"""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 		UpperCamelCase      (   unittest.TestCase						):



				def __init__(self    :				Union[str, Any]     ,  _A    :				List[str]     ,  _A    :				List[str]=7     ,  _A    :				Tuple=3     ,  _A    :				Any=18     ,  _A    :				str=30     ,  _A    :				List[str]=4_00     ,  _A    :				List[Any]=True     ,  _A    :				Union[str, Any]=None     ,  _A    :				List[str]=True     ,  )   ->     Any:
					__snake_case      :							int						=			size if size is not None else {'height': 18, 'width': 18}
					__snake_case      :							List[str]						=			parent
					__snake_case      :							Optional[Any]						=			batch_size
					__snake_case      :							Optional[int]						=			num_channels
					__snake_case      :							Dict						=			image_size
					__snake_case      :							int						=			min_resolution
					__snake_case      :							Tuple						=			max_resolution
					__snake_case      :							List[str]						=			do_resize
					__snake_case      :							Dict						=			size
					__snake_case      :							Dict						=			do_normalize






				def  _lowercase					(self    :				List[str])   ->     List[Any]:
					return {
					    # here we create 2 clusters for the sake of simplicity
					    "clusters": np.asarray(
					        [
					            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
					            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
					        ]),
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					}







@require_torch
@require_vision
class 		UpperCamelCase      (   lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				Optional[int]   =		ImageGPTImageProcessor if is_vision_available() else None



				def  _lowercase					(self    :				Dict)   ->     Optional[Any]:
					__snake_case      :							Dict						=			ImageGPTImageProcessingTester(self)



				@property
				def  _lowercase					(self    :				str)   ->     Tuple:
					return self.image_processor_tester.prepare_image_processor_dict()



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(_A     ,  'clusters'))
					self.assertTrue(hasattr(_A     ,  'do_resize'))
					self.assertTrue(hasattr(_A     ,  'size'))
					self.assertTrue(hasattr(_A     ,  'do_normalize'))



				def  _lowercase					(self    :				List[Any])   ->     Any:
					__snake_case      :							Optional[int]						=			self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size     ,  {'height': 18, 'width': 18})

					__snake_case      :							Optional[int]						=			self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42)
					self.assertEqual(image_processor.size     ,  {'height': 42, 'width': 42})



				def  _lowercase					(self    :				Optional[Any])   ->     int:
					__snake_case      :							int						=			self.image_processing_class(**self.image_processor_dict)
					__snake_case      :							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(_A     ,  obj[key]))
						else:
							self.assertEqual(obj[key]     ,  _A)



				def  _lowercase					(self    :				Any)   ->     Optional[int]:
					__snake_case      :							Dict						=			self.image_processing_class(**self.image_processor_dict)

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

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



				def  _lowercase					(self    :				int)   ->     Tuple:
					__snake_case      :							Union[str, Any]						=			self.image_processing_class(**self.image_processor_dict)

					with tempfile.TemporaryDirectory() as tmpdirname:
						image_processor_first.save_pretrained(_A)
						__snake_case      :							Optional[int]						=			self.image_processing_class.from_pretrained(_A).to_dict()

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






				@unittest.skip('ImageGPT requires clusters at initialization')
				def  _lowercase					(self    :				Dict)   ->     int:
					pass







def   __UpperCAmelCase    (   )							->      str:

	'''simple docstring'''





	__snake_case      :							Tuple						=			load_dataset('hf-internal-testing/fixtures_image_utils'      ,							split='test'  )

	__snake_case      :							List[str]						=			Image.open(dataset[4]['file']  )
	__snake_case      :							List[Any]						=			Image.open(dataset[5]['file']  )

	__snake_case      :							Any						=			[imagea, imagea]

	return images

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



				@slow
				def  _lowercase					(self    :				Optional[Any])   ->     Any:
					__snake_case      :							Optional[int]						=			ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small')

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

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

					self.assertIsInstance(encoding.input_ids     ,  torch.LongTensor)
					self.assertEqual(encoding.input_ids.shape     ,  (1, 10_24))

					__snake_case      :							int						=			[3_06, 1_91, 1_91]
					self.assertEqual(encoding.input_ids[0, :3].tolist()     ,  _A)

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

					self.assertIsInstance(encoding.input_ids     ,  torch.LongTensor)
					self.assertEqual(encoding.input_ids.shape     ,  (2, 10_24))

					__snake_case      :							Any						=			[3_03, 13, 13]
					self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,  _A)






"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_a					:					Dict=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):



				def __init__(self    :				List[str]     ,  *_A    :				Dict     ,  **_A    :				Optional[Any])   ->     None:
					warnings.warn(
					    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
					    ' use CLIPImageProcessor instead.'     ,  _A     ,  )
					super().__init__(*_A     ,  **_A)





