import json
import pathlib
import unittest

import numpy as np

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, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import YolosImageProcessor


class   lowercase      (       unittest.TestCase       ):




							def __init__(  self				:   Optional[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Any=7		,							_UpperCamelCase				:   List[str]=3		,							_UpperCamelCase				:   Optional[int]=30		,							_UpperCamelCase				:   List[Any]=400		,							_UpperCamelCase				:   Optional[int]=True		,							_UpperCamelCase				:   Optional[int]=None		,							_UpperCamelCase				:   Tuple=True		,							_UpperCamelCase				:   int=[0.5, 0.5, 0.5]		,							_UpperCamelCase				:   int=[0.5, 0.5, 0.5]		,							_UpperCamelCase				:   str=True		,							_UpperCamelCase				:   List[Any]=1 / 255		,							_UpperCamelCase				:   str=True		,							)   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    min_resolution
												SCREAMING_SNAKE_CASE           =    max_resolution
												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean
												SCREAMING_SNAKE_CASE           =    image_std
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor
												SCREAMING_SNAKE_CASE           =    do_pad




							def 		__snake_case(  self				:   List[Any] )   ->			Union[str, Any]:



												'''simple docstring'''

												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_rescale": self.do_rescale,
												    "rescale_factor": self.rescale_factor,
												    "do_pad": self.do_pad,
												}





							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Tuple=False )   ->			int:



												'''simple docstring'''

												if not batched:
																	SCREAMING_SNAKE_CASE           =    image_inputs[0]
																	if isinstance(_UpperCamelCase		,							Image.Image ):
																						SCREAMING_SNAKE_CASE           =    image.size
																	else:
																						SCREAMING_SNAKE_CASE           =    image.shape[1], image.shape[2]
																	if w < h:
																						SCREAMING_SNAKE_CASE           =    int(self.size["shortest_edge"] * h / w )
																						SCREAMING_SNAKE_CASE           =    self.size['shortest_edge']
																	elif w > h:
																						SCREAMING_SNAKE_CASE           =    self.size['shortest_edge']
																						SCREAMING_SNAKE_CASE           =    int(self.size["shortest_edge"] * w / h )
																	else:
																						SCREAMING_SNAKE_CASE           =    self.size['shortest_edge']
																						SCREAMING_SNAKE_CASE           =    self.size['shortest_edge']

												else:
																	SCREAMING_SNAKE_CASE           =    []
																	for image in image_inputs:
																						SCREAMING_SNAKE_CASE           =    self.get_expected_values([image] )
																						expected_values.append((expected_height, expected_width) )
																	SCREAMING_SNAKE_CASE           =    max(_UpperCamelCase		,							key=lambda _UpperCamelCase : item[0] )[0]
																	SCREAMING_SNAKE_CASE           =    max(_UpperCamelCase		,							key=lambda _UpperCamelCase : item[1] )[1]

												return expected_height, expected_width



@require_torch
@require_vision
class   lowercase      (       __UpperCamelCase			,    unittest.TestCase       ):
							lowercase__  :    List[str]									=							YolosImageProcessor if is_vision_available() else None




							def 		__snake_case(  self				:   List[Any] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    YolosImageProcessingTester(self )




							@property
							def 		__snake_case(  self				:   Tuple )   ->			str:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												self.assertTrue(hasattr(_UpperCamelCase		,							"image_mean" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"image_std" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"do_normalize" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"do_resize" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"size" ) )




							def 		__snake_case(  self				:   Dict )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class.from_dict(self.image_processor_dict )
												self.assertEqual(image_processor.size		,							{"shortest_edge": 18, "longest_edge": 1_333} )
												self.assertEqual(image_processor.do_pad		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.image_processing_class.from_dict(
												    self.image_processor_dict		,							size=42		,							max_size=84		,							pad_and_return_pixel_mask=_UpperCamelCase )
												self.assertEqual(image_processor.size		,							{"shortest_edge": 42, "longest_edge": 84} )
												self.assertEqual(image_processor.do_pad		,							_UpperCamelCase )




							def 		__snake_case(  self				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												pass




							def 		__snake_case(  self				:   Union[str, Any] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												# create random PIL images
												SCREAMING_SNAKE_CASE           =    prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_UpperCamelCase )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase		,							Image.Image )

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    image_processing(image_inputs[0]		,							return_tensors="pt" ).pixel_values

												SCREAMING_SNAKE_CASE           =    self.image_processor_tester.get_expected_values(_UpperCamelCase )

												self.assertEqual(
												    encoded_images.shape		,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    self.image_processor_tester.get_expected_values(_UpperCamelCase		,							batched=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							return_tensors="pt" ).pixel_values
												self.assertEqual(
												    encoded_images.shape		,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )		,							)




							def 		__snake_case(  self				:   Optional[int] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE           =    prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_UpperCamelCase		,							numpify=_UpperCamelCase )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase		,							np.ndarray )

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    image_processing(image_inputs[0]		,							return_tensors="pt" ).pixel_values

												SCREAMING_SNAKE_CASE           =    self.image_processor_tester.get_expected_values(_UpperCamelCase )

												self.assertEqual(
												    encoded_images.shape		,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							return_tensors="pt" ).pixel_values

												SCREAMING_SNAKE_CASE           =    self.image_processor_tester.get_expected_values(_UpperCamelCase		,							batched=_UpperCamelCase )

												self.assertEqual(
												    encoded_images.shape		,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )		,							)




							def 		__snake_case(  self				:   List[Any] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE           =    prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_UpperCamelCase		,							torchify=_UpperCamelCase )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase		,							torch.Tensor )

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    image_processing(image_inputs[0]		,							return_tensors="pt" ).pixel_values

												SCREAMING_SNAKE_CASE           =    self.image_processor_tester.get_expected_values(_UpperCamelCase )

												self.assertEqual(
												    encoded_images.shape		,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							return_tensors="pt" ).pixel_values

												SCREAMING_SNAKE_CASE           =    self.image_processor_tester.get_expected_values(_UpperCamelCase		,							batched=_UpperCamelCase )

												self.assertEqual(
												    encoded_images.shape		,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )		,							)




							def 		__snake_case(  self				:   List[Any] )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												SCREAMING_SNAKE_CASE           =    self.image_processing_class(do_resize=_UpperCamelCase		,							do_normalize=_UpperCamelCase		,							do_rescale=_UpperCamelCase )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE           =    prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_UpperCamelCase		,							torchify=_UpperCamelCase )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase		,							torch.Tensor )

												# Test whether the method "pad" and calling the image processor return the same tensors
												SCREAMING_SNAKE_CASE           =    image_processing_a.pad(_UpperCamelCase		,							return_tensors="pt" )
												SCREAMING_SNAKE_CASE           =    image_processing_a(_UpperCamelCase		,							return_tensors="pt" )

												self.assertTrue(
												    torch.allclose(encoded_images_with_method["pixel_values"]		,							encoded_images["pixel_values"]		,							atol=1e-4 ) )




							@slow
							def 		__snake_case(  self				:   str )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
												with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,							"r" ) as f:
																	SCREAMING_SNAKE_CASE           =    json.loads(f.read() )

												SCREAMING_SNAKE_CASE           =    {'image_id': 39_769, 'annotations': target}

												# encode them
												SCREAMING_SNAKE_CASE           =    YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
												SCREAMING_SNAKE_CASE           =    image_processing(images=_UpperCamelCase		,							annotations=_UpperCamelCase		,							return_tensors="pt" )

												# verify pixel values
												SCREAMING_SNAKE_CASE           =    torch.Size([1, 3, 800, 1_066] )
												self.assertEqual(encoding["pixel_values"].shape		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
												self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,							_UpperCamelCase		,							atol=1e-4 ) )

												# verify area
												SCREAMING_SNAKE_CASE           =    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,							_UpperCamelCase ) )
												# verify boxes
												SCREAMING_SNAKE_CASE           =    torch.Size([6, 4] )
												self.assertEqual(encoding["labels"][0]["boxes"].shape		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,							_UpperCamelCase		,							atol=1e-3 ) )
												# verify image_id
												SCREAMING_SNAKE_CASE           =    torch.tensor([39_769] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,							_UpperCamelCase ) )
												# verify is_crowd
												SCREAMING_SNAKE_CASE           =    torch.tensor([0, 0, 0, 0, 0, 0] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,							_UpperCamelCase ) )
												# verify class_labels
												SCREAMING_SNAKE_CASE           =    torch.tensor([75, 75, 63, 65, 17, 17] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,							_UpperCamelCase ) )
												# verify orig_size
												SCREAMING_SNAKE_CASE           =    torch.tensor([480, 640] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,							_UpperCamelCase ) )
												# verify size
												SCREAMING_SNAKE_CASE           =    torch.tensor([800, 1_066] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,							_UpperCamelCase ) )





							@slow
							def 		__snake_case(  self				:   List[str] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
												with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"		,							"r" ) as f:
																	SCREAMING_SNAKE_CASE           =    json.loads(f.read() )

												SCREAMING_SNAKE_CASE           =    {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

												SCREAMING_SNAKE_CASE           =    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

												# encode them
												SCREAMING_SNAKE_CASE           =    YolosImageProcessor(format="coco_panoptic" )
												SCREAMING_SNAKE_CASE           =    image_processing(images=_UpperCamelCase		,							annotations=_UpperCamelCase		,							masks_path=_UpperCamelCase		,							return_tensors="pt" )

												# verify pixel values
												SCREAMING_SNAKE_CASE           =    torch.Size([1, 3, 800, 1_066] )
												self.assertEqual(encoding["pixel_values"].shape		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
												self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,							_UpperCamelCase		,							atol=1e-4 ) )

												# verify area
												SCREAMING_SNAKE_CASE           =    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,							_UpperCamelCase ) )
												# verify boxes
												SCREAMING_SNAKE_CASE           =    torch.Size([6, 4] )
												self.assertEqual(encoding["labels"][0]["boxes"].shape		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,							_UpperCamelCase		,							atol=1e-3 ) )
												# verify image_id
												SCREAMING_SNAKE_CASE           =    torch.tensor([39_769] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,							_UpperCamelCase ) )
												# verify is_crowd
												SCREAMING_SNAKE_CASE           =    torch.tensor([0, 0, 0, 0, 0, 0] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,							_UpperCamelCase ) )
												# verify class_labels
												SCREAMING_SNAKE_CASE           =    torch.tensor([17, 17, 63, 75, 75, 93] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,							_UpperCamelCase ) )
												# verify masks
												SCREAMING_SNAKE_CASE           =    822_873
												self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,							_UpperCamelCase )
												# verify orig_size
												SCREAMING_SNAKE_CASE           =    torch.tensor([480, 640] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,							_UpperCamelCase ) )
												# verify size
												SCREAMING_SNAKE_CASE           =    torch.tensor([800, 1_066] )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,							_UpperCamelCase ) )





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, is_vision_available, logging


if is_vision_available():
							import PIL


_lowerCamelCase   :				List[Any]     			=						logging.get_logger(__name__)


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							["""pixel_values"""]




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Union[int, float] = 1 / 255		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							_UpperCamelCase				:   Optional[Union[float, List[float]]] = None		,							**_UpperCamelCase				:   Dict		,							)   ->			None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"shortest_edge": 384}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												# Default value set here for backwards compatibility where the value in config is None
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else 224 / 256
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Dict[str, int]		,							_UpperCamelCase				:   float		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BICUBIC		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )
												if "shortest_edge" not in size:
																	raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" )
												SCREAMING_SNAKE_CASE           =    size["shortest_edge"]

												if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	SCREAMING_SNAKE_CASE           =    int(shortest_edge / crop_pct )
																	SCREAMING_SNAKE_CASE           =    get_resize_output_image_size(_UpperCamelCase		,							size=_UpperCamelCase		,							default_to_square=_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=_UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    _UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




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



												'''simple docstring'''

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




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



												'''simple docstring'''

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





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else self.crop_pct
												SCREAMING_SNAKE_CASE           =    resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE           =    size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    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_resize and size is None or resample is None:
																	raise ValueError("Size and resample must be specified if do_resize is True." )

												if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError("crop_pct must be specified if size < 384." )

												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.
												SCREAMING_SNAKE_CASE           =    [to_numpy_array(_UpperCamelCase ) for image in images]

												if do_resize:
																	SCREAMING_SNAKE_CASE           =    [self.resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							crop_pct=_UpperCamelCase		,							resample=_UpperCamelCase ) for image in images]

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

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

												SCREAMING_SNAKE_CASE           =    [to_channel_dimension_format(_UpperCamelCase		,							_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    {"pixel_values": images}
												return BatchFeature(data=_UpperCamelCase		,							tensor_type=_UpperCamelCase )




