


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 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def __init__( self       : int  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : int=7  ,							SCREAMING_SNAKE_CASE__       : Any=3  ,							SCREAMING_SNAKE_CASE__       : Dict=1_8  ,							SCREAMING_SNAKE_CASE__       : int=3_0  ,							SCREAMING_SNAKE_CASE__       : str=4_0_0  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=True  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=None  ,							SCREAMING_SNAKE_CASE__       : Dict=True  ,							)	->    Optional[int]:
             a_      :	int							=						size if size is not None else {'height': 1_8, 'width': 1_8}
             a_      :	Dict							=						parent
             a_      :	Tuple							=						batch_size
             a_      :	List[str]							=						num_channels
             a_      :	Optional[int]							=						image_size
             a_      :	int							=						min_resolution
             a_      :	Optional[Any]							=						max_resolution
             a_      :	int							=						do_resize
             a_      :	Any							=						size
             a_      :	Tuple							=						do_normalize

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    List[Any]:
             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 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  List[str]       =		ImageGPTImageProcessor if is_vision_available() else None
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Optional[Any]:
             a_      :	List[str]							=						ImageGPTImageProcessingTester(self  )
      @property
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             return self.image_processor_tester.prepare_image_processor_dict()
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Tuple:
             a_      :	List[str]							=						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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Any:
             a_      :	Dict							=						self.image_processing_class.from_dict(self.image_processor_dict  )
             self.assertEqual(image_processor.size  ,							{'height': 1_8, 'width': 1_8}  )

             a_      :	List[str]							=						self.image_processing_class.from_dict(self.image_processor_dict  ,							size=4_2  )
             self.assertEqual(image_processor.size  ,							{'height': 4_2, 'width': 4_2}  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             a_      :	Dict							=						self.image_processing_class(**self.image_processor_dict  )
             a_      :	Optional[int]							=						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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Tuple:
             a_      :	Dict							=						self.image_processing_class(**self.image_processor_dict  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    a_      :	str							=						os.path.join(SCREAMING_SNAKE_CASE__  ,							'image_processor.json'  )
                    image_processor_first.to_json_file(SCREAMING_SNAKE_CASE__  )
                    a_      :	Union[str, Any]							=						self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE__  ).to_dict()

             a_      :	Union[str, Any]							=						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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             a_      :	int							=						self.image_processing_class(**self.image_processor_dict  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE__  ).to_dict()

             a_      :	Union[str, Any]							=						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  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Union[str, Any]:
             pass







def  SCREAMING_SNAKE_CASE_		(    )			->	Tuple:

       """simple docstring"""

       a_      :	List[str]							=						load_dataset('hf-internal-testing/fixtures_image_utils'						,					split='test'       )

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

       a_      :	List[Any]							=						[imagea, imagea]

       return images
@require_vision
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    List[str]:
             a_      :	Optional[int]							=						ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'  )

             a_      :	Union[str, Any]							=						prepare_images()

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

             self.assertIsInstance(encoding.input_ids  ,							torch.LongTensor  )
             self.assertEqual(encoding.input_ids.shape  ,							(1, 1_0_2_4)  )

             a_      :	str							=						[3_0_6, 1_9_1, 1_9_1]
             self.assertEqual(encoding.input_ids[0, :3].tolist()  ,							SCREAMING_SNAKE_CASE__  )

             # test batched
             a_      :	str							=						image_processing(SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )

             self.assertIsInstance(encoding.input_ids  ,							torch.LongTensor  )
             self.assertEqual(encoding.input_ids.shape  ,							(2, 1_0_2_4)  )

             a_      :	Optional[int]							=						[3_0_3, 1_3, 1_3]
             self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,							SCREAMING_SNAKE_CASE__  )






import unittest

import numpy as np







def  SCREAMING_SNAKE_CASE_		(    __A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray | None = None						,					)			->	np.ndarray:

       """simple docstring"""

       a_      :	List[str]							=						np.shape(__A       )
       a_      :	Any							=						np.shape(__A       )
       a_      :	List[Any]							=						np.shape(__A       )

       if shape_a[0] != shape_b[0]:
              a_      :	List[Any]							=						(
                  'Expected the same number of rows for A and B. '
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(__A       )

       if shape_b[1] != shape_c[1]:
              a_      :	int							=						(
                  'Expected the same number of columns for B and C. '
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(__A       )

       a_      :	Any							=						pseudo_inv
       if a_inv is None:
              try:
                     a_      :	List[str]							=						np.linalg.inv(__A       )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         'Input matrix A is not invertible. Cannot compute Schur complement.'       )

       return mat_c - mat_b.T @ a_inv @ mat_b
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    None:
             a_      :	str							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Any							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1], [6, 3]]  )

             a_      :	int							=						schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.block([[a, b], [b.T, c]]  )

             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )

             self.assertAlmostEqual(SCREAMING_SNAKE_CASE__  ,							det_a * det_s  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    None:
             a_      :	Optional[int]							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	str							=						np.array([[2, 1], [6, 3]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    None:
             a_      :	Tuple							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1, 3], [6, 3, 5]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()


