

'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 CLIPImageProcessor





class 							_snake_case	(    unittest.TestCase  ):
   def __init__(	self     : Optional[int]       ,SCREAMING_SNAKE_CASE__     : str       ,SCREAMING_SNAKE_CASE__     : List[Any]=7       ,SCREAMING_SNAKE_CASE__     : List[str]=3       ,SCREAMING_SNAKE_CASE__     : Optional[int]=18       ,SCREAMING_SNAKE_CASE__     : Dict=30       ,SCREAMING_SNAKE_CASE__     : Optional[int]=400       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=True       ,SCREAMING_SNAKE_CASE__     : Any=None       ,SCREAMING_SNAKE_CASE__     : Tuple=True       ,SCREAMING_SNAKE_CASE__     : Optional[int]=None       ,SCREAMING_SNAKE_CASE__     : Optional[int]=True       ,SCREAMING_SNAKE_CASE__     : str=[0.48_145_466, 0.4_578_275, 0.40_821_073]       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711]       ,SCREAMING_SNAKE_CASE__     : str=True       ,):
     SCREAMING_SNAKE_CASE:List[str]   =   size if size is not None else {"shortest_edge": 20}
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   crop_size if crop_size is not None else {"height": 18, "width": 18}
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   parent
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   batch_size
     SCREAMING_SNAKE_CASE:List[Any]   =   num_channels
     SCREAMING_SNAKE_CASE:Any   =   image_size
     SCREAMING_SNAKE_CASE:Optional[Any]   =   min_resolution
     SCREAMING_SNAKE_CASE:Tuple   =   max_resolution
     SCREAMING_SNAKE_CASE:Any   =   do_resize
     SCREAMING_SNAKE_CASE:int   =   size
     SCREAMING_SNAKE_CASE:str   =   do_center_crop
     SCREAMING_SNAKE_CASE:Dict   =   crop_size
     SCREAMING_SNAKE_CASE:List[str]   =   do_normalize
     SCREAMING_SNAKE_CASE:List[str]   =   image_mean
     SCREAMING_SNAKE_CASE:List[Any]   =   image_std
     SCREAMING_SNAKE_CASE:List[Any]   =   do_convert_rgb
   def       __UpperCamelCase  (	self     : Dict							):
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
     }







   def       __UpperCamelCase  (	self     : str       ,SCREAMING_SNAKE_CASE__     : Optional[int]=False       ,SCREAMING_SNAKE_CASE__     : Optional[int]=False       ,SCREAMING_SNAKE_CASE__     : Dict=False							):
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
       SCREAMING_SNAKE_CASE:Optional[int]   =   []
       for i in range(self.batch_size							):
         image_inputs.append(
             np.random.randint(
                 255       ,size=(self.num_channels, self.max_resolution, self.max_resolution)       ,dtype=np.uinta							)							)
     else:
       SCREAMING_SNAKE_CASE:str   =   []
       for i in range(self.batch_size							):
         SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:List[str]   =   np.random.choice(np.arange(self.min_resolution       ,self.max_resolution							)       ,2							)
         image_inputs.append(np.random.randint(255       ,size=(self.num_channels, width, height)       ,dtype=np.uinta							)							)

     if not numpify and not torchify:
       # PIL expects the channel dimension as last dimension
       SCREAMING_SNAKE_CASE:Dict   =   [Image.fromarray(np.moveaxis(__UpperCamelCase       ,0       ,-1							)							) for x in image_inputs]

     if torchify:
       SCREAMING_SNAKE_CASE:List[str]   =   [torch.from_numpy(__UpperCamelCase							) for x in image_inputs]

     return image_inputs





@require_torch
@require_vision
class 							_snake_case	(    UpperCAmelCase_		,					unittest.TestCase  ):
   _A    :       str      				=   CLIPImageProcessor if is_vision_available() else None
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:int   =   CLIPImageProcessingTester(self							)
   @property
   def       __UpperCamelCase  (	self     : Any							):
     return self.image_processor_tester.prepare_image_processor_dict()
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:Optional[int]   =   self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"do_resize"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"size"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"do_center_crop"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"center_crop"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"do_normalize"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"image_mean"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"image_std"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"do_convert_rgb"							)							)
   def       __UpperCamelCase  (	self     : Any							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   self.image_processing_class.from_dict(self.image_processor_dict							)
     self.assertEqual(image_processor.size       ,{"shortest_edge": 20}							)
     self.assertEqual(image_processor.crop_size       ,{"height": 18, "width": 18}							)

     SCREAMING_SNAKE_CASE:Dict   =   self.image_processing_class.from_dict(self.image_processor_dict       ,size=42       ,crop_size=84							)
     self.assertEqual(image_processor.size       ,{"shortest_edge": 42}							)
     self.assertEqual(image_processor.crop_size       ,{"height": 84, "width": 84}							)
   def       __UpperCamelCase  (	self     : Union[str, Any]							):
     pass
   def       __UpperCamelCase  (	self     : List[str]							):
     # Initialize image_processing
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     SCREAMING_SNAKE_CASE:Optional[int]   =   self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase							)
     for image in image_inputs:
       self.assertIsInstance(__UpperCamelCase       ,Image.Image							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:Any   =   image_processing(image_inputs[0]       ,return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape       ,(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )       ,)

     # Test batched
     SCREAMING_SNAKE_CASE:Any   =   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,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )       ,)
   def       __UpperCamelCase  (	self     : Optional[int]							):
     # Initialize image_processing
     SCREAMING_SNAKE_CASE:List[Any]   =   self.image_processing_class(**self.image_processor_dict							)
     # create random numpy tensors
     SCREAMING_SNAKE_CASE:Any   =   self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase       ,numpify=__UpperCamelCase							)
     for image in image_inputs:
       self.assertIsInstance(__UpperCamelCase       ,np.ndarray							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:int   =   image_processing(image_inputs[0]       ,return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape       ,(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )       ,)

     # Test batched
     SCREAMING_SNAKE_CASE:Optional[int]   =   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,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )       ,)







   def       __UpperCamelCase  (	self     : List[str]							):
     # Initialize image_processing
     SCREAMING_SNAKE_CASE:List[Any]   =   self.image_processing_class(**self.image_processor_dict							)
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE:int   =   self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase       ,torchify=__UpperCamelCase							)
     for image in image_inputs:
       self.assertIsInstance(__UpperCamelCase       ,torch.Tensor							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:str   =   image_processing(image_inputs[0]       ,return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape       ,(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )       ,)

     # Test batched
     SCREAMING_SNAKE_CASE:Optional[Any]   =   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,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )       ,)





@require_torch
@require_vision
class 							_snake_case	(    UpperCAmelCase_		,					unittest.TestCase  ):
   _A    :       Optional[int]      				=   CLIPImageProcessor if is_vision_available() else None
   def       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:Any   =   CLIPImageProcessingTester(self       ,num_channels=4							)
     SCREAMING_SNAKE_CASE:List[Any]   =   3
   @property
   def       __UpperCamelCase  (	self     : Optional[Any]							):
     return self.image_processor_tester.prepare_image_processor_dict()
   def       __UpperCamelCase  (	self     : List[Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]   =   self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"do_resize"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"size"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"do_center_crop"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"center_crop"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"do_normalize"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"image_mean"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"image_std"							)							)
     self.assertTrue(hasattr(__UpperCamelCase       ,"do_convert_rgb"							)							)
   def       __UpperCamelCase  (	self     : Dict							):
     pass







   def       __UpperCamelCase  (	self     : Dict							):
     # Initialize image_processing
     SCREAMING_SNAKE_CASE:Optional[Any]   =   self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     SCREAMING_SNAKE_CASE:Dict   =   self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase							)
     for image in image_inputs:
       self.assertIsInstance(__UpperCamelCase       ,Image.Image							)

     # Test not batched input
     SCREAMING_SNAKE_CASE:Optional[Any]   =   image_processing(image_inputs[0]       ,return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape       ,(
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )       ,)

     # Test batched
     SCREAMING_SNAKE_CASE:Any   =   image_processing(__UpperCamelCase       ,return_tensors="pt"							).pixel_values
     self.assertEqual(
         encoded_images.shape       ,(
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )       ,)









'''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,
    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


A_      =							logging.get_logger(__name__)


if is_vision_available():
    import PIL





class 							_snake_case	(    _a  ):
   _A    :       List[str]      				=   ['''pixel_values''']
   def __init__(	self     : int       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__     : PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Dict[str, int] = None       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Union[int, float] = 1 / 255       ,SCREAMING_SNAKE_CASE__     : bool = True       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : Optional[Union[float, List[float]]] = None       ,SCREAMING_SNAKE_CASE__     : bool = True       ,**SCREAMING_SNAKE_CASE__     : int       ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]   =   size if size is not None else {"shortest_edge": 224}
     SCREAMING_SNAKE_CASE:str   =   get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple   =   crop_size if crop_size is not None else {"height": 224, "width": 224}
     SCREAMING_SNAKE_CASE:Any   =   get_size_dict(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Tuple   =   do_resize
     SCREAMING_SNAKE_CASE:Any   =   size
     SCREAMING_SNAKE_CASE:Any   =   resample
     SCREAMING_SNAKE_CASE:Tuple   =   do_center_crop
     SCREAMING_SNAKE_CASE:List[str]   =   crop_size
     SCREAMING_SNAKE_CASE:Dict   =   do_rescale
     SCREAMING_SNAKE_CASE:Optional[Any]   =   rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]   =   do_normalize
     SCREAMING_SNAKE_CASE:List[str]   =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE:Dict   =   image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE:Any   =   do_convert_rgb
   def       __UpperCamelCase  (	self     : int       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Dict[str, int]       ,SCREAMING_SNAKE_CASE__     : PILImageResampling = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : str       ,):
     SCREAMING_SNAKE_CASE:List[str]   =   get_size_dict(SCREAMING_SNAKE_CASE__       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str   =   get_resize_output_image_size(
         SCREAMING_SNAKE_CASE__       ,size=(size["height"], size["width"])       ,default_to_square=SCREAMING_SNAKE_CASE__							)
     return resize(SCREAMING_SNAKE_CASE__       ,size=SCREAMING_SNAKE_CASE__       ,resample=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Union[str, Any]       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Dict[str, int]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Optional[Any]       ,):
     SCREAMING_SNAKE_CASE:str   =   get_size_dict(SCREAMING_SNAKE_CASE__							)
     return center_crop(SCREAMING_SNAKE_CASE__       ,size=(size["height"], size["width"])       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Dict       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Union[int, float]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Optional[int]       ,):
     return rescale(SCREAMING_SNAKE_CASE__       ,scale=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)
   def       __UpperCamelCase  (	self     : Tuple       ,SCREAMING_SNAKE_CASE__     : np.ndarray       ,SCREAMING_SNAKE_CASE__     : Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__     : Union[float, List[float]]       ,SCREAMING_SNAKE_CASE__     : Optional[Union[str, ChannelDimension]] = None       ,**SCREAMING_SNAKE_CASE__     : Any       ,):
     return normalize(SCREAMING_SNAKE_CASE__       ,mean=SCREAMING_SNAKE_CASE__       ,std=SCREAMING_SNAKE_CASE__       ,data_format=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__							)







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

     SCREAMING_SNAKE_CASE:Any   =   make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_SNAKE_CASE__							):
       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:
       SCREAMING_SNAKE_CASE:Any   =   [convert_to_rgb(SCREAMING_SNAKE_CASE__							) for image in images]

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

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

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

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

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

     SCREAMING_SNAKE_CASE:Union[str, Any]   =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__							) for image in images]

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






