




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     __a(	unittest.TestCase  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=18  ,_SCREAMING_SNAKE_CASE=30  ,_SCREAMING_SNAKE_CASE=400  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=True  ,)					->						Dict:
         UpperCAmelCase_     :				Optional[int]  =     size if size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_     :				List[str]  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				Dict  =     num_channels
         UpperCAmelCase_     :				Tuple  =     image_size
         UpperCAmelCase_     :				List[str]  =     min_resolution
         UpperCAmelCase_     :				Any  =     max_resolution
         UpperCAmelCase_     :				Optional[int]  =     do_resize
         UpperCAmelCase_     :				Any  =     size
         UpperCAmelCase_     :				str  =     do_normalize





    def 				a__ ( self    )					->						Optional[int]:
         return {
             # here we create 2 clusters for the sake of simplicity
             "clusters": np.asarray(
                 [
                     [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                     [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                 ]    ),
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
         }


@require_torch
@require_vision
class     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = ImageGPTImageProcessor if is_vision_available() else None





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				Dict  =     ImageGPTImageProcessingTester(self    )





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





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Tuple  =     self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''clusters'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_resize'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''size'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_normalize'''    )    )





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Tuple  =     self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size  ,{'''height''': 18, '''width''': 18}    )

         UpperCAmelCase_     :				Any  =     self.image_processing_class.from_dict(self.image_processor_dict  ,size=42    )
         self.assertEqual(image_processor.size  ,{'''height''': 42, '''width''': 42}    )





    def 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         UpperCAmelCase_     :				Tuple  =     json.loads(image_processor.to_json_string()    )
         for key, value in self.image_processor_dict.items():
              if key == "clusters":
                   self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE  ,obj[key]    )    )
              else:
                   self.assertEqual(obj[key]  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Dict  =     self.image_processing_class(**self.image_processor_dict    )

         with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase_     :				Any  =     os.path.join(_SCREAMING_SNAKE_CASE  ,'''image_processor.json'''    )
              image_processor_first.to_json_file(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Optional[Any]  =     self.image_processing_class.from_json_file(_SCREAMING_SNAKE_CASE    ).to_dict()

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





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				str  =     self.image_processing_class(**self.image_processor_dict    )

         with tempfile.TemporaryDirectory() as tmpdirname:
              image_processor_first.save_pretrained(_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				Any  =     self.image_processing_class.from_pretrained(_SCREAMING_SNAKE_CASE    ).to_dict()

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





    @unittest.skip('''ImageGPT requires clusters at initialization'''    )
    def 				a__ ( self    )					->						str:
         pass







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				int  =     load_dataset('''hf-internal-testing/fixtures_image_utils'''					,		split='''test''' )

     UpperCAmelCase_     :				Optional[int]  =     Image.open(dataset[4]['''file'''] )
     UpperCAmelCase_     :				Any  =     Image.open(dataset[5]['''file'''] )

     UpperCAmelCase_     :				Union[str, Any]  =     [imagea, imagea]

     return images



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






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				int  =     ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''    )

         UpperCAmelCase_     :				Any  =     prepare_images()

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

         self.assertIsInstance(encoding.input_ids  ,torch.LongTensor    )
         self.assertEqual(encoding.input_ids.shape  ,(1, 1_024)    )

         UpperCAmelCase_     :				str  =     [306, 191, 191]
         self.assertEqual(encoding.input_ids[0, :3].tolist()  ,_SCREAMING_SNAKE_CASE    )

         # test batched
         UpperCAmelCase_     :				List[str]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    )

         self.assertIsInstance(encoding.input_ids  ,torch.LongTensor    )
         self.assertEqual(encoding.input_ids.shape  ,(2, 1_024)    )

         UpperCAmelCase_     :				List[str]  =     [303, 13, 13]
         self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,_SCREAMING_SNAKE_CASE    )





'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		tuple[float, float]:

			# Check if the input is valid
			if not len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     ) == 3:
						raise ValueError('Please enter a valid equation.'     )
			if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
						raise ValueError('Both a & b of two equations can\'t be zero.'     )

			# Extract the coefficients
			A_		, A_		, A_									= equationa
			A_		, A_		, A_									= equationa

			# Calculate the determinants of the matrices
			A_									= aa * ba - aa * ba
			A_									= ca * ba - ca * ba
			A_									= aa * ca - aa * ca

			# Check if the system of linear equations has a solution (using Cramer's rule)
			if determinant == 0:
						if determinant_x == determinant_y == 0:
									raise ValueError('Infinite solutions. (Consistent system)'     )
						else:
									raise ValueError('No solution. (Inconsistent system)'     )
			else:
						if determinant_x == determinant_y == 0:
									# Trivial solution (Inconsistent system)
									return (0.0, 0.0)
						else:
									A_									= determinant_x / determinant
									A_									= determinant_y / determinant
									# Non-Trivial Solution (Consistent system)
									return (x, y)




