

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				):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    size if size is not None else {"height": 30, "width": 30}
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    parent
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    batch_size
				SCREAMING_SNAKE_CASE				: List[str]			       =    num_channels
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    min_resolution
				SCREAMING_SNAKE_CASE				: str			       =    max_resolution
				SCREAMING_SNAKE_CASE				: Any			       =    do_resize
				SCREAMING_SNAKE_CASE				: int			       =    size
				SCREAMING_SNAKE_CASE				: Tuple			       =    do_normalize
				SCREAMING_SNAKE_CASE				: Dict			       =    image_mean
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    image_std
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    do_reduce_labels






			def 							__UpperCamelCase	(     self							:	str		)							->    Tuple:

				"""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 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    load_dataset("hf-internal-testing/fixtures_ade20k"							,     split="test")

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

	return image, map

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

	SCREAMING_SNAKE_CASE				: List[Any]			       =    Image.open(dataset[0]["file"])
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    Image.open(dataset[1]["file"])
	SCREAMING_SNAKE_CASE				: Tuple			       =    Image.open(dataset[2]["file"])
	SCREAMING_SNAKE_CASE				: Tuple			       =    Image.open(dataset[3]["file"])

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






@require_torch
@require_vision
class _UpperCamelCase							(							__A							,		unittest.TestCase				):
			'''simple docstring'''







			lowerCamelCase__          =SegformerImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    SegformerImageProcessingTester(self		)
			@property
			def 							__UpperCamelCase	(     self							:	int		)							->    Tuple:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    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 							__UpperCamelCase	(     self							:	Tuple		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size , {"height": 30, "width": 30}		)
				self.assertEqual(image_processor.do_reduce_labels , a		)

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.image_processing_class.from_dict(self.image_processor_dict , size=42 , reduce_labels=a		)
				self.assertEqual(image_processor.size , {"height": 42, "width": 42}		)
				self.assertEqual(image_processor.do_reduce_labels , a		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Tuple:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    prepare_image_inputs(self.image_processor_tester , equal_resolution=a		)
				for image in image_inputs:
					self.assertIsInstance(a , 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				: 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 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE				: List[str]			       =    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
				SCREAMING_SNAKE_CASE				: 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
				SCREAMING_SNAKE_CASE				: str			       =    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 							__UpperCamelCase	(     self							:	List[str]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE				: Tuple			       =    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
				SCREAMING_SNAKE_CASE				: Dict			       =    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				: 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 							__UpperCamelCase	(     self							:	Tuple		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE				: Tuple			       =    prepare_image_inputs(self.image_processor_tester , equal_resolution=a , torchify=a		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    []
				for image in image_inputs:
					self.assertIsInstance(a , torch.Tensor		)
					maps.append(torch.zeros(image.shape[-2:]		).long()		)

				# Test not batched input
				SCREAMING_SNAKE_CASE				: Tuple			       =    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() <= 255		)

				# Test batched
				SCREAMING_SNAKE_CASE				: Tuple			       =    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() <= 255		)

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

				SCREAMING_SNAKE_CASE				: List[str]			       =    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() <= 255		)

				# Test batched input (PIL images)
				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: List[Any]			       =    prepare_semantic_batch_inputs()

				SCREAMING_SNAKE_CASE				: str			       =    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() <= 255		)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, 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       ,SCREAMING_SNAKE_CASE				: List[Any]			       =    prepare_semantic_single_inputs()
				SCREAMING_SNAKE_CASE				: Tuple			       =    image_processing(a , a , return_tensors="pt"		)
				self.assertTrue(encoding["labels"].min().item() >= 0		)
				self.assertTrue(encoding["labels"].max().item() <= 150		)

				SCREAMING_SNAKE_CASE				: List[str]			       =    True
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    image_processing(a , a , return_tensors="pt"		)
				self.assertTrue(encoding["labels"].min().item() >= 0		)
				self.assertTrue(encoding["labels"].max().item() <= 255		)


from __future__ import annotations

from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Dict , a							:	Any , a							:	Any		)							->    None:  # noqa: A002

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    item
				SCREAMING_SNAKE_CASE				: List[Any]			       =    next






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Optional[int]		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Node | None			       =    None
				SCREAMING_SNAKE_CASE				: List[Any]			       =    0
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	Any		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    Node(a , self.head		)
				self.size += 1
			def 							__UpperCamelCase	(     self							:	Dict		)							->    Any:

				"""simple docstring"""

				if self.head is None:
					return None
				else:
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.head.item
					SCREAMING_SNAKE_CASE				: Dict			       =    self.head.next
					self.size -= 1
					return item
			def 							__UpperCamelCase	(     self							:	Any		)							->    bool:

				"""simple docstring"""

				return self.head is None
			def __str__(     self							:	Union[str, Any]		)							->    str:

				"""simple docstring"""

				if self.is_empty():
					return ""
				else:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.head
					SCREAMING_SNAKE_CASE				: str			       =    ""
					SCREAMING_SNAKE_CASE				: list[str]			       =    []
					while iterate:
						item_list.append(str(iterate.item		)		)
						SCREAMING_SNAKE_CASE				: str			       =    iterate.next

					SCREAMING_SNAKE_CASE				: List[str]			       =    " --> ".join(a		)

					return item_str






			def __len__(     self							:	List[Any]		)							->    int:

				"""simple docstring"""

				return self.size