

'''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 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







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







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										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 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    ImageGPTImageProcessor if is_vision_available() else None







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=ImageGPTImageProcessingTester(self   )







				@property
				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''clusters'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_resize'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''size'''   )   )
										self.assertTrue(hasattr(UpperCAmelCase__		,					'''do_normalize'''   )   )







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Any    		=self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size		,					{'''height''': 18, '''width''': 18}   )

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







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=self.image_processing_class(**self.image_processor_dict   )
										lowercase :							List[str]    		=json.loads(image_processor.to_json_string()   )
										for key, value in self.image_processor_dict.items():
																if key == "clusters":
																						self.assertTrue(np.array_equal(UpperCAmelCase__		,					obj[key]   )   )
																else:
																						self.assertEqual(obj[key]		,					UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							str    		=self.image_processing_class(**self.image_processor_dict   )

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

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







				def  lowerCamelCase_	(       self     :		List[str]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=self.image_processing_class(**self.image_processor_dict   )

										with tempfile.TemporaryDirectory() as tmpdirname:
																image_processor_first.save_pretrained(UpperCAmelCase__   )
																lowercase :							Tuple    		=self.image_processing_class.from_pretrained(UpperCAmelCase__   ).to_dict()

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







				@unittest.skip('''ImageGPT requires clusters at initialization'''   )
				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										pass






def 				_lowerCAmelCase			(   )				->  Dict:
						lowercase :							List[Any]    		=load_dataset('''hf-internal-testing/fixtures_image_utils'''   , split='''test'''						)

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

						lowercase :							Optional[Any]    		=[imagea, imagea]

						return images







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







				@slow
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

										lowercase :							int    		=prepare_images()

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

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

										lowercase :							Tuple    		=[306, 191, 191]
										self.assertEqual(encoding.input_ids[0, :3].tolist()		,					UpperCAmelCase__   )

										# test batched
										lowercase :							Optional[int]    		=image_processing(UpperCAmelCase__		,					return_tensors='''pt'''   )

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

										lowercase :							str    		=[303, 13, 13]
										self.assertEqual(encoding.input_ids[1, -3:].tolist()		,					UpperCAmelCase__   )








'''simple docstring'''





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase_    						=       logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    ['pixel_values']







				def __init__(       self     :		List[Any]		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		Union[int, float] = 1 / 255		,					UpperCAmelCase__     :		bool = True		,					UpperCAmelCase__     :		int = 8		,					**UpperCAmelCase__     :		str		,					):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=do_rescale
										lowercase :							List[Any]    		=rescale_factor
										lowercase :							Tuple    		=do_pad
										lowercase :							List[str]    		=pad_size







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		float		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None		,					**UpperCAmelCase__     :		Optional[int]   ):
										'''simple docstring'''
										return rescale(UpperCAmelCase__		,					scale=UpperCAmelCase__		,					data_format=UpperCAmelCase__		,					**UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		np.ndarray		,					UpperCAmelCase__     :		int		,					UpperCAmelCase__     :		Optional[Union[str, ChannelDimension]] = None   ):
										'''simple docstring'''
										lowercase		,	lowercase :							Union[str, Any]    		=get_image_size(UpperCAmelCase__   )
										lowercase :							Tuple    		=(old_height // size + 1) * size - old_height
										lowercase :							Tuple    		=(old_width // size + 1) * size - old_width

										return pad(UpperCAmelCase__		,					((0, pad_height), (0, pad_width))		,					mode='''symmetric'''		,					data_format=UpperCAmelCase__   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		ImageInput		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[float] = None		,					UpperCAmelCase__     :		Optional[bool] = None		,					UpperCAmelCase__     :		Optional[int] = None		,					UpperCAmelCase__     :		Optional[Union[str, TensorType]] = None		,					UpperCAmelCase__     :		Union[str, ChannelDimension] = ChannelDimension.FIRST		,					**UpperCAmelCase__     :		Optional[Any]		,					):
										'''simple docstring'''
										lowercase :							int    		=do_rescale if do_rescale is not None else self.do_rescale
										lowercase :							Tuple    		=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase :							int    		=do_pad if do_pad is not None else self.do_pad
										lowercase :							List[Any]    		=pad_size if pad_size is not None else self.pad_size

										lowercase :							Any    		=make_list_of_images(UpperCAmelCase__   )

										if not valid_images(UpperCAmelCase__   ):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

										if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

										# All transformations expect numpy arrays.
										lowercase :							Dict    		=[to_numpy_array(UpperCAmelCase__   ) for image in images]

										if do_rescale:
																lowercase :							Tuple    		=[self.rescale(image=UpperCAmelCase__		,					scale=UpperCAmelCase__   ) for image in images]

										if do_pad:
																lowercase :							Union[str, Any]    		=[self.pad(UpperCAmelCase__		,					size=UpperCAmelCase__   ) for image in images]

										lowercase :							Dict    		=[to_channel_dimension_format(UpperCAmelCase__		,					UpperCAmelCase__   ) for image in images]

										lowercase :							Any    		={'''pixel_values''': images}
										return BatchFeature(data=UpperCAmelCase__		,					tensor_type=UpperCAmelCase__   )





