




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 YolosImageProcessor







class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__=7     ,		lowerCamelCase__=3     ,		lowerCamelCase__=30     ,		lowerCamelCase__=400     ,		lowerCamelCase__=True     ,		lowerCamelCase__=None     ,		lowerCamelCase__=True     ,		lowerCamelCase__=[0.5, 0.5, 0.5]     ,		lowerCamelCase__=[0.5, 0.5, 0.5]     ,		lowerCamelCase__=True     ,		lowerCamelCase__=1 / 255     ,		lowerCamelCase__=True     ,		)       -> Optional[int]:



							'''simple docstring'''
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							__lowerCamelCase									=       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
							__lowerCamelCase									=       parent
							__lowerCamelCase									=       batch_size
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       min_resolution
							__lowerCamelCase									=       max_resolution
							__lowerCamelCase									=       do_resize
							__lowerCamelCase									=       size
							__lowerCamelCase									=       do_normalize
							__lowerCamelCase									=       image_mean
							__lowerCamelCase									=       image_std
							__lowerCamelCase									=       do_rescale
							__lowerCamelCase									=       rescale_factor
							__lowerCamelCase									=       do_pad


			def  lowercase_       ( self				)       -> 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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__=False				)       -> Optional[Any]:



							'''simple docstring'''
							if not batched:
											__lowerCamelCase									=       image_inputs[0]
											if isinstance(lowerCamelCase__     ,		Image.Image				):
															__lowerCamelCase			,		__lowerCamelCase									=       image.size
											else:
															__lowerCamelCase			,		__lowerCamelCase									=       image.shape[1], image.shape[2]
											if w < h:
															__lowerCamelCase									=       int(self.size['shortest_edge'] * h / w				)
															__lowerCamelCase									=       self.size['shortest_edge']
											elif w > h:
															__lowerCamelCase									=       self.size['shortest_edge']
															__lowerCamelCase									=       int(self.size['shortest_edge'] * w / h				)
											else:
															__lowerCamelCase									=       self.size['shortest_edge']
															__lowerCamelCase									=       self.size['shortest_edge']

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

							return expected_height, expected_width







@require_torch
@require_vision
class        __lowerCAmelCase					(			__magic_name__     ,							unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			YolosImageProcessor if is_vision_available() else None


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       YolosImageProcessingTester(self				)


			@property
			def  lowercase_       ( self				)       -> List[str]:



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


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowerCamelCase__     ,		'image_mean'				)				)
							self.assertTrue(hasattr(lowerCamelCase__     ,		'image_std'				)				)
							self.assertTrue(hasattr(lowerCamelCase__     ,		'do_normalize'				)				)
							self.assertTrue(hasattr(lowerCamelCase__     ,		'do_resize'				)				)
							self.assertTrue(hasattr(lowerCamelCase__     ,		'size'				)				)


			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       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     ,		lowerCamelCase__				)

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


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							pass


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							# Initialize image_processing
							__lowerCamelCase									=       self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							__lowerCamelCase									=       prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=lowerCamelCase__				)
							for image in image_inputs:
											self.assertIsInstance(lowerCamelCase__     ,		Image.Image				)

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

							__lowerCamelCase			,		__lowerCamelCase									=       self.image_processor_tester.get_expected_values(lowerCamelCase__				)

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

							# Test batched
							__lowerCamelCase			,		__lowerCamelCase									=       self.image_processor_tester.get_expected_values(lowerCamelCase__     ,		batched=lowerCamelCase__				)

							__lowerCamelCase									=       image_processing(lowerCamelCase__     ,		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				)       -> Optional[int]:



							'''simple docstring'''
							# Initialize image_processing
							__lowerCamelCase									=       self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							__lowerCamelCase									=       prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=lowerCamelCase__     ,		numpify=lowerCamelCase__				)
							for image in image_inputs:
											self.assertIsInstance(lowerCamelCase__     ,		np.ndarray				)

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

							__lowerCamelCase			,		__lowerCamelCase									=       self.image_processor_tester.get_expected_values(lowerCamelCase__				)

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

							# Test batched
							__lowerCamelCase									=       image_processing(lowerCamelCase__     ,		return_tensors='pt'				).pixel_values

							__lowerCamelCase			,		__lowerCamelCase									=       self.image_processor_tester.get_expected_values(lowerCamelCase__     ,		batched=lowerCamelCase__				)

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


			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							# Initialize image_processing
							__lowerCamelCase									=       self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							__lowerCamelCase									=       prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=lowerCamelCase__     ,		torchify=lowerCamelCase__				)
							for image in image_inputs:
											self.assertIsInstance(lowerCamelCase__     ,		torch.Tensor				)

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

							__lowerCamelCase			,		__lowerCamelCase									=       self.image_processor_tester.get_expected_values(lowerCamelCase__				)

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

							# Test batched
							__lowerCamelCase									=       image_processing(lowerCamelCase__     ,		return_tensors='pt'				).pixel_values

							__lowerCamelCase			,		__lowerCamelCase									=       self.image_processor_tester.get_expected_values(lowerCamelCase__     ,		batched=lowerCamelCase__				)

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


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							# Initialize image_processings
							__lowerCamelCase									=       self.image_processing_class(**self.image_processor_dict				)
							__lowerCamelCase									=       self.image_processing_class(do_resize=lowerCamelCase__     ,		do_normalize=lowerCamelCase__     ,		do_rescale=lowerCamelCase__				)
							# create random PyTorch tensors
							__lowerCamelCase									=       prepare_image_inputs(self.image_processor_tester     ,		equal_resolution=lowerCamelCase__     ,		torchify=lowerCamelCase__				)
							for image in image_inputs:
											self.assertIsInstance(lowerCamelCase__     ,		torch.Tensor				)

							# Test whether the method "pad" and calling the image processor return the same tensors
							__lowerCamelCase									=       image_processing_a.pad(lowerCamelCase__     ,		return_tensors='pt'				)
							__lowerCamelCase									=       image_processing_a(lowerCamelCase__     ,		return_tensors='pt'				)

							self.assertTrue(
							    torch.allclose(encoded_images_with_method['pixel_values']     ,		encoded_images['pixel_values']     ,		atol=1e-4				)				)


			@slow
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							# prepare image and target
							__lowerCamelCase									=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,		'r'				) as f:
											__lowerCamelCase									=       json.loads(f.read()				)

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

							# encode them
							__lowerCamelCase									=       YolosImageProcessor.from_pretrained('hustvl/yolos-small'				)
							__lowerCamelCase									=       image_processing(images=lowerCamelCase__     ,		annotations=lowerCamelCase__     ,		return_tensors='pt'				)

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

							__lowerCamelCase									=       torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,		lowerCamelCase__     ,		atol=1e-4				)				)

							# verify area
							__lowerCamelCase									=       torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,		lowerCamelCase__				)				)
							# verify boxes
							__lowerCamelCase									=       torch.Size([6, 4]				)
							self.assertEqual(encoding['labels'][0]['boxes'].shape     ,		lowerCamelCase__				)
							__lowerCamelCase									=       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,		lowerCamelCase__     ,		atol=1e-3				)				)
							# verify image_id
							__lowerCamelCase									=       torch.tensor([39_769]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,		lowerCamelCase__				)				)
							# verify is_crowd
							__lowerCamelCase									=       torch.tensor([0, 0, 0, 0, 0, 0]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,		lowerCamelCase__				)				)
							# verify class_labels
							__lowerCamelCase									=       torch.tensor([75, 75, 63, 65, 17, 17]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,		lowerCamelCase__				)				)
							# verify orig_size
							__lowerCamelCase									=       torch.tensor([480, 640]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,		lowerCamelCase__				)				)
							# verify size
							__lowerCamelCase									=       torch.tensor([800, 1_066]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,		lowerCamelCase__				)				)






			@slow
			def  lowercase_       ( self				)       -> Optional[Any]:



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

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

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

							# encode them
							__lowerCamelCase									=       YolosImageProcessor(format='coco_panoptic'				)
							__lowerCamelCase									=       image_processing(images=lowerCamelCase__     ,		annotations=lowerCamelCase__     ,		masks_path=lowerCamelCase__     ,		return_tensors='pt'				)

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

							__lowerCamelCase									=       torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,		lowerCamelCase__     ,		atol=1e-4				)				)

							# verify area
							__lowerCamelCase									=       torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,		lowerCamelCase__				)				)
							# verify boxes
							__lowerCamelCase									=       torch.Size([6, 4]				)
							self.assertEqual(encoding['labels'][0]['boxes'].shape     ,		lowerCamelCase__				)
							__lowerCamelCase									=       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,		lowerCamelCase__     ,		atol=1e-3				)				)
							# verify image_id
							__lowerCamelCase									=       torch.tensor([39_769]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,		lowerCamelCase__				)				)
							# verify is_crowd
							__lowerCamelCase									=       torch.tensor([0, 0, 0, 0, 0, 0]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,		lowerCamelCase__				)				)
							# verify class_labels
							__lowerCamelCase									=       torch.tensor([17, 17, 63, 75, 75, 93]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,		lowerCamelCase__				)				)
							# verify masks
							__lowerCamelCase									=       822_873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,		lowerCamelCase__				)
							# verify orig_size
							__lowerCamelCase									=       torch.tensor([480, 640]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,		lowerCamelCase__				)				)
							# verify size
							__lowerCamelCase									=       torch.tensor([800, 1_066]				)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,		lowerCamelCase__				)				)








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__lowerCamelCase									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

    # print entropy
				print(F"""{round(-1 * my_fir_sum ):.1f}""" )

				# two len string
				__lowerCamelCase									=       sum(two_char_strings.values() )
				__lowerCamelCase									=       0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__lowerCamelCase									=       cha + cha
												if sequence in two_char_strings:
																__lowerCamelCase									=       two_char_strings[sequence]
																__lowerCamelCase									=       int(UpperCamelCase__ ) / all_sum
																my_sec_sum += prob * math.loga(UpperCamelCase__ )

    # print second entropy
				print(F"""{round(-1 * my_sec_sum ):.1f}""" )

				# print the difference between them
				print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0					,	len(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()


