
"""simple docstring"""

import unittest

import numpy as np

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 ConvNextImageProcessor






class        _lowercase   (				unittest.TestCase		):




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_=7		, UpperCAmelCase_=3		, UpperCAmelCase_=18		, UpperCAmelCase_=30		, UpperCAmelCase_=400		, UpperCAmelCase_=True		, UpperCAmelCase_=None		, UpperCAmelCase_=0.875		, UpperCAmelCase_=True		, UpperCAmelCase_=[0.5, 0.5, 0.5]		, UpperCAmelCase_=[0.5, 0.5, 0.5]		, )		->							Optional[int]:
			lowerCamelCase     :			Dict		=    size if size is not None else {'shortest_edge': 20}
			lowerCamelCase     :			List[Any]		=    parent
			lowerCamelCase     :			int		=    batch_size
			lowerCamelCase     :			List[str]		=    num_channels
			lowerCamelCase     :			Any		=    image_size
			lowerCamelCase     :			Any		=    min_resolution
			lowerCamelCase     :			int		=    max_resolution
			lowerCamelCase     :			Tuple		=    do_resize
			lowerCamelCase     :			Union[str, Any]		=    size
			lowerCamelCase     :			List[Any]		=    crop_pct
			lowerCamelCase     :			Optional[Any]		=    do_normalize
			lowerCamelCase     :			Optional[Any]		=    image_mean
			lowerCamelCase     :			Optional[Any]		=    image_std




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			return {
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_normalize": self.do_normalize,
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "crop_pct": self.crop_pct,
			}






@require_torch
@require_vision
class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	ConvNextImageProcessor if is_vision_available() else None




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			int		=    ConvNextImageProcessingTester(self					)




	@property
	def 			_UpperCamelCase    ( self					)		->							Dict:
			return self.image_processor_tester.prepare_image_processor_dict()




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase     :			Union[str, Any]		=    self.image_processing_class(**self.image_processor_dict					)
			self.assertTrue(hasattr(UpperCAmelCase_		, 'do_resize'					)					)
			self.assertTrue(hasattr(UpperCAmelCase_		, 'size'					)					)
			self.assertTrue(hasattr(UpperCAmelCase_		, 'crop_pct'					)					)
			self.assertTrue(hasattr(UpperCAmelCase_		, 'do_normalize'					)					)
			self.assertTrue(hasattr(UpperCAmelCase_		, 'image_mean'					)					)
			self.assertTrue(hasattr(UpperCAmelCase_		, 'image_std'					)					)




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			lowerCamelCase     :			List[str]		=    self.image_processing_class.from_dict(self.image_processor_dict					)
			self.assertEqual(image_processor.size		, {'shortest_edge': 20}					)

			lowerCamelCase     :			int		=    self.image_processing_class.from_dict(self.image_processor_dict		, size=42					)
			self.assertEqual(image_processor.size		, {'shortest_edge': 42}					)




	def 			_UpperCamelCase    ( self					)		->							str:
			pass




	def 			_UpperCamelCase    ( self					)		->							str:
			# Initialize image_processing
			lowerCamelCase     :			List[str]		=    self.image_processing_class(**self.image_processor_dict					)
			# create random PIL images
			lowerCamelCase     :			int		=    prepare_image_inputs(self.image_processor_tester		, equal_resolution=UpperCAmelCase_					)
			for image in image_inputs:
					self.assertIsInstance(UpperCAmelCase_		, Image.Image					)

			# Test not batched input
			lowerCamelCase     :			Optional[Any]		=    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.size['shortest_edge'],
			        self.image_processor_tester.size['shortest_edge'],
			    )		, )

			# Test batched
			lowerCamelCase     :			Union[str, Any]		=    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.size['shortest_edge'],
			        self.image_processor_tester.size['shortest_edge'],
			    )		, )




	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			# Initialize image_processing
			lowerCamelCase     :			Any		=    self.image_processing_class(**self.image_processor_dict					)
			# create random numpy tensors
			lowerCamelCase     :			Optional[Any]		=    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
			lowerCamelCase     :			Tuple		=    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.size['shortest_edge'],
			        self.image_processor_tester.size['shortest_edge'],
			    )		, )

			# Test batched
			lowerCamelCase     :			List[Any]		=    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.size['shortest_edge'],
			        self.image_processor_tester.size['shortest_edge'],
			    )		, )




	def 			_UpperCamelCase    ( self					)		->							Dict:
			# Initialize image_processing
			lowerCamelCase     :			Optional[Any]		=    self.image_processing_class(**self.image_processor_dict					)
			# create random PyTorch tensors
			lowerCamelCase     :			int		=    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
			lowerCamelCase     :			int		=    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.size['shortest_edge'],
			        self.image_processor_tester.size['shortest_edge'],
			    )		, )

			# Test batched
			lowerCamelCase     :			int		=    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.size['shortest_edge'],
			        self.image_processor_tester.size['shortest_edge'],
			    )		, )








"""simple docstring"""

import numpy as np
def 		UpperCAmelCase					(		a_,						a_="utf-8",						a_="surrogatepass"			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    bin(int.from_bytes(text.encode(a_,						a_			),						'big'			)			)[2:]
		return bits.zfill(8 * ((len(a_			) + 7) // 8)			)
def 		UpperCAmelCase					(		a_,						a_="utf-8",						a_="surrogatepass"			):





		'''simple docstring'''


		lowerCamelCase     :			List[Any]		=    int(a_,						2			)
		return n.to_bytes((n.bit_length() + 7) // 8,						'big'			).decode(a_,						a_			) or "\0"
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		if size_par + len(a_			) <= 2**size_par - (len(a_			) - 1):
				raise ValueError('size of parity don\'t match with size of data'			)

		lowerCamelCase     :			List[str]		=    []
		lowerCamelCase     :			str		=    []
		lowerCamelCase     :			Any		=    [bin(a_			)[2:] for x in range(1,						size_par + len(a_			) + 1			)]

		# sorted information data for the size of the output data
		lowerCamelCase     :			int		=    []
		# data position template + parity
		lowerCamelCase     :			Union[str, Any]		=    []
		# parity bit counter
		lowerCamelCase     :			Optional[int]		=    0
		# counter position of data bits
		lowerCamelCase     :			Any		=    0

		for x in range(1,						size_par + len(a_			) + 1			):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(a_			) / np.log(2			)).is_integer():
								data_out_gab.append('P'			)
								lowerCamelCase     :			List[Any]		=    qtd_bp + 1
						else:
								data_out_gab.append('D'			)
				else:
						data_out_gab.append('D'			)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]			)
						cont_data += 1
				else:
						data_ord.append(a_			)

    # Calculates parity
		lowerCamelCase     :			Optional[Any]		=    0  # parity bit counter
		for bp in range(1,						size_par + 1			):
				# Bit counter one for a given parity
				lowerCamelCase     :			Union[str, Any]		=    0
				# counter to control the loop reading
				lowerCamelCase     :			Optional[int]		=    0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase     :			Union[str, Any]		=    (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase     :			str		=    '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2			)

				qtd_bp += 1

		# Mount the message
		lowerCamelCase     :			Tuple		=    0  # parity bit counter
		for x in range(0,						size_par + len(a_			)			):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]			)			)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]			)

		return data_out
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    []
		# Parity bit counter
		lowerCamelCase     :			Dict		=    0
		# Counter p data bit reading
		lowerCamelCase     :			int		=    0
		# list of parity received
		lowerCamelCase     :			Optional[int]		=    []
		lowerCamelCase     :			Any		=    []

		for x in range(1,						len(a_			) + 1			):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(a_			) / np.log(2			)).is_integer():
						data_out_gab.append('P'			)
						lowerCamelCase     :			List[Any]		=    qtd_bp + 1
				else:
						data_out_gab.append('D'			)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]			)
				else:
						parity_received.append(data[cont_data]			)
				cont_data += 1

		# -----------calculates the parity with the data
		lowerCamelCase     :			Dict		=    []
		lowerCamelCase     :			Optional[Any]		=    []
		lowerCamelCase     :			Dict		=    [bin(a_			)[2:] for x in range(1,						size_par + len(a_			) + 1			)]

		#  sorted information data for the size of the output data
		lowerCamelCase     :			Any		=    []
		# Data position feedback + parity
		lowerCamelCase     :			List[str]		=    []
		# Parity bit counter
		lowerCamelCase     :			List[str]		=    0
		# Counter p data bit reading
		lowerCamelCase     :			int		=    0

		for x in range(1,						size_par + len(a_			) + 1			):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(a_			) / np.log(2			)).is_integer():
						data_out_gab.append('P'			)
						lowerCamelCase     :			Tuple		=    qtd_bp + 1
				else:
						data_out_gab.append('D'			)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]			)
						cont_data += 1
				else:
						data_ord.append(a_			)

    # Calculates parity
		lowerCamelCase     :			List[str]		=    0  # parity bit counter
		for bp in range(1,						size_par + 1			):
				# Bit counter one for a certain parity
				lowerCamelCase     :			List[Any]		=    0
				# Counter to control loop reading
				lowerCamelCase     :			List[Any]		=    0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase     :			Dict		=    (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase     :			List[Any]		=    '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2			)			)

				qtd_bp += 1

		# Mount the message
		lowerCamelCase     :			Tuple		=    0  # Parity bit counter
		for x in range(0,						size_par + len(a_			)			):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]			)			)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]			)

		lowerCamelCase     :			Union[str, Any]		=    parity_received == parity
		return data_output, ack






