





"""simple docstring"""





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 DeformableDetrImageProcessor



class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







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







								"""simple docstring"""
								A_				 =						size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						num_channels
								A_				 =						min_resolution
								A_				 =						max_resolution
								A_				 =						do_resize
								A_				 =						size
								A_				 =						do_normalize
								A_				 =						image_mean
								A_				 =						image_std
								A_				 =						do_rescale
								A_				 =						rescale_factor
								A_				 =						do_pad




			def      lowerCamelCase__     (				self						:					Dict	)      -> Any:







								"""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      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					List[Any]       ,   _snake_case						:					str=False	)      -> Any:







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

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

								return expected_height, expected_width





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




			"""simple docstring"""







			snake_case	      =				DeformableDetrImageProcessor if is_vision_available() else None




			def      lowerCamelCase__     (				self						:					int	)      -> Tuple:







								"""simple docstring"""
								A_				 =						DeformableDetrImageProcessingTester(self	)




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Dict:







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




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







								"""simple docstring"""
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(_snake_case       ,   "image_mean"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "image_std"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_normalize"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_resize"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_rescale"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "do_pad"	)	)
								self.assertTrue(hasattr(_snake_case       ,   "size"	)	)




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						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       ,   _snake_case	)

								A_				 =						self.image_processing_class.from_dict(
								    self.image_processor_dict       ,   size=42       ,   max_size=84       ,   pad_and_return_pixel_mask=_snake_case	)
								self.assertEqual(image_processor.size       ,   {"shortest_edge": 42, "longest_edge": 84}	)
								self.assertEqual(image_processor.do_pad       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> Optional[int]:







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								A_				 =						prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=_snake_case	)
								for image in image_inputs:
													self.assertIsInstance(_snake_case       ,   Image.Image	)

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

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

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

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

								A_				 =						image_processing(_snake_case       ,   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      lowerCamelCase__     (				self						:					Dict	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								A_				 =						prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=_snake_case       ,   numpify=_snake_case	)
								for image in image_inputs:
													self.assertIsInstance(_snake_case       ,   np.ndarray	)

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

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

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

								# Test batched
								A_				 =						image_processing(_snake_case       ,   return_tensors="pt"	).pixel_values

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

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




			def      lowerCamelCase__     (				self						:					str	)      -> int:







								"""simple docstring"""
								A_				 =						self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								A_				 =						prepare_image_inputs(self.image_processor_tester       ,   equal_resolution=_snake_case       ,   torchify=_snake_case	)
								for image in image_inputs:
													self.assertIsInstance(_snake_case       ,   torch.Tensor	)

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

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

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

								# Test batched
								A_				 =						image_processing(_snake_case       ,   return_tensors="pt"	).pixel_values

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

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




			@slow
			def      lowerCamelCase__     (				self						:					Any	)      -> Any:







								"""simple docstring"""
								A_				 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
								with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,   "r"	) as f:
													A_				 =						json.loads(f.read()	)

								A_				 =						{"image_id": 39_769, "annotations": target}

								# encode them
								A_				 =						DeformableDetrImageProcessor()
								A_				 =						image_processing(images=_snake_case       ,   annotations=_snake_case       ,   return_tensors="pt"	)

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

								A_				 =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,   _snake_case       ,   atol=1e-4	)	)

								# verify area
								A_				 =						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,   _snake_case	)	)
								# verify boxes
								A_				 =						torch.Size([6, 4]	)
								self.assertEqual(encoding["labels"][0]["boxes"].shape       ,   _snake_case	)
								A_				 =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,   _snake_case       ,   atol=1e-3	)	)
								# verify image_id
								A_				 =						torch.tensor([39_769]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,   _snake_case	)	)
								# verify is_crowd
								A_				 =						torch.tensor([0, 0, 0, 0, 0, 0]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,   _snake_case	)	)
								# verify class_labels
								A_				 =						torch.tensor([75, 75, 63, 65, 17, 17]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,   _snake_case	)	)
								# verify orig_size
								A_				 =						torch.tensor([480, 640]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,   _snake_case	)	)
								# verify size
								A_				 =						torch.tensor([800, 1_066]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,   _snake_case	)	)






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







								"""simple docstring"""
								A_				 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
								with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,   "r"	) as f:
													A_				 =						json.loads(f.read()	)

								A_				 =						{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

								A_				 =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

								# encode them
								A_				 =						DeformableDetrImageProcessor(format="coco_panoptic"	)
								A_				 =						image_processing(images=_snake_case       ,   annotations=_snake_case       ,   masks_path=_snake_case       ,   return_tensors="pt"	)

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

								A_				 =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]	)
								self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,   _snake_case       ,   atol=1e-4	)	)

								# verify area
								A_				 =						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,   _snake_case	)	)
								# verify boxes
								A_				 =						torch.Size([6, 4]	)
								self.assertEqual(encoding["labels"][0]["boxes"].shape       ,   _snake_case	)
								A_				 =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,   _snake_case       ,   atol=1e-3	)	)
								# verify image_id
								A_				 =						torch.tensor([39_769]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,   _snake_case	)	)
								# verify is_crowd
								A_				 =						torch.tensor([0, 0, 0, 0, 0, 0]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,   _snake_case	)	)
								# verify class_labels
								A_				 =						torch.tensor([17, 17, 63, 75, 75, 93]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,   _snake_case	)	)
								# verify masks
								A_				 =						822_873
								self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,   _snake_case	)
								# verify orig_size
								A_				 =						torch.tensor([480, 640]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,   _snake_case	)	)
								# verify size
								A_				 =						torch.tensor([800, 1_066]	)
								self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,   _snake_case	)	)






"""simple docstring"""





from __future__ import annotations
def 							A_     (__a				,   __a = None				,   __a = None				,   __a = False				,   ):

					'''simple docstring'''





					A_				 =						cipher_alphabet or [chr(__a				) for i in range(97				,   123				)]

					# If the argument is None or the user provided an empty dictionary
					if not frequencies_dict:
										# Frequencies of letters in the english language (how much they show up)
										A_				 =						{
										    "a": 0.08497,
										    "b": 0.01492,
										    "c": 0.02202,
										    "d": 0.04253,
										    "e": 0.11162,
										    "f": 0.02228,
										    "g": 0.02015,
										    "h": 0.06094,
										    "i": 0.07546,
										    "j": 0.00153,
										    "k": 0.01292,
										    "l": 0.04025,
										    "m": 0.02406,
										    "n": 0.06749,
										    "o": 0.07507,
										    "p": 0.01929,
										    "q": 0.00095,
										    "r": 0.07587,
										    "s": 0.06327,
										    "t": 0.09356,
										    "u": 0.02758,
										    "v": 0.00978,
										    "w": 0.02560,
										    "x": 0.00150,
										    "y": 0.01994,
										    "z": 0.00077,
										}
					else:
										# Custom frequencies dictionary
										A_				 =						frequencies_dict

					if not case_sensitive:
										A_				 =						ciphertext.lower()

					# Chi squared statistic values
					A_				 =						{}

					# cycle through all of the shifts
					for shift in range(len(__a				)				):
										A_				 =						""

										# decrypt the message with the shift
										for letter in ciphertext:
															try:
																				# Try to index the letter in the alphabet
																				A_				 =						(alphabet_letters.index(letter.lower()				) - shift) % len(
																				    __a				)
																				decrypted_with_shift += (
																				    alphabet_letters[new_key].upper()
																				    if case_sensitive and letter.isupper()
																				    else alphabet_letters[new_key]
																				)
															except ValueError:
																				# Append the character if it isn't in the alphabet
																				decrypted_with_shift += letter

										A_				 =						0.0

										# Loop through each letter in the decoded message with the shift
										for letter in decrypted_with_shift:
															if case_sensitive:
																				A_				 =						letter.lower()
																				if letter in frequencies:
																									# Get the amount of times the letter occurs in the message
																									A_				 =						decrypted_with_shift.lower().count(__a				)

																									# Get the excepcted amount of times the letter should appear based
																									# on letter frequencies
																									A_				 =						frequencies[letter] * occurrences

																									# Complete the chi squared statistic formula
																									A_				 =						((occurrences - expected) ** 2) / expected

																									# Add the margin of error to the total chi squared statistic
																									chi_squared_statistic += chi_letter_value
															else:
																				if letter.lower() in frequencies:
																									# Get the amount of times the letter occurs in the message
																									A_				 =						decrypted_with_shift.count(__a				)

																									# Get the excepcted amount of times the letter should appear based
																									# on letter frequencies
																									A_				 =						frequencies[letter] * occurrences

																									# Complete the chi squared statistic formula
																									A_				 =						((occurrences - expected) ** 2) / expected

																									# Add the margin of error to the total chi squared statistic
																									chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
										A_				 =						(
										    chi_squared_statistic,
										    decrypted_with_shift,
										)

					# Get the most likely cipher by finding the cipher with the smallest chi squared
					# statistic
					def chi_squared_statistic_values_sorting_key(__a				) -> tuple[float, str]:
										return chi_squared_statistic_values[key]

					A_				 =						min(
					    __a				,   key=__a				,   )

					# Get all the data from the most likely cipher (key, decoded message)
					(
					    (
					    A_
					) ,    (
					    A_
					) ,    
					)				 =						chi_squared_statistic_values[most_likely_cipher]

					# Return the data on the most likely shift
					return (
					    most_likely_cipher,
					    most_likely_cipher_chi_squared_value,
					    decoded_most_likely_cipher,
					)




