




'''simple docstring'''


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


_snake_case : List[Any]  				=						logging.get_logger(__name__)
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    Union[int, Iterable[int]]					,		UpperCamelCase			:    bool					,		UpperCamelCase			:    int ):





							'''simple docstring'''


							def constraint_to_multiple_of(UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    Optional[Any]=0					,		UpperCamelCase			:    Tuple=None ):
														_a      	=							round(val / multiple ) * multiple

														if max_val is not None and x > max_val:
																					_a      	=							math.floor(val / multiple ) * multiple

														if x < min_val:
																					_a      	=							math.ceil(val / multiple ) * multiple

														return x

							_a      	=							(output_size, output_size) if isinstance(UpperCamelCase					,		UpperCamelCase ) else output_size

							_a	,				_a      	=							get_image_size(UpperCamelCase )
							_a	,				_a      	=							output_size

							# determine new height and width
							_a      	=							output_height / input_height
							_a      	=							output_width / input_width

							if keep_aspect_ratio:
														# scale as little as possible
														if abs(1 - scale_width ) < abs(1 - scale_height ):
																					# fit width
																					_a      	=							scale_width
														else:
																					# fit height
																					_a      	=							scale_height

							_a      	=							constraint_to_multiple_of(scale_height * input_height					,		multiple=UpperCamelCase )
							_a      	=							constraint_to_multiple_of(scale_width * input_width					,		multiple=UpperCamelCase )

							return (new_height, new_width)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    int				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    int = 1				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''height''': 3_84, '''width''': 3_84}
														_a      	=							get_size_dict(lowerCAmelCase_   )
														_a      	=							do_resize
														_a      	=							size
														_a      	=							keep_aspect_ratio
														_a      	=							ensure_multiple_of
														_a      	=							resample
														_a      	=							do_rescale
														_a      	=							rescale_factor
														_a      	=							do_normalize
														_a      	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    bool = False				,     lowerCAmelCase_			:    int = 1				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'   )
														_a      	=							get_resize_output_image_size(
														    lowerCAmelCase_				,     output_size=(size['''height'''], size['''width'''])				,     keep_aspect_ratio=lowerCAmelCase_				,     multiple=lowerCAmelCase_				,     )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[int, float]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Any				,     )     ->				List[Any]:


														"""simple docstring"""


														return rescale(lowerCAmelCase_				,     scale=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







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


														"""simple docstring"""


														return normalize(lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    int = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    int = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    float = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     lowerCAmelCase_			:    ChannelDimension = ChannelDimension.FIRST				,     **lowerCAmelCase_			:    List[str]				,     )     ->				PIL.Image.Image:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_   )
														_a      	=							keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
														_a      	=							ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
														_a      	=							resample if resample is not None else self.resample
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							make_list_of_images(lowerCAmelCase_   )

														if not valid_images(lowerCAmelCase_   ):
																					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 or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize 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.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

														if do_resize:
																					_a      	=							[self.resize(image=lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_   ) for image in images]

														if do_rescale:
																					_a      	=							[self.rescale(image=lowerCAmelCase_				,     scale=lowerCAmelCase_   ) for image in images]

														if do_normalize:
																					_a      	=							[self.normalize(image=lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_   ) for image in images]

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    List[Tuple] = None   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
																												raise ValueError(
																												    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

																					if is_torch_tensor(lowerCAmelCase_   ):
																												_a      	=							target_sizes.numpy()

																					_a      	=							[]

																					for idx in range(len(lowerCAmelCase_   )   ):
																												_a      	=							torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0   )				,     size=target_sizes[idx]				,     mode='''bilinear'''				,     align_corners=lowerCAmelCase_   )
																												_a      	=							resized_logits[0].argmax(dim=0   )
																												semantic_segmentation.append(lowerCAmelCase_   )
														else:
																					_a      	=							logits.argmax(dim=1   )
																					_a      	=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

														return semantic_segmentation







'''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			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    List[str]=7				,     lowerCAmelCase_			:    Dict=3				,     lowerCAmelCase_			:    List[Any]=18				,     lowerCAmelCase_			:    Any=30				,     lowerCAmelCase_			:    Optional[int]=4_00				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    List[str]=True				,     )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							size if size is not None else {'''height''': 18, '''width''': 18}
														_a      	=							parent
														_a      	=							batch_size
														_a      	=							num_channels
														_a      	=							image_size
														_a      	=							min_resolution
														_a      	=							max_resolution
														_a      	=							do_resize
														_a      	=							size
														_a      	=							do_normalize







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				int:


														"""simple docstring"""


														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      A       (						_a	,unittest.TestCase       ):
							lowercase_							     =							ImageGPTImageProcessor if is_vision_available() else None







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				str:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessingTester(self   )







							@property
							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				int:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''clusters'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_resize'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''size'''   )   )
														self.assertTrue(hasattr(lowerCAmelCase_				,     '''do_normalize'''   )   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							self.image_processing_class.from_dict(self.image_processor_dict   )
														self.assertEqual(image_processor.size				,     {'''height''': 18, '''width''': 18}   )

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







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )
														_a      	=							json.loads(image_processor.to_json_string()   )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(lowerCAmelCase_				,     obj[key]   )   )
																					else:
																												self.assertEqual(obj[key]				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							os.path.join(lowerCAmelCase_				,     '''image_processor.json'''   )
																					image_processor_first.to_json_file(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_json_file(lowerCAmelCase_   ).to_dict()

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







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.image_processing_class(**self.image_processor_dict   )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(lowerCAmelCase_   )
																					_a      	=							self.image_processing_class.from_pretrained(lowerCAmelCase_   ).to_dict()

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







							@unittest.skip('''ImageGPT requires clusters at initialization'''   )
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														pass



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							load_dataset('''hf-internal-testing/fixtures_image_utils'''					,		split='''test''' )

							_a      	=							Image.open(dataset[4]['''file'''] )
							_a      	=							Image.open(dataset[5]['''file'''] )

							_a      	=							[imagea, imagea]

							return images

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







							@slow
							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				int:


														"""simple docstring"""


														_a      	=							ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''   )

														_a      	=							prepare_images()

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

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

														_a      	=							[3_06, 1_91, 1_91]
														self.assertEqual(encoding.input_ids[0, :3].tolist()				,     lowerCAmelCase_   )

														# test batched
														_a      	=							image_processing(lowerCAmelCase_				,     return_tensors='''pt'''   )

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

														_a      	=							[3_03, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()				,     lowerCAmelCase_   )

