


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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase							=						logging.get_logger(__name__)


if is_vision_available():
				import PIL




class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		Optional[int]        =	['''pixel_values''']


	def __init__(						self							,    lowercase__ = True							,    lowercase__ = None							,    lowercase__ = PILImageResampling.BICUBIC							,    lowercase__ = True							,    lowercase__ = None							,    lowercase__ = True							,    lowercase__ = 1 / 2_5_5							,    lowercase__ = True							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = True							,    **lowercase__							,    ):
							super().__init__(**lowercase__)
							__UpperCAmelCase :       Any  						=  size if size is not None else {'''shortest_edge''': 2_2_4}
							__UpperCAmelCase :       str  						=  get_size_dict(lowercase__							,    default_to_square=lowercase__)
							__UpperCAmelCase :       Union[str, Any]  						=  crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
							__UpperCAmelCase :       str  						=  get_size_dict(lowercase__							,    default_to_square=lowercase__							,    param_name='''crop_size''')

							__UpperCAmelCase :       List[Any]  						=  do_resize
							__UpperCAmelCase :       List[Any]  						=  size
							__UpperCAmelCase :       Optional[int]  						=  resample
							__UpperCAmelCase :       List[Any]  						=  do_center_crop
							__UpperCAmelCase :       Union[str, Any]  						=  crop_size
							__UpperCAmelCase :       Optional[int]  						=  do_rescale
							__UpperCAmelCase :       Optional[int]  						=  rescale_factor
							__UpperCAmelCase :       List[Any]  						=  do_normalize
							__UpperCAmelCase :       Tuple  						=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							__UpperCAmelCase :       Tuple  						=  image_std if image_std is not None else OPENAI_CLIP_STD
							__UpperCAmelCase :       List[str]  						=  do_convert_rgb


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = PILImageResampling.BICUBIC							,    lowercase__ = None							,    **lowercase__							,    ):
							__UpperCAmelCase :       Union[str, Any]  						=  get_size_dict(lowercase__							,    default_to_square=lowercase__)
							if "shortest_edge" not in size:
													raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
							__UpperCAmelCase :       str  						=  get_resize_output_image_size(lowercase__							,    size=size['''shortest_edge''']							,    default_to_square=lowercase__)
							return resize(lowercase__							,    size=lowercase__							,    resample=lowercase__							,    data_format=lowercase__							,    **lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    **lowercase__							,    ):
							__UpperCAmelCase :       str  						=  get_size_dict(lowercase__)
							if "height" not in size or "width" not in size:
													raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}")
							return center_crop(lowercase__							,    size=(size['''height'''], size['''width'''])							,    data_format=lowercase__							,    **lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    **lowercase__							,    ):
							return rescale(lowercase__							,    scale=lowercase__							,    data_format=lowercase__							,    **lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    **lowercase__							,    ):
							return normalize(lowercase__							,    mean=lowercase__							,    std=lowercase__							,    data_format=lowercase__							,    **lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = ChannelDimension.FIRST							,    **lowercase__							,    ):
							__UpperCAmelCase :       Tuple  						=  do_resize if do_resize is not None else self.do_resize
							__UpperCAmelCase :       Dict  						=  size if size is not None else self.size
							__UpperCAmelCase :       Tuple  						=  get_size_dict(lowercase__							,    param_name='''size'''							,    default_to_square=lowercase__)
							__UpperCAmelCase :       str  						=  resample if resample is not None else self.resample
							__UpperCAmelCase :       str  						=  do_center_crop if do_center_crop is not None else self.do_center_crop
							__UpperCAmelCase :       Union[str, Any]  						=  crop_size if crop_size is not None else self.crop_size
							__UpperCAmelCase :       Dict  						=  get_size_dict(lowercase__							,    param_name='''crop_size'''							,    default_to_square=lowercase__)
							__UpperCAmelCase :       Tuple  						=  do_rescale if do_rescale is not None else self.do_rescale
							__UpperCAmelCase :       Optional[Any]  						=  rescale_factor if rescale_factor is not None else self.rescale_factor
							__UpperCAmelCase :       Optional[int]  						=  do_normalize if do_normalize is not None else self.do_normalize
							__UpperCAmelCase :       List[str]  						=  image_mean if image_mean is not None else self.image_mean
							__UpperCAmelCase :       Optional[Any]  						=  image_std if image_std is not None else self.image_std
							__UpperCAmelCase :       List[Any]  						=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							__UpperCAmelCase :       Union[str, Any]  						=  make_list_of_images(lowercase__)

							if not valid_images(lowercase__):
													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.''')

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
													__UpperCAmelCase :       Optional[Any]  						=  [convert_to_rgb(lowercase__) for image in images]

							# All transformations expect numpy arrays.
							__UpperCAmelCase :       Tuple  						=  [to_numpy_array(lowercase__) for image in images]

							if do_resize:
													__UpperCAmelCase :       Dict  						=  [self.resize(image=lowercase__							,    size=lowercase__							,    resample=lowercase__) for image in images]

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

							if do_rescale:
													__UpperCAmelCase :       List[str]  						=  [self.rescale(image=lowercase__							,    scale=lowercase__) for image in images]

							if do_normalize:
													__UpperCAmelCase :       List[Any]  						=  [self.normalize(image=lowercase__							,    mean=lowercase__							,    std=lowercase__) for image in images]

							__UpperCAmelCase :       Optional[int]  						=  [to_channel_dimension_format(lowercase__							,    lowercase__) for image in images]

							__UpperCAmelCase :       int  						=  {'''pixel_values''': images}
							return BatchFeature(data=lowercase__							,    tensor_type=lowercase__)





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   ):


	def __init__(						self							,    lowercase__							,    lowercase__=7							,    lowercase__=3							,    lowercase__=1_8							,    lowercase__=3_0							,    lowercase__=4_0_0							,    lowercase__=True							,    lowercase__=None							,    lowercase__=True							,    ):
							__UpperCAmelCase :       Union[str, Any]  						=  size if size is not None else {'''height''': 1_8, '''width''': 1_8}
							__UpperCAmelCase :       Any  						=  parent
							__UpperCAmelCase :       Dict  						=  batch_size
							__UpperCAmelCase :       List[str]  						=  num_channels
							__UpperCAmelCase :       int  						=  image_size
							__UpperCAmelCase :       Tuple  						=  min_resolution
							__UpperCAmelCase :       str  						=  max_resolution
							__UpperCAmelCase :       Optional[int]  						=  do_resize
							__UpperCAmelCase :       Tuple  						=  size
							__UpperCAmelCase :       Union[str, Any]  						=  do_normalize


	def    A(						self):
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
							            [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
							        ]),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}






@require_torch
@require_vision
class        lowerCamelCase      (     _UpperCamelCase       ,	unittest.TestCase   ):
	_lowerCAmelCase    :		Dict        =	ImageGPTImageProcessor if is_vision_available() else None


	def    A(						self):
							__UpperCAmelCase :       Optional[Any]  						=  ImageGPTImageProcessingTester(self)


	@property
	def    A(						self):
							return self.image_processor_tester.prepare_image_processor_dict()


	def    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  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    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size							,    {'''height''': 1_8, '''width''': 1_8})

							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_dict(self.image_processor_dict							,    size=4_2)
							self.assertEqual(image_processor.size							,    {'''height''': 4_2, '''width''': 4_2})


	def    A(						self):
							__UpperCAmelCase :       Any  						=  self.image_processing_class(**self.image_processor_dict)
							__UpperCAmelCase :       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(lowercase__							,    obj[key]))
													else:
																			self.assertEqual(obj[key]							,    lowercase__)


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase :       Dict  						=  os.path.join(lowercase__							,    '''image_processor.json''')
													image_processor_first.to_json_file(lowercase__)
													__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class.from_json_file(lowercase__).to_dict()

							__UpperCAmelCase :       Any  						=  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    A(						self):
							__UpperCAmelCase :       Union[str, Any]  						=  self.image_processing_class(**self.image_processor_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													image_processor_first.save_pretrained(lowercase__)
													__UpperCAmelCase :       Dict  						=  self.image_processing_class.from_pretrained(lowercase__).to_dict()

							__UpperCAmelCase :       Optional[Any]  						=  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    A(						self):
							pass






def 						__SCREAMING_SNAKE_CASE       (					)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  load_dataset('''hf-internal-testing/fixtures_image_utils'''			,					split='''test'''			)

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

						__UpperCAmelCase :       int  						=  [imagea, imagea]

						return images





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


	@slow
	def    A(						self):
							__UpperCAmelCase :       int  						=  ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small''')

							__UpperCAmelCase :       Any  						=  prepare_images()

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

							self.assertIsInstance(encoding.input_ids							,    torch.LongTensor)
							self.assertEqual(encoding.input_ids.shape							,    (1, 1_0_2_4))

							__UpperCAmelCase :       int  						=  [3_0_6, 1_9_1, 1_9_1]
							self.assertEqual(encoding.input_ids[0, :3].tolist()							,    lowercase__)

							# test batched
							__UpperCAmelCase :       int  						=  image_processing(lowercase__							,    return_tensors='''pt''')

							self.assertIsInstance(encoding.input_ids							,    torch.LongTensor)
							self.assertEqual(encoding.input_ids.shape							,    (2, 1_0_2_4))

							__UpperCAmelCase :       Any  						=  [3_0_3, 1_3, 1_3]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()							,    lowercase__)

