

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 ViltImageProcessor



class       lowercase_		(unittest.TestCase   ):



						def __init__( self  :      List[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      List[Any]=7				,       snake_case__  :      List[str]=3				,       snake_case__  :      Optional[Any]=18				,       snake_case__  :      Dict=30				,       snake_case__  :      str=4_00				,       snake_case__  :      List[str]=True				,       snake_case__  :      List[Any]=None				,       snake_case__  :      str=2				,       snake_case__  :      Union[str, Any]=True				,       snake_case__  :      Tuple=[0.5, 0.5, 0.5]				,       snake_case__  :      List[Any]=[0.5, 0.5, 0.5]				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       size if size is not None else {'shortest_edge': 30}
								SCREAMING_SNAKE_CASE_							=       parent
								SCREAMING_SNAKE_CASE_							=       batch_size
								SCREAMING_SNAKE_CASE_							=       num_channels
								SCREAMING_SNAKE_CASE_							=       image_size
								SCREAMING_SNAKE_CASE_							=       min_resolution
								SCREAMING_SNAKE_CASE_							=       max_resolution
								SCREAMING_SNAKE_CASE_							=       do_resize
								SCREAMING_SNAKE_CASE_							=       size
								SCREAMING_SNAKE_CASE_							=       size_divisor
								SCREAMING_SNAKE_CASE_							=       do_normalize
								SCREAMING_SNAKE_CASE_							=       image_mean
								SCREAMING_SNAKE_CASE_							=       image_std



						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "size_divisor": self.size_divisor,
								}






						def   __a				( self  :      List[str]				,       snake_case__  :      Any				,       snake_case__  :      int=False     ):

								"""simple docstring"""

								if not batched:
										SCREAMING_SNAKE_CASE_							=       self.size['shortest_edge']
										SCREAMING_SNAKE_CASE_							=       image_inputs[0]
										if isinstance(snake_case__				,       Image.Image     ):
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       image.size
										else:
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       image.shape[1], image.shape[2]
										SCREAMING_SNAKE_CASE_							=       size / min(snake_case__				,       snake_case__     )
										if h < w:
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       size, scale * w
										else:
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       scale * h, size

										SCREAMING_SNAKE_CASE_							=       int((13_33 / 8_00) * size     )
										if max(snake_case__				,       snake_case__     ) > max_size:
												SCREAMING_SNAKE_CASE_							=       max_size / max(snake_case__				,       snake_case__     )
												SCREAMING_SNAKE_CASE_							=       newh * scale
												SCREAMING_SNAKE_CASE_							=       neww * scale

										SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       int(newh + 0.5     ), int(neww + 0.5     )
										SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       (
										    newh // self.size_divisor * self.size_divisor,
										    neww // self.size_divisor * self.size_divisor,
										)

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

								return expected_height, expected_width



@require_torch
@require_vision
class       lowercase_		(SCREAMING_SNAKE_CASE__       ,  unittest.TestCase   ):
						lowerCAmelCase__ 			=ViltImageProcessor if is_vision_available() else None



						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       ViltImageProcessingTester(self     )



						@property
						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								return self.image_processor_tester.prepare_image_processor_dict()



						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       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__				,       'size'     )     )
								self.assertTrue(hasattr(snake_case__				,       'size_divisor'     )     )



						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class.from_dict(self.image_processor_dict     )
								self.assertEqual(image_processor.size				,       {'shortest_edge': 30}     )

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



						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								pass



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								SCREAMING_SNAKE_CASE_							=       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
								SCREAMING_SNAKE_CASE_							=       image_processing(image_inputs[0]				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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
								SCREAMING_SNAKE_CASE_							=       image_processing(snake_case__				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								SCREAMING_SNAKE_CASE_							=       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
								SCREAMING_SNAKE_CASE_							=       image_processing(image_inputs[0]				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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
								SCREAMING_SNAKE_CASE_							=       image_processing(snake_case__				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE_							=       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
								SCREAMING_SNAKE_CASE_							=       image_processing(image_inputs[0]				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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
								SCREAMING_SNAKE_CASE_							=       image_processing(snake_case__				,       return_tensors='pt'     ).pixel_values

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       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,
								    )				,       )





SCREAMING_SNAKE_CASE:    Optional[Any]											=   [0.33, 0.44, 0.55, 0.44, 0.33]
SCREAMING_SNAKE_CASE:    int											=   3
SCREAMING_SNAKE_CASE:    str											=   5

# Buffer Space (with Parameters Space)
SCREAMING_SNAKE_CASE:    list[float]											=   []
SCREAMING_SNAKE_CASE:    list[float]											=   []

# Machine Time
SCREAMING_SNAKE_CASE:    str											=   0


def 				_a       (				lowerCAmelCase   )->  int:
		global buffer_space, params_space, machine_time, K, m, t

		# Choosing Dynamical Systems (All)
		for key, value in enumerate(lowerCAmelCase   ):
				# Evolution Parameter
				SCREAMING_SNAKE_CASE_							=       float(seed / value   )

				# Control Theory: Orbit Change
				SCREAMING_SNAKE_CASE_							=       (buffer_space[(key + 1) % m] + e) % 1

				# Control Theory: Trajectory Change
				SCREAMING_SNAKE_CASE_							=       (params_space[key] + e) % 1 + 3

				# Modification (Transition Function) - Jumps
				SCREAMING_SNAKE_CASE_							=       round(float(r * value * (1 - value)   )						,	10   )
				SCREAMING_SNAKE_CASE_							=       r  # Saving to Parameters Space

		# Logistic Map
		assert max(lowerCAmelCase   ) < 1
		assert max(lowerCAmelCase   ) < 4

		# Machine Time
		machine_time += 1


def 				_a       (				)->  int:
		global buffer_space, params_space, machine_time, K, m, t

		# PRNG (Xorshift by George Marsaglia)
		def xorshift(lowerCAmelCase						,	lowerCAmelCase   ):
				x ^= y >> 13
				y ^= x << 17
				x ^= y >> 5
				return x

		# Choosing Dynamical Systems (Increment)
		SCREAMING_SNAKE_CASE_							=       machine_time % m

		# Evolution (Time Length)
		for _ in range(0						,	lowerCAmelCase   ):
				# Variables (Position + Parameters)
				SCREAMING_SNAKE_CASE_							=       params_space[key]
				SCREAMING_SNAKE_CASE_							=       buffer_space[key]

				# Modification (Transition Function) - Flow
				SCREAMING_SNAKE_CASE_							=       round(float(r * value * (1 - value)   )						,	10   )
				SCREAMING_SNAKE_CASE_							=       (machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

		# Choosing Chaotic Data
		SCREAMING_SNAKE_CASE_							=       int(buffer_space[(key + 2) % m] * (10**10)   )
		SCREAMING_SNAKE_CASE_							=       int(buffer_space[(key - 2) % m] * (10**10)   )

		# Machine Time
		machine_time += 1

		return xorshift(lowerCAmelCase						,	lowerCAmelCase   ) % 0xFFFF_FFFF


def 				_a       (				)->  Optional[Any]:
		global buffer_space, params_space, machine_time, K, m, t

		SCREAMING_SNAKE_CASE_							=       K
		SCREAMING_SNAKE_CASE_							=       [0] * m
		SCREAMING_SNAKE_CASE_							=       0


if __name__ == "__main__":
						# Initialization
						reset()

						# Pushing Data (Input)
						import random

						SCREAMING_SNAKE_CASE:    List[Any]											=   random.sample(range(0xff_ff_ff_ff), 1_0_0)
						for chunk in message:
												push(chunk)

						# for controlling
						SCREAMING_SNAKE_CASE:    List[str]											=   ''''''

						# Pulling Data (Output)
						while inp in ("e", "E"):
												print(f"""{format(pull(), '#04x')}""")
												print(buffer_space)
												print(params_space)
												SCREAMING_SNAKE_CASE:    List[Any]											=   input('''(e)exit? ''').strip()