


"""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  _a							(    unittest.TestCase):




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




	def 							UpperCAmelCase__( self						:		List[str]		)->	List[Any]:
			return {
			    # here we create 2 clusters for the sake of simplicity
			    "clusters": np.asarray(
			        [
			            [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
			            [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
			        ]		),
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			}





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




	def 							UpperCAmelCase__( self						:		Dict		)->	Optional[Any]:
			lowerCAmelCase__       :      Dict   					= ImageGPTImageProcessingTester(self		)




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




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

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




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

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

			lowerCAmelCase__       :      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 							UpperCAmelCase__( self						:		int		)->	Tuple:
			lowerCAmelCase__       :      Union[str, Any]   					= self.image_processing_class(**self.image_processor_dict		)

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

			lowerCAmelCase__       :      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 							UpperCAmelCase__( self						:		Dict		)->	int:
			pass






def 					lowerCamelCase_   (	):




		"""simple docstring"""






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

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

		lowerCAmelCase__       :      Any   					= [imagea, imagea]

		return images





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




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

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

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

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

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

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

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

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase						=		{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		['''BeitFeatureExtractor''']
		lowerCamelCase						=		['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase						=		[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase						=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




