"""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      _lowercase						(				unittest.TestCase					):

				def __init__(       self    :      Tuple    ,      _lowercase    :      Tuple    ,      _lowercase    :      List[str]=7    ,      _lowercase    :      Dict=3    ,      _lowercase    :      int=3_0    ,      _lowercase    :      List[str]=4_0_0    ,      _lowercase    :      List[Any]=True    ,      _lowercase    :      Tuple=None    ,      _lowercase    :      int=True    ,      _lowercase    :      Union[str, Any]=[0.5, 0.5, 0.5]    ,      _lowercase    :      Optional[Any]=[0.5, 0.5, 0.5]    ,      _lowercase    :      List[str]=False    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						size if size is not None else {"""height""": 3_0, """width""": 3_0}
										UpperCAmelCase__   		=						parent
										UpperCAmelCase__   		=						batch_size
										UpperCAmelCase__   		=						num_channels
										UpperCAmelCase__   		=						min_resolution
										UpperCAmelCase__   		=						max_resolution
										UpperCAmelCase__   		=						do_resize
										UpperCAmelCase__   		=						size
										UpperCAmelCase__   		=						do_normalize
										UpperCAmelCase__   		=						image_mean
										UpperCAmelCase__   		=						image_std
										UpperCAmelCase__   		=						do_reduce_labels

				def     A	(       self    :      List[Any]       ):


										'''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 					a__ (						)->		str:
						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 					a__ (						)->		int:
						UpperCAmelCase__   		=						load_dataset("""hf-internal-testing/fixtures_ade20k"""						,     split="""test"""     )

						UpperCAmelCase__   		=						Image.open(dataset[0]["""file"""]     )
						UpperCAmelCase__   		=						Image.open(dataset[1]["""file"""]     )
						UpperCAmelCase__   		=						Image.open(dataset[2]["""file"""]     )
						UpperCAmelCase__   		=						Image.open(dataset[3]["""file"""]     )

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







@require_torch
@require_vision
class      _lowercase						(				__lowerCamelCase , unittest.TestCase					):
				lowerCamelCase__									=    SegformerImageProcessor if is_vision_available() else None

				def     A	(       self    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						SegformerImageProcessingTester(self       )

				@property
				def     A	(       self    :      Any       ):


										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(_lowercase    ,      """do_resize"""       )       )
										self.assertTrue(hasattr(_lowercase    ,      """size"""       )       )
										self.assertTrue(hasattr(_lowercase    ,      """do_normalize"""       )       )
										self.assertTrue(hasattr(_lowercase    ,      """image_mean"""       )       )
										self.assertTrue(hasattr(_lowercase    ,      """image_std"""       )       )
										self.assertTrue(hasattr(_lowercase    ,      """do_reduce_labels"""       )       )

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						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    ,      _lowercase       )

										UpperCAmelCase__   		=						self.image_processing_class.from_dict(self.image_processor_dict    ,      size=4_2    ,      reduce_labels=_lowercase       )
										self.assertEqual(image_processor.size    ,      {"""height""": 4_2, """width""": 4_2}       )
										self.assertEqual(image_processor.do_reduce_labels    ,      _lowercase       )

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										pass

				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										# Initialize image_processing
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										UpperCAmelCase__   		=						prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=_lowercase       )
										for image in image_inputs:
																self.assertIsInstance(_lowercase    ,      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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )    ,      )

										# Test batched
										UpperCAmelCase__   		=						image_processing(_lowercase    ,      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    :      int       ):


										'''simple docstring'''
										# Initialize image_processing
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										# create random numpy tensors
										UpperCAmelCase__   		=						prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=_lowercase    ,      numpify=_lowercase       )
										for image in image_inputs:
																self.assertIsInstance(_lowercase    ,      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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )    ,      )

										# Test batched
										UpperCAmelCase__   		=						image_processing(_lowercase    ,      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    :      Optional[Any]       ):


										'''simple docstring'''
										# Initialize image_processing
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										# create random PyTorch tensors
										UpperCAmelCase__   		=						prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=_lowercase    ,      torchify=_lowercase       )
										for image in image_inputs:
																self.assertIsInstance(_lowercase    ,      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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )    ,      )

										# Test batched
										UpperCAmelCase__   		=						image_processing(_lowercase    ,      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    :      Union[str, Any]       ):


										'''simple docstring'''
										# Initialize image_processing
										UpperCAmelCase__   		=						self.image_processing_class(**self.image_processor_dict       )
										# create random PyTorch tensors
										UpperCAmelCase__   		=						prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=_lowercase    ,      torchify=_lowercase       )
										UpperCAmelCase__   		=						[]
										for image in image_inputs:
																self.assertIsInstance(_lowercase    ,      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.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__   		=						image_processing(_lowercase    ,      _lowercase    ,      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__   		=						prepare_semantic_single_inputs()

										UpperCAmelCase__   		=						image_processing(_lowercase    ,      _lowercase    ,      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__   		=						prepare_semantic_batch_inputs()

										UpperCAmelCase__   		=						image_processing(_lowercase    ,      _lowercase    ,      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    :      Union[str, Any]       ):


										'''simple docstring'''
										# Initialize image_processing
										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__		, UpperCAmelCase__   		=						prepare_semantic_single_inputs()
										UpperCAmelCase__   		=						image_processing(_lowercase    ,      _lowercase    ,      return_tensors="""pt"""       )
										self.assertTrue(encoding["""labels"""].min().item() >= 0       )
										self.assertTrue(encoding["""labels"""].max().item() <= 1_5_0       )

										UpperCAmelCase__   		=						True
										UpperCAmelCase__   		=						image_processing(_lowercase    ,      _lowercase    ,      return_tensors="""pt"""       )
										self.assertTrue(encoding["""labels"""].min().item() >= 0       )
										self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5       )


"""simple docstring"""







from collections import namedtuple

UpperCamelCase_    :    Any											=						namedtuple("""from_to""", """from_ to""")

UpperCamelCase_    :    Union[str, Any]											=						{
    """millimeter""": """mm""",
    """centimeter""": """cm""",
    """meter""": """m""",
    """kilometer""": """km""",
    """inch""": """in""",
    """inche""": """in""",  # Trailing 's' has been stripped off
    """feet""": """ft""",
    """foot""": """ft""",
    """yard""": """yd""",
    """mile""": """mi""",
}

UpperCamelCase_    :    str											=						{
    """mm""": from_to(0.001, 1000),
    """cm""": from_to(0.01, 100),
    """m""": from_to(1, 1),
    """km""": from_to(1000, 0.001),
    """in""": from_to(0.0254, 39.3701),
    """ft""": from_to(0.3048, 3.2_8084),
    """yd""": from_to(0.9144, 1.0_9361),
    """mi""": from_to(1609.34, 0.0_0062_1371),
}


def 					a__ (						UpperCAmelCase__    :     float						,     UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     str     )->		float:
						UpperCAmelCase__   		=						from_type.lower().rstrip("""s"""     )
						UpperCAmelCase__   		=						TYPE_CONVERSION.get(UpperCAmelCase__						,     UpperCAmelCase__     )
						UpperCAmelCase__   		=						to_type.lower().rstrip("""s"""     )
						UpperCAmelCase__   		=						TYPE_CONVERSION.get(UpperCAmelCase__						,     UpperCAmelCase__     )
						if new_from not in METRIC_CONVERSION:
												UpperCAmelCase__   		=						(
												    f'Invalid \'from_type\' value: {from_type!r}.\n'
												    f'Conversion abbreviations are: {", ".join(UpperCAmelCase__     )}'
												)
												raise ValueError(UpperCAmelCase__     )
						if new_to not in METRIC_CONVERSION:
												UpperCAmelCase__   		=						(
												    f'Invalid \'to_type\' value: {to_type!r}.\n'
												    f'Conversion abbreviations are: {", ".join(UpperCAmelCase__     )}'
												)
												raise ValueError(UpperCAmelCase__     )
						return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
						import doctest

						doctest.testmod()

