





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor
class 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):
				"""simple docstring"""




				def __init__(     self  :Dict						,    __lowercase  :List[str]						,    __lowercase  :Dict=7						,    __lowercase  :List[Any]=3						,    __lowercase  :Any=30						,    __lowercase  :Any=400						,    __lowercase  :Any=True						,    __lowercase  :Tuple=None						,    __lowercase  :Union[str, Any]=True						,    __lowercase  :Optional[Any]=[0.5, 0.5, 0.5]						,    __lowercase  :List[str]=[0.5, 0.5, 0.5]						,    __lowercase  :Optional[int]=True						,    __lowercase  :Tuple=1 / 255						,    __lowercase  :Union[str, Any]=True						,    ):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											__lowerCamelCase      :      Dict         =size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
											__lowerCamelCase      :      int         =parent
											__lowerCamelCase      :      Dict         =batch_size
											__lowerCamelCase      :      Union[str, Any]         =num_channels
											__lowerCamelCase      :      Optional[Any]         =min_resolution
											__lowerCamelCase      :      List[str]         =max_resolution
											__lowerCamelCase      :      Union[str, Any]         =do_resize
											__lowerCamelCase      :      Optional[int]         =size
											__lowerCamelCase      :      Optional[Any]         =do_normalize
											__lowerCamelCase      :      Optional[int]         =image_mean
											__lowerCamelCase      :      List[str]         =image_std
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[str]         =rescale_factor
											__lowerCamelCase      :      Optional[Any]         =do_pad




				def 					__lowercase  (     self  :Any			):
											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_rescale": self.do_rescale,
											    "rescale_factor": self.rescale_factor,
											    "do_pad": self.do_pad,
											}




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :List[Any]						,    __lowercase  :List[str]=False			):
											if not batched:
																		__lowerCamelCase      :      Optional[int]         =image_inputs[0]
																		if isinstance(__lowercase						,    Image.Image			):
																									__lowerCamelCase      :      Optional[int]         =image.size
																		else:
																									__lowerCamelCase      :      Optional[int]         =image.shape[1], image.shape[2]
																		if w < h:
																									__lowerCamelCase      :      Union[str, Any]         =int(self.size['''shortest_edge'''] * h / w			)
																									__lowerCamelCase      :      int         =self.size['''shortest_edge''']
																		elif w > h:
																									__lowerCamelCase      :      Union[str, Any]         =self.size['''shortest_edge''']
																									__lowerCamelCase      :      int         =int(self.size['''shortest_edge'''] * w / h			)
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =self.size['''shortest_edge''']
																									__lowerCamelCase      :      Optional[int]         =self.size['''shortest_edge''']

											else:
																		__lowerCamelCase      :      Tuple         =[]
																		for image in image_inputs:
																									__lowerCamelCase      :      Tuple         =self.get_expected_values([image]			)
																									expected_values.append((expected_height, expected_width)			)
																		__lowerCamelCase      :      Union[str, Any]         =max(__lowercase						,    key=lambda __lowercase			: item[0]			)[0]
																		__lowerCamelCase      :      Union[str, Any]         =max(__lowercase						,    key=lambda __lowercase			: item[1]			)[1]

											return expected_height, expected_width

@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
				"""simple docstring"""
				__snake_case   :       Any	 =		ConditionalDetrImageProcessor if is_vision_available() else None




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      List[Any]         =ConditionalDetrImageProcessingTester(self			)




				@property
				def 					__lowercase  (     self  :Union[str, Any]			):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      Union[str, Any]         =self.image_processing_class(**self.image_processor_dict			)
											self.assertTrue(hasattr(__lowercase						,    '''image_mean'''			)			)
											self.assertTrue(hasattr(__lowercase						,    '''image_std'''			)			)
											self.assertTrue(hasattr(__lowercase						,    '''do_normalize'''			)			)
											self.assertTrue(hasattr(__lowercase						,    '''do_resize'''			)			)
											self.assertTrue(hasattr(__lowercase						,    '''size'''			)			)




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Optional[Any]         =self.image_processing_class.from_dict(self.image_processor_dict			)
											self.assertEqual(image_processor.size						,    {'''shortest_edge''': 18, '''longest_edge''': 1333}			)
											self.assertEqual(image_processor.do_pad						,    __lowercase			)

											__lowerCamelCase      :      str         =self.image_processing_class.from_dict(
											    self.image_processor_dict						,    size=42						,    max_size=84						,    pad_and_return_pixel_mask=__lowercase			)
											self.assertEqual(image_processor.size						,    {'''shortest_edge''': 42, '''longest_edge''': 84}			)
											self.assertEqual(image_processor.do_pad						,    __lowercase			)




				def 					__lowercase  (     self  :Optional[int]			):
											pass




				def 					__lowercase  (     self  :List[Any]			):
											# Initialize image_processing
											__lowerCamelCase      :      List[Any]         =self.image_processing_class(**self.image_processor_dict			)
											# create random PIL images
											__lowerCamelCase      :      List[Any]         =prepare_image_inputs(self.image_processor_tester						,    equal_resolution=__lowercase			)
											for image in image_inputs:
																		self.assertIsInstance(__lowercase						,    Image.Image			)

											# Test not batched input
											__lowerCamelCase      :      Any         =image_processing(image_inputs[0]						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase      :      List[Any]         =self.image_processor_tester.get_expected_values(__lowercase			)

											self.assertEqual(
											    encoded_images.shape						,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,    )

											# Test batched
											__lowerCamelCase      :      int         =self.image_processor_tester.get_expected_values(__lowercase						,    batched=__lowercase			)

											__lowerCamelCase      :      Any         =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,
											        expected_height,
											        expected_width,
											    )						,    )




				def 					__lowercase  (     self  :List[Any]			):
											# Initialize image_processing
											__lowerCamelCase      :      int         =self.image_processing_class(**self.image_processor_dict			)
											# create random numpy tensors
											__lowerCamelCase      :      int         =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
											__lowerCamelCase      :      List[Any]         =image_processing(image_inputs[0]						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase      :      Optional[int]         =self.image_processor_tester.get_expected_values(__lowercase			)

											self.assertEqual(
											    encoded_images.shape						,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,    )

											# Test batched
											__lowerCamelCase      :      List[str]         =image_processing(__lowercase						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase      :      Optional[int]         =self.image_processor_tester.get_expected_values(__lowercase						,    batched=__lowercase			)

											self.assertEqual(
											    encoded_images.shape						,    (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						,    )




				def 					__lowercase  (     self  :Tuple			):
											# Initialize image_processing
											__lowerCamelCase      :      Tuple         =self.image_processing_class(**self.image_processor_dict			)
											# create random PyTorch tensors
											__lowerCamelCase      :      List[str]         =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
											__lowerCamelCase      :      str         =image_processing(image_inputs[0]						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase      :      Union[str, Any]         =self.image_processor_tester.get_expected_values(__lowercase			)

											self.assertEqual(
											    encoded_images.shape						,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,    )

											# Test batched
											__lowerCamelCase      :      int         =image_processing(__lowercase						,    return_tensors='''pt'''			).pixel_values

											__lowerCamelCase      :      List[str]         =self.image_processor_tester.get_expected_values(__lowercase						,    batched=__lowercase			)

											self.assertEqual(
											    encoded_images.shape						,    (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )						,    )




				@slow
				def 					__lowercase  (     self  :Any			):
											# prepare image and target
											__lowerCamelCase      :      List[Any]         =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
											with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''						,    '''r'''			) as f:
																		__lowerCamelCase      :      List[str]         =json.loads(f.read()			)

											__lowerCamelCase      :      Optional[int]         ={'''image_id''': 3_9769, '''annotations''': target}

											# encode them
											__lowerCamelCase      :      Optional[int]         =ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''			)
											__lowerCamelCase      :      Optional[int]         =image_processing(images=__lowercase						,    annotations=__lowercase						,    return_tensors='''pt'''			)

											# verify pixel values
											__lowerCamelCase      :      List[Any]         =torch.Size([1, 3, 800, 1066]			)
											self.assertEqual(encoding['''pixel_values'''].shape						,    __lowercase			)

											__lowerCamelCase      :      List[Any]         =torch.tensor([0.2796, 0.3138, 0.3481]			)
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,    __lowercase						,    atol=1e-4			)			)

											# verify area
											__lowerCamelCase      :      Dict         =torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,    __lowercase			)			)
											# verify boxes
											__lowerCamelCase      :      Any         =torch.Size([6, 4]			)
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,    __lowercase			)
											__lowerCamelCase      :      List[str]         =torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,    __lowercase						,    atol=1e-3			)			)
											# verify image_id
											__lowerCamelCase      :      int         =torch.tensor([3_9769]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,    __lowercase			)			)
											# verify is_crowd
											__lowerCamelCase      :      Dict         =torch.tensor([0, 0, 0, 0, 0, 0]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,    __lowercase			)			)
											# verify class_labels
											__lowerCamelCase      :      Optional[int]         =torch.tensor([75, 75, 63, 65, 17, 17]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,    __lowercase			)			)
											# verify orig_size
											__lowerCamelCase      :      Optional[Any]         =torch.tensor([480, 640]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,    __lowercase			)			)
											# verify size
											__lowerCamelCase      :      Any         =torch.tensor([800, 1066]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,    __lowercase			)			)




				@slow
				def 					__lowercase  (     self  :Tuple			):
											# prepare image, target and masks_path
											__lowerCamelCase      :      Tuple         =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
											with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''						,    '''r'''			) as f:
																		__lowerCamelCase      :      Tuple         =json.loads(f.read()			)

											__lowerCamelCase      :      Union[str, Any]         ={'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

											__lowerCamelCase      :      Union[str, Any]         =pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

											# encode them
											__lowerCamelCase      :      Tuple         =ConditionalDetrImageProcessor(format='''coco_panoptic'''			)
											__lowerCamelCase      :      Optional[Any]         =image_processing(images=__lowercase						,    annotations=__lowercase						,    masks_path=__lowercase						,    return_tensors='''pt'''			)

											# verify pixel values
											__lowerCamelCase      :      Optional[Any]         =torch.Size([1, 3, 800, 1066]			)
											self.assertEqual(encoding['''pixel_values'''].shape						,    __lowercase			)

											__lowerCamelCase      :      Tuple         =torch.tensor([0.2796, 0.3138, 0.3481]			)
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,    __lowercase						,    atol=1e-4			)			)

											# verify area
											__lowerCamelCase      :      List[Any]         =torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,    __lowercase			)			)
											# verify boxes
											__lowerCamelCase      :      List[str]         =torch.Size([6, 4]			)
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,    __lowercase			)
											__lowerCamelCase      :      int         =torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,    __lowercase						,    atol=1e-3			)			)
											# verify image_id
											__lowerCamelCase      :      Any         =torch.tensor([3_9769]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,    __lowercase			)			)
											# verify is_crowd
											__lowerCamelCase      :      Tuple         =torch.tensor([0, 0, 0, 0, 0, 0]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,    __lowercase			)			)
											# verify class_labels
											__lowerCamelCase      :      str         =torch.tensor([17, 17, 63, 75, 75, 93]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,    __lowercase			)			)
											# verify masks
											__lowerCamelCase      :      Dict         =82_2873
											self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,    __lowercase			)
											# verify orig_size
											__lowerCamelCase      :      Union[str, Any]         =torch.tensor([480, 640]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,    __lowercase			)			)
											# verify size
											__lowerCamelCase      :      Any         =torch.tensor([800, 1066]			)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,    __lowercase			)			)











"""simple docstring"""





import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


_UpperCamelCase							=      False
try:
							_UpperCamelCase							=      _is_package_available('google.colab')
except ModuleNotFoundError:
							pass
@input.register
class 		SCREAMING_SNAKE_CASE_							:
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :str = None						,    __lowercase  :list = []			):
											__lowerCamelCase      :      Any         =0
											__lowerCamelCase      :      List[str]         =choices
											__lowerCamelCase      :      int         =prompt
											if sys.platform == "win32":
																		__lowerCamelCase      :      Dict         ='''*'''
											else:
																		__lowerCamelCase      :      Union[str, Any]         ='''➔ '''




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Any						,    __lowercase  :str = ""			):
											if sys.platform != "win32":
																		writeColor(self.choices[index]						,    32						,    __lowercase			)
											else:
																		forceWrite(self.choices[index]						,    __lowercase			)




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :int			):
											if index == self.position:
																		forceWrite(f' {self.arrow_char} '			)
																		self.write_choice(__lowercase			)
											else:
																		forceWrite(f'    {self.choices[index]}'			)
											reset_cursor()




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Direction						,    __lowercase  :int = 1			):
											__lowerCamelCase      :      List[str]         =self.position
											if direction == Direction.DOWN:
																		if self.position + 1 >= len(self.choices			):
																									return
																		self.position += num_spaces
											else:
																		if self.position - 1 < 0:
																									return
																		self.position -= num_spaces
											clear_line()
											self.print_choice(__lowercase			)
											move_cursor(__lowercase						,    direction.name			)
											self.print_choice(self.position			)




				@input.mark(KEYMAP['''up''']			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											self.move_direction(Direction.UP			)




				@input.mark(KEYMAP['''down''']			)
				def 					__lowercase  (     self  :Union[str, Any]			):
											self.move_direction(Direction.DOWN			)




				@input.mark(KEYMAP['''newline''']			)
				def 					__lowercase  (     self  :Any			):
											move_cursor(len(self.choices			) - self.position						,    '''DOWN'''			)
											return self.position




				@input.mark(KEYMAP['''interrupt''']			)
				def 					__lowercase  (     self  :Any			):
											move_cursor(len(self.choices			) - self.position						,    '''DOWN'''			)
											raise KeyboardInterrupt




				@input.mark_multiple(*[KEYMAP[str(__lowercase			)] for number in range(10			)]			)
				def 					__lowercase  (     self  :Any			):
											__lowerCamelCase      :      Tuple         =int(chr(self.current_selection			)			)
											__lowerCamelCase      :      Dict         =index - self.position
											if index == self.position:
																		return
											if index < len(self.choices			):
																		if self.position > index:
																									self.move_direction(Direction.UP						,    -movement			)
																		elif self.position < index:
																									self.move_direction(Direction.DOWN						,    __lowercase			)
																		else:
																									return
											else:
																		return




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :int = 0			):
											if self.prompt:
																		linebreak()
																		forceWrite(self.prompt						,    '''\n'''			)
																		if in_colab:
																									forceWrite('''Please input a choice index (starting from 0), and press enter'''						,    '''\n'''			)
																		else:
																									forceWrite('''Please select a choice using the arrow or number keys, and selecting with enter'''						,    '''\n'''			)
											__lowerCamelCase      :      Union[str, Any]         =default_choice
											for i in range(len(self.choices			)			):
																		self.print_choice(__lowercase			)
																		forceWrite('''\n'''			)
											move_cursor(len(self.choices			) - self.position						,    '''UP'''			)
											with cursor.hide():
																		while True:
																									if in_colab:
																																try:
																																							__lowerCamelCase      :      Union[str, Any]         =int(builtins.input()			)
																																except ValueError:
																																							__lowerCamelCase      :      Optional[Any]         =default_choice
																									else:
																																__lowerCamelCase      :      Dict         =self.handle_input()
																									if choice is not None:
																																reset_cursor()
																																for _ in range(len(self.choices			) + 1			):
																																							move_cursor(1						,    '''UP'''			)
																																							clear_line()
																																self.write_choice(__lowercase						,    '''\n'''			)
																																return choice




