
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 DetaImageProcessor


class 				lowercase					(		unittest.TestCase   ):



		def __init__( self				,      A_				,      A_=7				,      A_=3				,      A_=30				,      A_=400				,      A_=True				,      A_=None				,      A_=True				,      A_=[0.5, 0.5, 0.5]				,      A_=[0.5, 0.5, 0.5]				,      A_=True				,      A_=1 / 255				,      A_=True				,      )  ->					int:
				"""simple docstring"""
				# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
				UpperCamelCase		=     size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
				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_rescale
				UpperCamelCase		=     rescale_factor
				UpperCamelCase		=     do_pad



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""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_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_pad": self.do_pad,
				}



		def __UpperCamelCase ( self				,      A_				,      A_=False       )  ->					List[str]:
				"""simple docstring"""
				if not batched:
						UpperCamelCase		=     image_inputs[0]
						if isinstance(A_				,      Image.Image       ):
								UpperCamelCase	,  UpperCamelCase		=     image.size
						else:
								UpperCamelCase	,  UpperCamelCase		=     image.shape[1], image.shape[2]
						if w < h:
								UpperCamelCase		=     int(self.size['shortest_edge'] * h / w       )
								UpperCamelCase		=     self.size['shortest_edge']
						elif w > h:
								UpperCamelCase		=     self.size['shortest_edge']
								UpperCamelCase		=     int(self.size['shortest_edge'] * w / h       )
						else:
								UpperCamelCase		=     self.size['shortest_edge']
								UpperCamelCase		=     self.size['shortest_edge']

				else:
						UpperCamelCase		=     []
						for image in image_inputs:
								UpperCamelCase	,  UpperCamelCase		=     self.get_expected_values([image]       )
								expected_values.append((expected_height, expected_width)       )
						UpperCamelCase		=     max(A_				,      key=lambda A_       : item[0]       )[0]
						UpperCamelCase		=     max(A_				,      key=lambda A_       : item[1]       )[1]

				return expected_height, expected_width






@require_torch
@require_vision
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       Union[str, Any]	     =				DetaImageProcessor if is_vision_available() else None



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     DetaImageProcessingTester(self       )



		@property
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     self.image_processing_class(**self.image_processor_dict       )
				self.assertTrue(hasattr(A_				,      'image_mean'       )       )
				self.assertTrue(hasattr(A_				,      'image_std'       )       )
				self.assertTrue(hasattr(A_				,      'do_normalize'       )       )
				self.assertTrue(hasattr(A_				,      'do_resize'       )       )
				self.assertTrue(hasattr(A_				,      'do_rescale'       )       )
				self.assertTrue(hasattr(A_				,      'do_pad'       )       )
				self.assertTrue(hasattr(A_				,      'size'       )       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.image_processing_class.from_dict(self.image_processor_dict       )
				self.assertEqual(image_processor.size				,      {'shortest_edge': 18, 'longest_edge': 1_333}       )
				self.assertEqual(image_processor.do_pad				,      A_       )



		def __UpperCamelCase ( self       )  ->					Optional[int]:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( 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=A_       )
				for image in image_inputs:
						self.assertIsInstance(A_				,      Image.Image       )

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

				UpperCamelCase	,  UpperCamelCase		=     self.image_processor_tester.get_expected_values(A_       )

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

				# Test batched
				UpperCamelCase	,  UpperCamelCase		=     self.image_processor_tester.get_expected_values(A_				,      batched=A_       )

				UpperCamelCase		=     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,
				        expected_height,
				        expected_width,
				    )				,      )



		def __UpperCamelCase ( 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=A_				,      numpify=A_       )
				for image in image_inputs:
						self.assertIsInstance(A_				,      np.ndarray       )

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

				UpperCamelCase	,  UpperCamelCase		=     self.image_processor_tester.get_expected_values(A_       )

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

				# Test batched
				UpperCamelCase		=     image_processing(A_				,      return_tensors='pt'       ).pixel_values

				UpperCamelCase	,  UpperCamelCase		=     self.image_processor_tester.get_expected_values(A_				,      batched=A_       )

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



		def __UpperCamelCase ( 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=A_				,      torchify=A_       )
				for image in image_inputs:
						self.assertIsInstance(A_				,      torch.Tensor       )

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

				UpperCamelCase	,  UpperCamelCase		=     self.image_processor_tester.get_expected_values(A_       )

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

				# Test batched
				UpperCamelCase		=     image_processing(A_				,      return_tensors='pt'       ).pixel_values

				UpperCamelCase	,  UpperCamelCase		=     self.image_processor_tester.get_expected_values(A_				,      batched=A_       )

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



		@slow
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				# prepare image and target
				UpperCamelCase		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'				,      'r'       ) as f:
						UpperCamelCase		=     json.loads(f.read()       )

				UpperCamelCase		=     {'image_id': 39_769, 'annotations': target}

				# encode them
				UpperCamelCase		=     DetaImageProcessor()
				UpperCamelCase		=     image_processing(images=A_				,      annotations=A_				,      return_tensors='pt'       )

				# verify pixel values
				UpperCamelCase		=     torch.Size([1, 3, 800, 1_066]       )
				self.assertEqual(encoding['pixel_values'].shape				,      A_       )

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

				# verify area
				UpperCamelCase		=     torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,      A_       )       )
				# verify boxes
				UpperCamelCase		=     torch.Size([6, 4]       )
				self.assertEqual(encoding['labels'][0]['boxes'].shape				,      A_       )
				UpperCamelCase		=     torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,      A_				,      atol=1e-3       )       )
				# verify image_id
				UpperCamelCase		=     torch.tensor([39_769]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,      A_       )       )
				# verify is_crowd
				UpperCamelCase		=     torch.tensor([0, 0, 0, 0, 0, 0]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,      A_       )       )
				# verify class_labels
				UpperCamelCase		=     torch.tensor([75, 75, 63, 65, 17, 17]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,      A_       )       )
				# verify orig_size
				UpperCamelCase		=     torch.tensor([480, 640]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,      A_       )       )
				# verify size
				UpperCamelCase		=     torch.tensor([800, 1_066]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,      A_       )       )



		@slow
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				# prepare image, target and masks_path
				UpperCamelCase		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'				,      'r'       ) as f:
						UpperCamelCase		=     json.loads(f.read()       )

				UpperCamelCase		=     {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

				UpperCamelCase		=     pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'       )

				# encode them
				UpperCamelCase		=     DetaImageProcessor(format='coco_panoptic'       )
				UpperCamelCase		=     image_processing(images=A_				,      annotations=A_				,      masks_path=A_				,      return_tensors='pt'       )

				# verify pixel values
				UpperCamelCase		=     torch.Size([1, 3, 800, 1_066]       )
				self.assertEqual(encoding['pixel_values'].shape				,      A_       )

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

				# verify area
				UpperCamelCase		=     torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,      A_       )       )
				# verify boxes
				UpperCamelCase		=     torch.Size([6, 4]       )
				self.assertEqual(encoding['labels'][0]['boxes'].shape				,      A_       )
				UpperCamelCase		=     torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,      A_				,      atol=1e-3       )       )
				# verify image_id
				UpperCamelCase		=     torch.tensor([39_769]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,      A_       )       )
				# verify is_crowd
				UpperCamelCase		=     torch.tensor([0, 0, 0, 0, 0, 0]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,      A_       )       )
				# verify class_labels
				UpperCamelCase		=     torch.tensor([17, 17, 63, 75, 75, 93]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,      A_       )       )
				# verify masks
				UpperCamelCase		=     822_873
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,      A_       )
				# verify orig_size
				UpperCamelCase		=     torch.tensor([480, 640]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,      A_       )       )
				# verify size
				UpperCamelCase		=     torch.tensor([800, 1_066]       )
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,      A_       )       )


from random import randint
from tempfile import TemporaryFile

import numpy as np


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     0
		if start < end:
				UpperCamelCase		=     randint(lowercase    ,       lowercase )
				UpperCamelCase		=     a[end]
				UpperCamelCase		=     a[pivot]
				UpperCamelCase		=     temp

				UpperCamelCase	,  UpperCamelCase		=     _in_place_partition(lowercase    ,       lowercase    ,       lowercase )
				count += _in_place_quick_sort(lowercase    ,       lowercase    ,       p - 1 )
				count += _in_place_quick_sort(lowercase    ,       p + 1    ,       lowercase )
		return count


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     randint(lowercase    ,       lowercase )
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     a[pivot]
		UpperCamelCase		=     temp
		UpperCamelCase		=     start - 1
		for index in range(lowercase    ,       lowercase ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						UpperCamelCase		=     new_pivot_index + 1
						UpperCamelCase		=     a[new_pivot_index]
						UpperCamelCase		=     a[index]
						UpperCamelCase		=     temp

		UpperCamelCase		=     a[new_pivot_index + 1]
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     temp
		return new_pivot_index + 1, count


_UpperCAmelCase					: Union[str, Any]      =       TemporaryFile()
_UpperCAmelCase					: List[Any]      =       100  # 1000 elements are to be sorted


_UpperCAmelCase   ,_UpperCAmelCase					: Any      =       0, 1  # mean and standard deviation
_UpperCAmelCase					: Any      =       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_UpperCAmelCase					: Any      =       np.load(outfile)
_UpperCAmelCase					: str      =       len(M) - 1
_UpperCAmelCase					: List[str]      =       _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)


print(z)
