

'''simple docstring'''

from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)





@flax.struct.dataclass
class 							_snake_case (       __lowercase						):
   _A       :     List[Any]       =					4_2





@flax_register_to_config
class 							_snake_case (       nn.Module	,  __lowercase	,  __lowercase						):
   _A       :     str       =					3_2
   _A       :     str       =					4
   _A       :     Dict       =					4
   _A       :     Any       =					(
       '''CrossAttnDownBlock2D''',
       '''CrossAttnDownBlock2D''',
       '''CrossAttnDownBlock2D''',
       '''DownBlock2D''',
   )
   _A       :     Union[str, Any]       =					('''UpBlock2D''', '''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''')
   _A       :     Any       =					False
   _A       :     str       =					(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
   _A       :     Optional[int]       =					2
   _A       :     str       =					8
   _A       :     List[str]       =					None
   _A       :     Tuple       =					1_2_8_0
   _A       :     Dict       =					0.0
   _A       :     Union[str, Any]       =					False
   _A       :     str       =					jnp.floataa
   _A       :     str       =					True
   _A       :     Optional[Any]       =					0
   _A       :     List[Any]       =					False
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      jax.random.KeyArray							):
     # init input tensors
     SCREAMING_SNAKE_CASE:Dict						      =					(1, self.in_channels, self.sample_size, self.sample_size)
     SCREAMING_SNAKE_CASE:int						      =					jnp.zeros(_A     ,dtype=jnp.floataa							)
     SCREAMING_SNAKE_CASE:List[str]						      =					jnp.ones((1,)     ,dtype=jnp.intaa							)
     SCREAMING_SNAKE_CASE:str						      =					jnp.zeros((1, 1, self.cross_attention_dim)     ,dtype=jnp.floataa							)

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					jax.random.split(_A							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{"params": params_rng, "dropout": dropout_rng}

     return self.init(_A     ,_A     ,_A     ,_A							)["params"]
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:int						      =					self.block_out_channels
     SCREAMING_SNAKE_CASE:Any						      =					block_out_channels[0] * 4

     if self.num_attention_heads is not None:
       raise ValueError(
           "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."							)

     # If `num_attention_heads` is not defined (which is the case for most models)
     # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
     # The reason for this behavior is to correct for incorrectly named variables that were introduced
     # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
     # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
     # which is why we correct for the naming here.
     SCREAMING_SNAKE_CASE:str						      =					self.num_attention_heads or self.attention_head_dim

     # input
     SCREAMING_SNAKE_CASE:List[Any]						      =					nn.Conv(
         block_out_channels[0]     ,kernel_size=(3, 3)     ,strides=(1, 1)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)

     # time
     SCREAMING_SNAKE_CASE:List[str]						      =					FlaxTimesteps(
         block_out_channels[0]     ,flip_sin_to_cos=self.flip_sin_to_cos     ,freq_shift=self.config.freq_shift							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					FlaxTimestepEmbedding(_A     ,dtype=self.dtype							)

     SCREAMING_SNAKE_CASE:str						      =					self.only_cross_attention
     if isinstance(_A     ,_A							):
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					(only_cross_attention,) * len(self.down_block_types							)

     if isinstance(_A     ,_A							):
       SCREAMING_SNAKE_CASE:Any						      =					(num_attention_heads,) * len(self.down_block_types							)

     # down
     SCREAMING_SNAKE_CASE:int						      =					[]
     SCREAMING_SNAKE_CASE:Any						      =					block_out_channels[0]
     for i, down_block_type in enumerate(self.down_block_types							):
       SCREAMING_SNAKE_CASE:Optional[int]						      =					output_channel
       SCREAMING_SNAKE_CASE:Dict						      =					block_out_channels[i]
       SCREAMING_SNAKE_CASE:Any						      =					i == len(_A							) - 1

       if down_block_type == "CrossAttnDownBlock2D":
         SCREAMING_SNAKE_CASE:Tuple						      =					FlaxCrossAttnDownBlockaD(
             in_channels=_A     ,out_channels=_A     ,dropout=self.dropout     ,num_layers=self.layers_per_block     ,num_attention_heads=num_attention_heads[i]     ,add_downsample=not is_final_block     ,use_linear_projection=self.use_linear_projection     ,only_cross_attention=only_cross_attention[i]     ,use_memory_efficient_attention=self.use_memory_efficient_attention     ,dtype=self.dtype     ,)
       else:
         SCREAMING_SNAKE_CASE:Any						      =					FlaxDownBlockaD(
             in_channels=_A     ,out_channels=_A     ,dropout=self.dropout     ,num_layers=self.layers_per_block     ,add_downsample=not is_final_block     ,dtype=self.dtype     ,)

       down_blocks.append(_A							)
     SCREAMING_SNAKE_CASE:Tuple						      =					down_blocks

     # mid
     SCREAMING_SNAKE_CASE:str						      =					FlaxUNetMidBlockaDCrossAttn(
         in_channels=block_out_channels[-1]     ,dropout=self.dropout     ,num_attention_heads=num_attention_heads[-1]     ,use_linear_projection=self.use_linear_projection     ,use_memory_efficient_attention=self.use_memory_efficient_attention     ,dtype=self.dtype     ,)

     # up
     SCREAMING_SNAKE_CASE:Dict						      =					[]
     SCREAMING_SNAKE_CASE:int						      =					list(reversed(_A							)							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					list(reversed(_A							)							)
     SCREAMING_SNAKE_CASE:Dict						      =					list(reversed(_A							)							)
     SCREAMING_SNAKE_CASE:List[str]						      =					reversed_block_out_channels[0]
     for i, up_block_type in enumerate(self.up_block_types							):
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					output_channel
       SCREAMING_SNAKE_CASE:Tuple						      =					reversed_block_out_channels[i]
       SCREAMING_SNAKE_CASE:Any						      =					reversed_block_out_channels[min(i + 1     ,len(_A							) - 1							)]

       SCREAMING_SNAKE_CASE:List[Any]						      =					i == len(_A							) - 1

       if up_block_type == "CrossAttnUpBlock2D":
         SCREAMING_SNAKE_CASE:Tuple						      =					FlaxCrossAttnUpBlockaD(
             in_channels=_A     ,out_channels=_A     ,prev_output_channel=_A     ,num_layers=self.layers_per_block + 1     ,num_attention_heads=reversed_num_attention_heads[i]     ,add_upsample=not is_final_block     ,dropout=self.dropout     ,use_linear_projection=self.use_linear_projection     ,only_cross_attention=only_cross_attention[i]     ,use_memory_efficient_attention=self.use_memory_efficient_attention     ,dtype=self.dtype     ,)
       else:
         SCREAMING_SNAKE_CASE:List[Any]						      =					FlaxUpBlockaD(
             in_channels=_A     ,out_channels=_A     ,prev_output_channel=_A     ,num_layers=self.layers_per_block + 1     ,add_upsample=not is_final_block     ,dropout=self.dropout     ,dtype=self.dtype     ,)

       up_blocks.append(_A							)
       SCREAMING_SNAKE_CASE:Any						      =					output_channel
     SCREAMING_SNAKE_CASE:str						      =					up_blocks

     # out
     SCREAMING_SNAKE_CASE:List[str]						      =					nn.GroupNorm(num_groups=32     ,epsilon=1e-5							)
     SCREAMING_SNAKE_CASE:int						      =					nn.Conv(
         self.out_channels     ,kernel_size=(3, 3)     ,strides=(1, 1)     ,padding=((1, 1), (1, 1))     ,dtype=self.dtype     ,)







   def __call__( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Dict     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      List[str]=None     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      bool = False     ,):
     # 1. time
     if not isinstance(_A     ,jnp.ndarray							):
       SCREAMING_SNAKE_CASE:Dict						      =					jnp.array([timesteps]     ,dtype=jnp.intaa							)
     elif isinstance(_A     ,jnp.ndarray							) and len(timesteps.shape							) == 0:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					timesteps.astype(dtype=jnp.floataa							)
       SCREAMING_SNAKE_CASE:int						      =					jnp.expand_dims(_A     ,0							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.time_proj(_A							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.time_embedding(_A							)

     # 2. pre-process
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					jnp.transpose(_A     ,(0, 2, 3, 1)							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.conv_in(_A							)

     # 3. down
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					(sample,)
     for down_block in self.down_blocks:
       if isinstance(_A     ,_A							):
         SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					down_block(_A     ,_A     ,_A     ,deterministic=not train							)
       else:
         SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					down_block(_A     ,_A     ,deterministic=not train							)
       down_block_res_samples += res_samples

     if down_block_additional_residuals is not None:
       SCREAMING_SNAKE_CASE:str						      =					()

       for down_block_res_sample, down_block_additional_residual in zip(
           _A     ,_A							):
         down_block_res_sample += down_block_additional_residual
         new_down_block_res_samples += (down_block_res_sample,)

       SCREAMING_SNAKE_CASE:Optional[Any]						      =					new_down_block_res_samples

     # 4. mid
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.mid_block(_A     ,_A     ,_A     ,deterministic=not train							)

     if mid_block_additional_residual is not None:
       sample += mid_block_additional_residual

     # 5. up
     for up_block in self.up_blocks:
       SCREAMING_SNAKE_CASE:Tuple						      =					down_block_res_samples[-(self.layers_per_block + 1) :]
       SCREAMING_SNAKE_CASE:Optional[int]						      =					down_block_res_samples[: -(self.layers_per_block + 1)]
       if isinstance(_A     ,_A							):
         SCREAMING_SNAKE_CASE:Optional[Any]						      =					up_block(
             _A     ,temb=_A     ,encoder_hidden_states=_A     ,res_hidden_states_tuple=_A     ,deterministic=not train     ,)
       else:
         SCREAMING_SNAKE_CASE:List[str]						      =					up_block(_A     ,temb=_A     ,res_hidden_states_tuple=_A     ,deterministic=not train							)

        # 6. post-process
     SCREAMING_SNAKE_CASE:Tuple						      =					self.conv_norm_out(_A							)
     SCREAMING_SNAKE_CASE:str						      =					nn.silu(_A							)
     SCREAMING_SNAKE_CASE:List[str]						      =					self.conv_out(_A							)
     SCREAMING_SNAKE_CASE:int						      =					jnp.transpose(_A     ,(0, 3, 1, 2)							)

     if not return_dict:
       return (sample,)

     return FlaxUNetaDConditionOutput(sample=_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,
    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       :     int       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,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__			:      Union[str, Any]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Any						      =					size if size is not None else {"shortest_edge": 224}
     SCREAMING_SNAKE_CASE:int						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:str						      =					crop_size if crop_size is not None else {"height": 224, "width": 224}
     SCREAMING_SNAKE_CASE:str						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__     ,param_name="crop_size"							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					do_resize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					size
     SCREAMING_SNAKE_CASE:List[Any]						      =					resample
     SCREAMING_SNAKE_CASE:Any						      =					do_center_crop
     SCREAMING_SNAKE_CASE:List[Any]						      =					crop_size
     SCREAMING_SNAKE_CASE:Tuple						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Dict						      =					do_normalize
     SCREAMING_SNAKE_CASE:int						      =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_convert_rgb
   def     __UpperCamelCase						( self			:      Optional[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__			:      Any     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Dict						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=size["shortest_edge"]     ,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[int]     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__							)
     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(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__			:      List[Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Any     ,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__			:      Tuple     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      int     ,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__			:      List[str]     ,):
     SCREAMING_SNAKE_CASE:Any						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Dict						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,param_name="size"     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE:Optional[int]						      =					crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE:str						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,param_name="crop_size"     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:Tuple						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:List[Any]						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:List[str]						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Any						      =					image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE:str						      =					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:List[Any]						      =					[convert_to_rgb(SCREAMING_SNAKE_CASE__							) for image in images]

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

     if do_resize:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[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:Any						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

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

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

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






