

'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:str						      =					SwinaSRConfig()

  if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    SCREAMING_SNAKE_CASE:Dict						      =					4
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					4
    SCREAMING_SNAKE_CASE:str						      =					48
    SCREAMING_SNAKE_CASE:List[str]						      =					"""pixelshuffle_aux"""
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    SCREAMING_SNAKE_CASE:Dict						      =					[6, 6, 6, 6]
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					60
    SCREAMING_SNAKE_CASE:int						      =					[6, 6, 6, 6]
    SCREAMING_SNAKE_CASE:Dict						      =					"""pixelshuffledirect"""
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    SCREAMING_SNAKE_CASE:str						      =					4
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					"""nearest+conv"""
  elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
    SCREAMING_SNAKE_CASE:int						      =					1
    SCREAMING_SNAKE_CASE:Optional[int]						      =					1
    SCREAMING_SNAKE_CASE:str						      =					126
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					7
    SCREAMING_SNAKE_CASE:Tuple						      =					255.0
    SCREAMING_SNAKE_CASE:Any						      =					""""""

  return config





def    A_  ( snake_case      ,       snake_case ):
  if "patch_embed.proj" in name and "layers" not in name:
    SCREAMING_SNAKE_CASE:Optional[int]						      =					name.replace("patch_embed.proj"      ,       "embeddings.patch_embeddings.projection" )
  if "patch_embed.norm" in name:
    SCREAMING_SNAKE_CASE:List[Any]						      =					name.replace("patch_embed.norm"      ,       "embeddings.patch_embeddings.layernorm" )
  if "layers" in name:
    SCREAMING_SNAKE_CASE:int						      =					name.replace("layers"      ,       "encoder.stages" )
  if "residual_group.blocks" in name:
    SCREAMING_SNAKE_CASE:Dict						      =					name.replace("residual_group.blocks"      ,       "layers" )
  if "attn.proj" in name:
    SCREAMING_SNAKE_CASE:int						      =					name.replace("attn.proj"      ,       "attention.output.dense" )
  if "attn" in name:
    SCREAMING_SNAKE_CASE:List[str]						      =					name.replace("attn"      ,       "attention.self" )
  if "norm1" in name:
    SCREAMING_SNAKE_CASE:int						      =					name.replace("norm1"      ,       "layernorm_before" )
  if "norm2" in name:
    SCREAMING_SNAKE_CASE:Tuple						      =					name.replace("norm2"      ,       "layernorm_after" )
  if "mlp.fc1" in name:
    SCREAMING_SNAKE_CASE:Tuple						      =					name.replace("mlp.fc1"      ,       "intermediate.dense" )
  if "mlp.fc2" in name:
    SCREAMING_SNAKE_CASE:str						      =					name.replace("mlp.fc2"      ,       "output.dense" )
  if "q_bias" in name:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					name.replace("q_bias"      ,       "query.bias" )
  if "k_bias" in name:
    SCREAMING_SNAKE_CASE:List[str]						      =					name.replace("k_bias"      ,       "key.bias" )
  if "v_bias" in name:
    SCREAMING_SNAKE_CASE:str						      =					name.replace("v_bias"      ,       "value.bias" )
  if "cpb_mlp" in name:
    SCREAMING_SNAKE_CASE:Dict						      =					name.replace("cpb_mlp"      ,       "continuous_position_bias_mlp" )
  if "patch_embed.proj" in name:
    SCREAMING_SNAKE_CASE:Dict						      =					name.replace("patch_embed.proj"      ,       "patch_embed.projection" )

  if name == "norm.weight":
    SCREAMING_SNAKE_CASE:int						      =					"""layernorm.weight"""
  if name == "norm.bias":
    SCREAMING_SNAKE_CASE:List[Any]						      =					"""layernorm.bias"""

  if "conv_first" in name:
    SCREAMING_SNAKE_CASE:List[str]						      =					name.replace("conv_first"      ,       "first_convolution" )

  if (
      "upsample" in name
      or "conv_before_upsample" in name
      or "conv_bicubic" in name
      or "conv_up" in name
      or "conv_hr" in name
      or "conv_last" in name
      or "aux" in name
  ):
    # heads
    if "conv_last" in name:
      SCREAMING_SNAKE_CASE:Tuple						      =					name.replace("conv_last"      ,       "final_convolution" )
    if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
      if "conv_before_upsample.0" in name:
        SCREAMING_SNAKE_CASE:Optional[int]						      =					name.replace("conv_before_upsample.0"      ,       "conv_before_upsample" )
      if "upsample.0" in name:
        SCREAMING_SNAKE_CASE:Tuple						      =					name.replace("upsample.0"      ,       "upsample.convolution_0" )
      if "upsample.2" in name:
        SCREAMING_SNAKE_CASE:List[Any]						      =					name.replace("upsample.2"      ,       "upsample.convolution_1" )
      SCREAMING_SNAKE_CASE:List[str]						      =					"""upsample.""" + name
    elif config.upsampler == "pixelshuffledirect":
      SCREAMING_SNAKE_CASE:Dict						      =					name.replace("upsample.0.weight"      ,       "upsample.conv.weight" )
      SCREAMING_SNAKE_CASE:str						      =					name.replace("upsample.0.bias"      ,       "upsample.conv.bias" )
    else:
      pass
  else:
    SCREAMING_SNAKE_CASE:Any						      =					"""swin2sr.""" + name

  return name





def    A_  ( snake_case      ,       snake_case ):
  for key in orig_state_dict.copy().keys():
    SCREAMING_SNAKE_CASE:str						      =					orig_state_dict.pop(snake_case_ )

    if "qkv" in key:
      SCREAMING_SNAKE_CASE:Dict						      =					key.split("." )
      SCREAMING_SNAKE_CASE:Any						      =					int(key_split[1] )
      SCREAMING_SNAKE_CASE:Any						      =					int(key_split[4] )
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					config.embed_dim

      if "weight" in key:
        SCREAMING_SNAKE_CASE:Tuple						      =					val[:dim, :]
        SCREAMING_SNAKE_CASE:Any						      =					val[dim : dim * 2, :]
        SCREAMING_SNAKE_CASE:List[str]						      =					val[-dim:, :]
      else:
        SCREAMING_SNAKE_CASE:Any						      =					val[:dim]
        SCREAMING_SNAKE_CASE:Tuple						      =					val[dim : dim * 2]
        SCREAMING_SNAKE_CASE:Optional[int]						      =					val[-dim:]
      pass
    else:
      SCREAMING_SNAKE_CASE:Dict						      =					val

  return orig_state_dict





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Tuple						      =					get_config(snake_case_ )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					SwinaSRForImageSuperResolution(snake_case_ )
  model.eval()

  SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.hub.load_state_dict_from_url(snake_case_      ,       map_location="cpu" )
  SCREAMING_SNAKE_CASE:str						      =					convert_state_dict(snake_case_      ,       snake_case_ )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model.load_state_dict(snake_case_      ,       strict=snake_case_ )

  if len(snake_case_ ) > 0:
    raise ValueError("Missing keys when converting: {}".format(snake_case_ ) )
  for key in unexpected_keys:
    if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
      raise ValueError(F'''Unexpected key {key} in state_dict''' )

    # verify values
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"""https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
  SCREAMING_SNAKE_CASE:str						      =					Image.open(requests.get(snake_case_      ,       stream=snake_case_ ).raw ).convert("RGB" )
  SCREAMING_SNAKE_CASE:List[str]						      =					SwinaSRImageProcessor()
  # pixel_values = processor(image, return_tensors="pt").pixel_values

  SCREAMING_SNAKE_CASE:str						      =					126 if """Jpeg""" in checkpoint_url else 256
  SCREAMING_SNAKE_CASE:List[str]						      =					Compose(
      [
          Resize((image_size, image_size) ),
          ToTensor(),
          Normalize(mean=[0.485, 0.456, 0.406]      ,       std=[0.229, 0.224, 0.225] ),
      ] )
  SCREAMING_SNAKE_CASE:List[str]						      =					transforms(snake_case_ ).unsqueeze(0 )

  if config.num_channels == 1:
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					pixel_values[:, 0, :, :].unsqueeze(1 )

  SCREAMING_SNAKE_CASE:int						      =					model(snake_case_ )

  # assert values
  if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
    SCREAMING_SNAKE_CASE:int						      =					torch.Size([1, 3, 512, 512] )
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.tensor(
        [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]] )
  elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
    SCREAMING_SNAKE_CASE:Any						      =					torch.Size([1, 3, 1024, 1024] )
    SCREAMING_SNAKE_CASE:str						      =					torch.tensor(
        [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]] )
  elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
    # TODO values didn't match exactly here
    SCREAMING_SNAKE_CASE:int						      =					torch.Size([1, 3, 1024, 1024] )
    SCREAMING_SNAKE_CASE:List[Any]						      =					torch.tensor(
        [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]] )
  elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
    SCREAMING_SNAKE_CASE:str						      =					torch.Size([1, 3, 512, 512] )
    SCREAMING_SNAKE_CASE:List[Any]						      =					torch.tensor(
        [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]] )
  elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					torch.Size([1, 3, 1024, 1024] )
    SCREAMING_SNAKE_CASE:Optional[int]						      =					torch.tensor(
        [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]] )

  assert (
      outputs.reconstruction.shape == expected_shape
  ), F'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
  assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]      ,       snake_case_      ,       atol=1e-3 )
  print("Looks ok!" )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{
      """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
          """swin2SR-classical-sr-x2-64"""
      ),
      """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
          """swin2SR-classical-sr-x4-64"""
      ),
      """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
          """swin2SR-compressed-sr-x4-48"""
      ),
      """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
          """swin2SR-lightweight-x2-64"""
      ),
      """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
          """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
      ),
  }
  SCREAMING_SNAKE_CASE:Any						      =					url_to_name[checkpoint_url]

  if pytorch_dump_folder_path is not None:
    print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
    model.save_pretrained(snake_case_ )
    print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
    processor.save_pretrained(snake_case_ )

  if push_to_hub:
    model.push_to_hub(F'''caidas/{model_name}''' )
    processor.push_to_hub(F'''caidas/{model_name}''' )


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--checkpoint_url",
        default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
        type=str,
        help="URL of the original Swin2SR checkpoint you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")

    A_       						=							parser.parse_args()
    convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''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,
    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


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Union[str, Any]       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BILINEAR     ,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__			:      List[str]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					size if size is not None else {"shortest_edge": 384}
     SCREAMING_SNAKE_CASE:List[str]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					do_resize
     SCREAMING_SNAKE_CASE:List[Any]						      =					size
     # Default value set here for backwards compatibility where the value in config is None
     SCREAMING_SNAKE_CASE:Dict						      =					crop_pct if crop_pct is not None else 224 / 256
     SCREAMING_SNAKE_CASE:Any						      =					resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[int]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_normalize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE:Tuple						      =					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      float     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     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'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Any						      =					size["shortest_edge"]

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       SCREAMING_SNAKE_CASE:Any						      =					int(shortest_edge / crop_pct							)
       SCREAMING_SNAKE_CASE:Any						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     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__			:      Union[str, 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			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = 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__			:      Optional[Union[str, TensorType]] = None     ,SCREAMING_SNAKE_CASE__			:      ChannelDimension = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:List[str]						      =					crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:List[str]						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:str						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:str						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Any						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[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 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:int						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

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

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

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

     SCREAMING_SNAKE_CASE: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__							)






