


'''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 _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







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







		def        __lowerCAmelCase		( self		:						int      )  ->			Dict:
			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 _lowerCamelCase	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Optional[Any]				   =    ImageGPTImageProcessor if is_vision_available() else None







		def        __lowerCAmelCase		( self		:						int      )  ->			str:
			__magic_name__	:   str					     =					ImageGPTImageProcessingTester(self      )







		@property
		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
			return self.image_processor_tester.prepare_image_processor_dict()







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

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

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

			__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						Any      )  ->			Union[str, Any]:
			__magic_name__	:   Any					     =					self.image_processing_class(**self.image_processor_dict      )

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

			__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Dict:
			pass







def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					load_dataset('hf-internal-testing/fixtures_image_utils'		,		split='test'		)

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

	__magic_name__	:   Tuple					     =					[imagea, imagea]

	return images





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



		'''simple docstring'''







		@slow
		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[int]:
			__magic_name__	:   str					     =					ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

			__magic_name__	:   List[str]					     =					prepare_images()

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

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

			__magic_name__	:   Optional[Any]					     =					[306, 191, 191]
			self.assertEqual(encoding.input_ids[0, :3].tolist()  ,					_A      )

			# test batched
			__magic_name__	:   Any					     =					image_processing(_A  ,					return_tensors='pt'      )

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

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



'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      int				   =    ["""pixel_values"""]







		def __init__( self		:						Any  ,					_A		:						bool = True  ,					_A		:						Optional[Dict[str, int]] = None  ,					_A		:						PILImageResampling = PILImageResampling.BILINEAR  ,					_A		:						bool = True  ,					_A		:						Dict[str, int] = None  ,					_A		:						bool = True  ,					_A		:						Union[int, float] = 1 / 255  ,					_A		:						bool = True  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					**_A		:						Optional[Any]  ,					)  ->			None:
			super().__init__(**_A      )
			__magic_name__	:   List[str]					     =					size if size is not None else {'shortest_edge': 256}
			__magic_name__	:   str					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   List[str]					     =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
			__magic_name__	:   Optional[int]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_resize
			__magic_name__	:   List[Any]					     =					size
			__magic_name__	:   List[str]					     =					resample
			__magic_name__	:   Dict					     =					do_center_crop
			__magic_name__	:   List[str]					     =					crop_size
			__magic_name__	:   int					     =					do_rescale
			__magic_name__	:   Tuple					     =					rescale_factor
			__magic_name__	:   List[str]					     =					do_normalize
			__magic_name__	:   Union[str, Any]					     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__magic_name__	:   Dict					     =					image_std if image_std is not None else IMAGENET_STANDARD_STD







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[str]  ,					)  ->			np.ndarray:
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			if "shortest_edge" not in size:
				raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
			__magic_name__	:   Dict					     =					get_resize_output_image_size(_A  ,					size=size['shortest_edge']  ,					default_to_square=_A      )
			return resize(_A  ,					size=_A  ,					resample=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Optional[int]  ,					)  ->			np.ndarray:
			__magic_name__	:   int					     =					get_size_dict(_A      )
			return center_crop(_A  ,					size=(size['height'], size['width'])  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						np.ndarray  ,					_A		:						float  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Tuple      )  ->			np.ndarray:
			return rescale(_A  ,					scale=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						np.ndarray  ,					_A		:						Union[float, List[float]]  ,					_A		:						Union[float, List[float]]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						int  ,					)  ->			np.ndarray:
			return normalize(_A  ,					mean=_A  ,					std=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						ImageInput  ,					_A		:						Optional[bool] = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						PILImageResampling = None  ,					_A		:						bool = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[float] = None  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[str, TensorType]] = None  ,					_A		:						Union[str, ChannelDimension] = ChannelDimension.FIRST  ,					**_A		:						List[Any]  ,					)  ->			List[str]:
			__magic_name__	:   int					     =					do_resize if do_resize is not None else self.do_resize
			__magic_name__	:   Tuple					     =					size if size is not None else self.size
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   Dict					     =					resample if resample is not None else self.resample
			__magic_name__	:   Optional[Any]					     =					do_center_crop if do_center_crop is not None else self.do_center_crop
			__magic_name__	:   Dict					     =					crop_size if crop_size is not None else self.crop_size
			__magic_name__	:   List[str]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_rescale if do_rescale is not None else self.do_rescale
			__magic_name__	:   str					     =					rescale_factor if rescale_factor is not None else self.rescale_factor
			__magic_name__	:   Any					     =					do_normalize if do_normalize is not None else self.do_normalize
			__magic_name__	:   Tuple					     =					image_mean if image_mean is not None else self.image_mean
			__magic_name__	:   Union[str, Any]					     =					image_std if image_std is not None else self.image_std

			__magic_name__	:   int					     =					make_list_of_images(_A      )

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

			if do_resize and size is None:
				raise ValueError('Size must be specified if do_resize is True.'      )

			if do_center_crop and crop_size is None:
				raise ValueError('Crop size must be specified if do_center_crop is True.'      )

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

			if do_normalize and (image_mean is None or image_std is None):
				raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

			# All transformations expect numpy arrays.
			__magic_name__	:   List[Any]					     =					[to_numpy_array(_A      ) for image in images]

			if do_resize:
				__magic_name__	:   Union[str, Any]					     =					[self.resize(image=_A  ,					size=_A  ,					resample=_A      ) for image in images]

			if do_center_crop:
				__magic_name__	:   Union[str, Any]					     =					[self.center_crop(image=_A  ,					size=_A      ) for image in images]

			if do_rescale:
				__magic_name__	:   List[Any]					     =					[self.rescale(image=_A  ,					scale=_A      ) for image in images]

			if do_normalize:
				__magic_name__	:   Optional[Any]					     =					[self.normalize(image=_A  ,					mean=_A  ,					std=_A      ) for image in images]

			__magic_name__	:   Union[str, Any]					     =					[to_channel_dimension_format(_A  ,					_A      ) for image in images]

			__magic_name__	:   List[str]					     =					{'pixel_values': images}
			return BatchFeature(data=_A  ,					tensor_type=_A      )