





"""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 EfficientNetImageProcessor

class 		lowercase ( unittest.TestCase ):





      def __init__(	self				,						lowercase				,						lowercase=13				,						lowercase=3				,						lowercase=18				,						lowercase=30				,						lowercase=400				,						lowercase=True				,						lowercase=None				,						lowercase=True				,						lowercase=[0.5, 0.5, 0.5]				,						lowercase=[0.5, 0.5, 0.5]				,						)		->					int:
            lowerCAmelCase  						=	size if size is not None else {"""height""": 18, """width""": 18}
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	num_channels
            lowerCAmelCase  						=	image_size
            lowerCAmelCase  						=	min_resolution
            lowerCAmelCase  						=	max_resolution
            lowerCAmelCase  						=	do_resize
            lowerCAmelCase  						=	size
            lowerCAmelCase  						=	do_normalize
            lowerCAmelCase  						=	image_mean
            lowerCAmelCase  						=	image_std





      def    _snake_case      (	self			)		->					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,
            }







@require_torch
@require_vision
class 		lowercase ( _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				EfficientNetImageProcessor if is_vision_available() else None





      def    _snake_case      (	self			)		->					Any:
            lowerCAmelCase  						=	EfficientNetImageProcessorTester(self			)





      @property
      def    _snake_case      (	self			)		->					Optional[int]:
            return self.image_processor_tester.prepare_image_processor_dict()





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            self.assertTrue(hasattr(lowercase				,						"""image_mean"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_std"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""do_normalize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""do_resize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""size"""			)			)





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict			)
            self.assertEqual(image_processor.size				,						{"""height""": 18, """width""": 18}			)

            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict				,						size=42			)
            self.assertEqual(image_processor.size				,						{"""height""": 42, """width""": 42}			)





      def    _snake_case      (	self			)		->					Optional[Any]:
            # 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=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						Image.Image			)

            # Test not batched input
            lowerCAmelCase  						=	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["""height"""],
                    self.image_processor_tester.size["""width"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						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["""height"""],
                    self.image_processor_tester.size["""width"""],
                )				,						)





      def    _snake_case      (	self			)		->					Any:
            # 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=lowercase				,						numpify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						np.ndarray			)

            # Test not batched input
            lowerCAmelCase  						=	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["""height"""],
                    self.image_processor_tester.size["""width"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						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["""height"""],
                    self.image_processor_tester.size["""width"""],
                )				,						)





      def    _snake_case      (	self			)		->					Tuple:
            # 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=lowercase				,						torchify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						torch.Tensor			)

            # Test not batched input
            lowerCAmelCase  						=	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["""height"""],
                    self.image_processor_tester.size["""width"""],
                )				,						)

            # Test batched
            lowerCAmelCase  						=	image_processing(lowercase				,						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["""height"""],
                    self.image_processor_tester.size["""width"""],
                )				,						)













"""simple docstring"""





SCREAMING_SNAKE_CASE__ =       [0.33, 0.44, 0.55, 0.44, 0.33]
SCREAMING_SNAKE_CASE__ =       3
SCREAMING_SNAKE_CASE__ =       5

# Buffer Space (with Parameters Space)
SCREAMING_SNAKE_CASE__ =       []
SCREAMING_SNAKE_CASE__ =       []

# Machine Time
SCREAMING_SNAKE_CASE__ =       0


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Tuple      ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

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

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

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

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

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

      # Machine Time
      machine_time += 1


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

      # PRNG (Xorshift by George Marsaglia)
      def xorshift(SCREAMING_SNAKE_CASE					:						List[str]							,						SCREAMING_SNAKE_CASE					:						Tuple      ):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x

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

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

            # Modification (Transition Function) - Flow
            lowerCAmelCase  						=	round(float(r * value * (1 - value)      )							,						10      )
            lowerCAmelCase  						=	(machine_time * 0.01 + r * 1.01) % 1 + 3

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

      # Machine Time
      machine_time += 1

      return xorshift(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      ) % 0xff_fff_fff


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

      lowerCAmelCase  						=	K
      lowerCAmelCase  						=	[0] * m
      lowerCAmelCase  						=	0


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

       # Pushing Data (Input)
       import random

       SCREAMING_SNAKE_CASE__ =       random.sample(range(0Xf_f_f_f_f_f_f_f), 100)
       for chunk in message:
              push(chunk)

       # for controlling
       SCREAMING_SNAKE_CASE__ =       ""

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






