





'''simple docstring'''

import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def __init__(							self       :		Any	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Any=7	,			lowerCAmelCase__       :		List[Any]=3	,			lowerCAmelCase__       :		Tuple=18	,			lowerCAmelCase__       :		Tuple=30	,			lowerCAmelCase__       :		Union[str, Any]=400	,			lowerCAmelCase__       :		List[str]=True	,			lowerCAmelCase__       :		Optional[int]=None	,			lowerCAmelCase__       :		int=True	,			)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	size if size is not None else {'''height''': 18, '''width''': 18}
            _UpperCamelCase       =	parent
            _UpperCamelCase       =	batch_size
            _UpperCamelCase       =	num_channels
            _UpperCamelCase       =	image_size
            _UpperCamelCase       =	min_resolution
            _UpperCamelCase       =	max_resolution
            _UpperCamelCase       =	do_resize
            _UpperCamelCase       =	size
            _UpperCamelCase       =	do_normalize







       def snake_case__						(							self       :		Optional[int]					)   ->					Optional[Any]:

            '''simple docstring'''




            return {
                # here we create 2 clusters for the sake of simplicity
                "clusters": np.asarray(
                    [
                        [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                        [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
                    ]					),
                "do_resize": self.do_resize,
                "size": self.size,
                "do_normalize": self.do_normalize,
            }

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






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   ImageGPTImageProcessor if is_vision_available() else None





       def snake_case__						(							self       :		int					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	ImageGPTImageProcessingTester(self					)





       @property
       def snake_case__						(							self       :		Dict					)   ->					Any:

            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()





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

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''clusters'''					)					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''do_resize'''					)					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''size'''					)					)
            self.assertTrue(hasattr(lowerCAmelCase__	,			'''do_normalize'''					)					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size	,			{'''height''': 18, '''width''': 18}					)

            _UpperCamelCase       =	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       :		Tuple					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)
            _UpperCamelCase       =	json.loads(image_processor.to_json_string()					)
            for key, value in self.image_processor_dict.items():
                 if key == "clusters":
                      self.assertTrue(np.array_equal(lowerCAmelCase__	,			obj[key]					)					)
                 else:
                      self.assertEqual(obj[key]	,			lowerCAmelCase__					)





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

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 _UpperCamelCase       =	os.path.join(lowerCAmelCase__	,			'''image_processor.json'''					)
                 image_processor_first.to_json_file(lowerCAmelCase__					)
                 _UpperCamelCase       =	self.image_processing_class.from_json_file(lowerCAmelCase__					).to_dict()

            _UpperCamelCase       =	image_processor_first.to_dict()
            for key, value in image_processor_first.items():
                 if key == "clusters":
                      self.assertTrue(np.array_equal(lowerCAmelCase__	,			image_processor_second[key]					)					)
                 else:
                      self.assertEqual(image_processor_first[key]	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[Any]					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processing_class(**self.image_processor_dict					)

            with tempfile.TemporaryDirectory() as tmpdirname:
                 image_processor_first.save_pretrained(lowerCAmelCase__					)
                 _UpperCamelCase       =	self.image_processing_class.from_pretrained(lowerCAmelCase__					).to_dict()

            _UpperCamelCase       =	image_processor_first.to_dict()
            for key, value in image_processor_first.items():
                 if key == "clusters":
                      self.assertTrue(np.array_equal(lowerCAmelCase__	,			image_processor_second[key]					)					)
                 else:
                      self.assertEqual(image_processor_first[key]	,			lowerCAmelCase__					)







       @unittest.skip('''ImageGPT requires clusters at initialization'''					)
       def snake_case__						(							self       :		List[str]					)   ->					List[Any]:

            '''simple docstring'''




            pass







def  a__    (    )		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	load_dataset('''hf-internal-testing/fixtures_image_utils''', split='''test'''				)

     _UpperCamelCase       =	Image.open(dataset[4]['''file''']				)
     _UpperCamelCase       =	Image.open(dataset[5]['''file''']				)

     _UpperCamelCase       =	[imagea, imagea]

     return images




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






       """simple docstring"""





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

            '''simple docstring'''




            _UpperCamelCase       =	ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''					)

            _UpperCamelCase       =	prepare_images()

            # test non-batched
            _UpperCamelCase       =	image_processing(images[0]	,			return_tensors='''pt'''					)

            self.assertIsInstance(encoding.input_ids	,			torch.LongTensor					)
            self.assertEqual(encoding.input_ids.shape	,			(1, 1024)					)

            _UpperCamelCase       =	[306, 191, 191]
            self.assertEqual(encoding.input_ids[0, :3].tolist()	,			lowerCAmelCase__					)

            # test batched
            _UpperCamelCase       =	image_processing(lowerCAmelCase__	,			return_tensors='''pt'''					)

            self.assertIsInstance(encoding.input_ids	,			torch.LongTensor					)
            self.assertEqual(encoding.input_ids.shape	,			(2, 1024)					)

            _UpperCamelCase       =	[303, 13, 13]
            self.assertEqual(encoding.input_ids[1, -3:].tolist()	,			lowerCAmelCase__					)










'''simple docstring'''

from math import isclose, sqrt







def  a__    (    lowercase :					float, lowercase :					float, lowercase :					float				)		->		tuple[float, float, float]:





     """simple docstring"""

     _UpperCamelCase       =	point_y / 4 / point_x
     _UpperCamelCase       =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
     _UpperCamelCase       =	(1 - normal_gradient * normal_gradient) / (
         1 + normal_gradient * normal_gradient
     )
     _UpperCamelCase       =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

     # to find the next point, solve the simultaeneous equations:
     # y^2 + 4x^2 = 100
     # y - b = m * (x - a)
     # ==> A x^2 + B x + C = 0
     _UpperCamelCase       =	outgoing_gradient**2 + 4
     _UpperCamelCase       =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
     _UpperCamelCase       =	(point_y - outgoing_gradient * point_x) ** 2 - 100

     _UpperCamelCase       =	(
         -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term				)
     ) / (2 * quadratic_term)
     _UpperCamelCase       =	(
         -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term				)
     ) / (2 * quadratic_term)

     # two solutions, one of which is our input point
     _UpperCamelCase       =	x_minus if isclose(lowercase, lowercase				) else x_plus
     _UpperCamelCase       =	point_y + outgoing_gradient * (next_x - point_x)

     return next_x, next_y, outgoing_gradient







def  a__    (    lowercase :					float = 1.4, lowercase :					float = -9.6				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	0
     _UpperCamelCase       =	first_x_coord
     _UpperCamelCase       =	first_y_coord
     _UpperCamelCase       =	(1_0.1 - point_y) / (0.0 - point_x)

     while not (-0.0_1 <= point_x <= 0.0_1 and point_y > 0):
          _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	next_point(lowercase, lowercase, lowercase				)
          num_reflections += 1

     return num_reflections


if __name__ == "__main__":
   print(F"""{solution() = }""")



