattr(lowerCamelCase__	,      """do_reduce_labels"""						)						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		int							      = 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	,      lowerCamelCase__						)

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







			def      _a			(			self						):






									'''simple docstring'''







									pass







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									UpperCamelCase							:		Dict							      = 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							:		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							:		Any							      = 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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )	,      )







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									UpperCamelCase							:		str							      = 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							:		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							:		str							      = 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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )	,      )







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Tuple							      = self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									UpperCamelCase							:		Optional[Any]							      = 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							:		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
									UpperCamelCase							:		Any							      = 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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )	,      )







			def      _a			(			self						):






									'''simple docstring'''







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

									# Test not batched input
									UpperCamelCase							:		Union[str, 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							:		int							      = 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.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							:		List[Any]							      = prepare_semantic_single_inputs()

									UpperCamelCase							:		Union[str, Any]							      = image_processing(lowerCamelCase__	,      lowerCamelCase__	,      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							:		Any							      = prepare_semantic_batch_inputs()

									UpperCamelCase							:		List[Any]							      = image_processing(lowerCamelCase__	,      lowerCamelCase__	,      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      _a			(			self						):






									'''simple docstring'''







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

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










"""simple docstring"""


def     UpperCamelCase       (SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE    ):
						UpperCamelCase							:		int							      = len(SCREAMING_SNAKE_CASE    )
						UpperCamelCase							:		int							      = len(SCREAMING_SNAKE_CASE    )
						UpperCamelCase							:		int							      = (
						    first_input_list_length
						    if first_input_list_length > second_input_list_length
						    else second_input_list_length
						)
						UpperCamelCase							:		list							      = []
						for char_count in range(SCREAMING_SNAKE_CASE    ):
												if char_count < first_input_list_length:
																		output_result_list.append(first_input_list[char_count]    )
												if char_count < second_input_list_length:
																		output_result_list.append(second_input_list[char_count]    )

						return output_result_list


if __name__ == "__main__":
		print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)



