


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       lowercase_					(unittest.TestCase   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=7       ,  lowercase_=3       ,  lowercase_=18       ,  lowercase_=30       ,  lowercase_=400       ,  lowercase_=True       ,  lowercase_=None       ,  lowercase_=True       ,  )  -> Union[str, Any]:
											a__      =size if size is not None else {'height': 18, 'width': 18}
											a__      =parent
											a__      =batch_size
											a__      =num_channels
											a__      =image_size
											a__      =min_resolution
											a__      =max_resolution
											a__      =do_resize
											a__      =size
											a__      =do_normalize





				def 						__UpperCamelCase    (				self)  -> 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       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =ImageGPTImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =ImageGPTImageProcessingTester(self)





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return self.image_processor_tester.prepare_image_processor_dict()





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





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size       ,  {'height': 18, 'width': 18})

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





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =self.image_processing_class(**self.image_processor_dict)
											a__      =json.loads(image_processor.to_json_string())
											for key, value in self.image_processor_dict.items():
																		if key == "clusters":
																									self.assertTrue(np.array_equal(lowercase_       ,  obj[key]))
																		else:
																									self.assertEqual(obj[key]       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.image_processing_class(**self.image_processor_dict)

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

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





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =self.image_processing_class(**self.image_processor_dict)

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

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





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







def   _lowercase(					):
							a__      =load_dataset('hf-internal-testing/fixtures_image_utils'    ,     split='test'     )

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

							a__      =[imagea, imagea]

							return images







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





				@slow
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small')

											a__      =prepare_images()

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

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

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

											# test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt')

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

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









from typing import Any

import numpy as np
def 		_a       ( a					:np.ndarray					)				->     bool:
 return np.array_equal(a      ,				matrix.conjugate().T					)
def 		_a       ( a					:np.ndarray      ,				a					:np.ndarray					)				->     Any:
 a						    = v.conjugate().T
 a						    = v_star.dot(a					)
 assert isinstance(a      ,				np.ndarray					)
 return (v_star_dot.dot(a					)) / (v_star.dot(a					))
def 		_a       ( )				->     None:
 a						    = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 a						    = np.array([[1], [2], [3]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(a      ,				a					)					)

 a						    = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(a      ,				a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()


