


"""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 BeitImageProcessor



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=3		,    lowerCAmelCase=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    lowerCAmelCase=False		,    ):






										"""simple docstring"""

										snake_case								=			size if size is not None else {'''height''': 20, '''width''': 20}
										snake_case								=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			num_channels
										snake_case								=			image_size
										snake_case								=			min_resolution
										snake_case								=			max_resolution
										snake_case								=			do_resize
										snake_case								=			size
										snake_case								=			do_center_crop
										snake_case								=			crop_size
										snake_case								=			do_normalize
										snake_case								=			image_mean
										snake_case								=			image_std
										snake_case								=			do_reduce_labels

				def 		snake_case (				self    ):






										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_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__    (			)					->				Any:





						"""simple docstring"""






						snake_case								=			load_dataset('hf-internal-testing/fixtures_ade20k' ,			split='test'					)

						snake_case								=			Image.open(dataset[0]['file']					)
						snake_case								=			Image.open(dataset[1]['file']					)

						return image, map





def    lowerCAmelCase__    (			)					->				Tuple:





						"""simple docstring"""






						snake_case								=			load_dataset('hf-internal-testing/fixtures_ade20k' ,			split='test'					)

						snake_case								=			Image.open(ds[0]['file']					)
						snake_case								=			Image.open(ds[1]['file']					)
						snake_case								=			Image.open(ds[2]['file']					)
						snake_case								=			Image.open(ds[3]['file']					)

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



@require_torch
@require_vision
class   lowerCAmelCase_    (			SCREAMING_SNAKE_CASE_     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						BeitImageProcessor if is_vision_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			BeitImageProcessingTester(self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(UpperCamelCase__		,    'do_resize'    )    )
										self.assertTrue(hasattr(UpperCamelCase__		,    'size'    )    )
										self.assertTrue(hasattr(UpperCamelCase__		,    'do_center_crop'    )    )
										self.assertTrue(hasattr(UpperCamelCase__		,    'center_crop'    )    )
										self.assertTrue(hasattr(UpperCamelCase__		,    'do_normalize'    )    )
										self.assertTrue(hasattr(UpperCamelCase__		,    'image_mean'    )    )
										self.assertTrue(hasattr(UpperCamelCase__		,    'image_std'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,    {'height': 20, 'width': 20}    )
										self.assertEqual(image_processor.crop_size		,    {'height': 18, 'width': 18}    )
										self.assertEqual(image_processor.do_reduce_labels		,    UpperCamelCase__    )

										snake_case								=			self.image_processing_class.from_dict(
										    self.image_processor_dict		,    size=42		,    crop_size=84		,    reduce_labels=UpperCamelCase__    )
										self.assertEqual(image_processor.size		,    {'height': 42, 'width': 42}    )
										self.assertEqual(image_processor.crop_size		,    {'height': 84, 'width': 84}    )
										self.assertEqual(image_processor.do_reduce_labels		,    UpperCamelCase__    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PIL images
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=UpperCamelCase__    )
										for image in image_inputs:
																self.assertIsInstance(UpperCamelCase__		,    Image.Image    )

										# Test not batched input
										snake_case								=			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										snake_case								=			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
										snake_case								=			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										snake_case								=			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
										snake_case								=			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )

										# Test batched
										snake_case								=			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )

				def 		snake_case (				self    ):






										"""simple docstring"""

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

										# Test not batched input
										snake_case								=			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )
										self.assertEqual(
										    encoding['labels'].shape		,    (
										        1,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )
										self.assertEqual(encoding['labels'].dtype		,    torch.long    )
										self.assertTrue(encoding['labels'].min().item() >= 0    )
										self.assertTrue(encoding['labels'].max().item() <= 2_55    )

										# Test batched
										snake_case								=			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.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )
										self.assertEqual(
										    encoding['labels'].shape		,    (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )
										self.assertEqual(encoding['labels'].dtype		,    torch.long    )
										self.assertTrue(encoding['labels'].min().item() >= 0    )
										self.assertTrue(encoding['labels'].max().item() <= 2_55    )

										# Test not batched input (PIL images)
										snake_case								=			prepare_semantic_single_inputs()

										snake_case								=			image_processing(UpperCamelCase__		,    UpperCamelCase__		,    return_tensors='pt'    )
										self.assertEqual(
										    encoding['pixel_values'].shape		,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )
										self.assertEqual(
										    encoding['labels'].shape		,    (
										        1,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )
										self.assertEqual(encoding['labels'].dtype		,    torch.long    )
										self.assertTrue(encoding['labels'].min().item() >= 0    )
										self.assertTrue(encoding['labels'].max().item() <= 2_55    )

										# Test batched input (PIL images)
										snake_case								=			prepare_semantic_batch_inputs()

										snake_case								=			image_processing(UpperCamelCase__		,    UpperCamelCase__		,    return_tensors='pt'    )
										self.assertEqual(
										    encoding['pixel_values'].shape		,    (
										        2,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )
										self.assertEqual(
										    encoding['labels'].shape		,    (
										        2,
										        self.image_processor_tester.crop_size['height'],
										        self.image_processor_tester.crop_size['width'],
										    )		,    )
										self.assertEqual(encoding['labels'].dtype		,    torch.long    )
										self.assertTrue(encoding['labels'].min().item() >= 0    )
										self.assertTrue(encoding['labels'].max().item() <= 2_55    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			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
										snake_case								=			prepare_semantic_single_inputs()
										snake_case								=			image_processing(UpperCamelCase__		,    UpperCamelCase__		,    return_tensors='pt'    )
										self.assertTrue(encoding['labels'].min().item() >= 0    )
										self.assertTrue(encoding['labels'].max().item() <= 1_50    )

										snake_case								=			True
										snake_case								=			image_processing(UpperCamelCase__		,    UpperCamelCase__		,    return_tensors='pt'    )
										self.assertTrue(encoding['labels'].min().item() >= 0    )
										self.assertTrue(encoding['labels'].max().item() <= 2_55    )



"""simple docstring"""
import os
import string
import sys


SCREAMING_SNAKE_CASE__      						=  1 << 8

SCREAMING_SNAKE_CASE__      						=  {
    "tab": ord("\t"),
    "newline": ord("\r"),
    "esc": 27,
    "up": 65 + ARROW_KEY_FLAG,
    "down": 66 + ARROW_KEY_FLAG,
    "right": 67 + ARROW_KEY_FLAG,
    "left": 68 + ARROW_KEY_FLAG,
    "mod_int": 91,
    "undefined": sys.maxsize,
    "interrupt": 3,
    "insert": 50,
    "delete": 51,
    "pg_up": 53,
    "pg_down": 54,
}

SCREAMING_SNAKE_CASE__      						=  KEYMAP["up"]
SCREAMING_SNAKE_CASE__      						=  KEYMAP["left"]

if sys.platform == "win32":
					SCREAMING_SNAKE_CASE__      						=  []
					SCREAMING_SNAKE_CASE__      						=  {
					    b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
					    b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
					    b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
					    b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
					    b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
					    b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
					    b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
					    b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
					}

for i in range(10):
					SCREAMING_SNAKE_CASE__      						=  ord(str(i))





def    lowerCAmelCase__    (			)					->				List[Any]:





						"""simple docstring"""






						if os.name == "nt":
												import msvcrt

												snake_case								=			'mbcs'
												# Flush the keyboard buffer
												while msvcrt.kbhit():
																		msvcrt.getch()
												if len(_UpperCamelCase					) == 0:
																		# Read the keystroke
																		snake_case								=			msvcrt.getch()

																		# If it is a prefix char, get second part
																		if ch in (b"\x00", b"\xe0"):
																								snake_case								=			ch + msvcrt.getch()
																								# Translate actual Win chars to bullet char types
																								try:
																														snake_case								=			chr(WIN_KEYMAP[cha]					)
																														WIN_CH_BUFFER.append(chr(KEYMAP['mod_int']					)					)
																														WIN_CH_BUFFER.append(_UpperCamelCase					)
																														if ord(_UpperCamelCase					) in (
																														    KEYMAP["insert"] - 1 << 9,
																														    KEYMAP["delete"] - 1 << 9,
																														    KEYMAP["pg_up"] - 1 << 9,
																														    KEYMAP["pg_down"] - 1 << 9,
																														):
																																				WIN_CH_BUFFER.append(chr(1_2_6					)					)
																														snake_case								=			chr(KEYMAP['esc']					)
																								except KeyError:
																														snake_case								=			cha[1]
																		else:
																								snake_case								=			ch.decode(_UpperCamelCase					)
												else:
																		snake_case								=			WIN_CH_BUFFER.pop(0					)
						elif os.name == "posix":
												import termios
												import tty

												snake_case								=			sys.stdin.fileno()
												snake_case								=			termios.tcgetattr(_UpperCamelCase					)
												try:
																		tty.setraw(_UpperCamelCase					)
																		snake_case								=			sys.stdin.read(1					)
												finally:
																		termios.tcsetattr(_UpperCamelCase ,			termios.TCSADRAIN ,			_UpperCamelCase					)
						return ch





def    lowerCAmelCase__    (			)					->				Union[str, Any]:





						"""simple docstring"""






						snake_case								=			get_raw_chars()
						if ord(_UpperCamelCase					) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
												return char

						elif ord(_UpperCamelCase					) == KEYMAP["esc"]:
												snake_case								=			get_raw_chars()
												if ord(_UpperCamelCase					) == KEYMAP["mod_int"]:
																		snake_case								=			get_raw_chars()
																		if ord(_UpperCamelCase					) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_UpperCamelCase					) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
																								return chr(ord(_UpperCamelCase					) + ARROW_KEY_FLAG					)
																		else:
																								return KEYMAP["undefined"]
												else:
																		return get_raw_chars()

						else:
												if char in string.printable:
																		return char
												else:
																		return KEYMAP["undefined"]


