






import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


a						:			Tuple     	=					logging.getLogger(__name__)






def 							lowerCamelCase__ (       __lowerCamelCase     :	str=2    ,						__lowerCamelCase     :	Union[str, Any]=3    ,						__lowerCamelCase     :	str=16    ,						__lowerCamelCase     :	int = 10    ,						__lowerCamelCase     :	int = 2						):

  def get_dataset(__lowerCamelCase     :	List[Any]						):
    __UpperCAmelCase				:      Tuple	 =    torch.randn(batch_size * n_batches    ,						1						)
    return TensorDataset(__lowerCamelCase    ,						a * x + b + 0.1 * torch.randn(batch_size * n_batches    ,						1						)						)

  __UpperCAmelCase				:      int	 =    get_dataset(__lowerCamelCase						)
  __UpperCAmelCase				:      Tuple	 =    get_dataset(__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[int]	 =    DataLoader(__lowerCamelCase    ,						shuffle=__lowerCamelCase    ,						batch_size=__lowerCamelCase    ,						num_workers=4						)
  __UpperCAmelCase				:      int	 =    DataLoader(__lowerCamelCase    ,						shuffle=__lowerCamelCase    ,						batch_size=__lowerCamelCase    ,						num_workers=4						)
  return (train_dataloader, valid_dataloader)






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	Tuple    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	str    ,						__lowerCamelCase     :	Tuple=None						):
  __UpperCAmelCase				:      Dict	 =    []
  for epoch in range(__lowerCamelCase						):
    # Train quickly
    model.train()
    for batch in dataloader:
      __UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    batch
      __UpperCAmelCase				:      str	 =    model(__lowerCamelCase						)
      __UpperCAmelCase				:      Dict	 =    torch.nn.functional.mse_loss(__lowerCamelCase    ,						__lowerCamelCase						)
      accelerator.backward(__lowerCamelCase						)
      optimizer.step()
      optimizer.zero_grad()
    rands.append(random.random()						)  # Introduce some randomness
    if scheduler is not None:
      scheduler.step()
  return rands
class        a       (					nn.Module  ):



      """simple docstring"""


      def __init__( self   :     Union[str, Any]   )				-> Any:
        super().__init__()
        __UpperCAmelCase				:      Tuple	 =    nn.Parameter(torch.randn(1   )   )
        __UpperCAmelCase				:      str	 =    nn.Parameter(torch.randn(1   )   )




      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     List[str]   )				-> Union[str, Any]:
        return x * self.a + self.b


class        a       (					unittest.TestCase  ):



      """simple docstring"""


      def       UpperCAmelCase ( self   :     List[str]   )				-> Tuple:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      List[Any]	 =    DummyModel()
          __UpperCAmelCase				:      Union[str, Any]	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    dummy_dataloaders()
          __UpperCAmelCase				:      int	 =    ProjectConfiguration(total_limit=1						, project_dir=__lowercase						, automatic_checkpoint_naming=__lowercase   )
          # Train baseline
          __UpperCAmelCase				:      Optional[int]	 =    Accelerator(project_config=__lowercase   )
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save initial
          accelerator.save_state()

          # Save second state
          accelerator.save_state()
          self.assertEqual(len(os.listdir(accelerator.project_dir   )   )						, 1   )
      def       UpperCAmelCase ( self   :     List[Any]   )				-> Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      List[str]	 =    DummyModel()
          __UpperCAmelCase				:      int	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    dummy_dataloaders()
          # Train baseline
          __UpperCAmelCase				:      int	 =    Accelerator()
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save initial
          __UpperCAmelCase				:      Any	 =    os.path.join(__lowercase						, """initial"""   )
          accelerator.save_state(__lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      List[Any]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      int	 =    optimizer.state_dict()
          __UpperCAmelCase				:      str	 =    train(3						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      int	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Tuple	 =    optimizer.state_dict()

          # Train partially
          set_seed(42   )
          __UpperCAmelCase				:      int	 =    DummyModel()
          __UpperCAmelCase				:      Tuple	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    dummy_dataloaders()
          __UpperCAmelCase				:      Optional[Any]	 =    Accelerator()
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Tuple	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          accelerator.load_state(__lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      List[Any]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Tuple	 =    optimizer.state_dict()
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )

          __UpperCAmelCase				:      Union[str, Any]	 =    train(2						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save everything
          __UpperCAmelCase				:      List[Any]	 =    os.path.join(__lowercase						, """checkpoint"""   )
          accelerator.save_state(__lowercase   )

          # Load everything back in and make sure all states work
          accelerator.load_state(__lowercase   )
          test_rands += train(1						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      Optional[int]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      str	 =    optimizer.state_dict()
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     int   )				-> Union[str, Any]:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      Any	 =    DummyModel()
          __UpperCAmelCase				:      Dict	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    dummy_dataloaders()
          __UpperCAmelCase				:      str	 =    ProjectConfiguration(automatic_checkpoint_naming=__lowercase   )

          # Train baseline
          __UpperCAmelCase				:      Dict	 =    Accelerator(project_dir=__lowercase						, project_config=__lowercase   )
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Dict	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save initial
          accelerator.save_state()
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      List[str]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Dict	 =    optimizer.state_dict()
          __UpperCAmelCase				:      Tuple	 =    train(3						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      int	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      str	 =    optimizer.state_dict()

          # Train partially
          set_seed(42   )
          __UpperCAmelCase				:      List[Any]	 =    DummyModel()
          __UpperCAmelCase				:      List[str]	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[int]	 =    dummy_dataloaders()
          __UpperCAmelCase				:      Tuple	 =    ProjectConfiguration(iteration=1						, automatic_checkpoint_naming=__lowercase   )
          __UpperCAmelCase				:      Any	 =    Accelerator(project_dir=__lowercase						, project_config=__lowercase   )
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          accelerator.load_state(os.path.join(__lowercase						, """checkpoints"""						, """checkpoint_0"""   )   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      Tuple	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Optional[Any]	 =    optimizer.state_dict()
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )

          __UpperCAmelCase				:      Dict	 =    train(2						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save everything
          accelerator.save_state()

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(__lowercase						, """checkpoints"""						, """checkpoint_1"""   )   )
          test_rands += train(1						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          ((__UpperCAmelCase) ,					(__UpperCAmelCase))				:      List[str]	 =    model.a.item(), model.b.item()
          __UpperCAmelCase				:      Optional[int]	 =    optimizer.state_dict()
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
          self.assertEqual(__lowercase						, __lowercase   )
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Optional[Any]:
        __UpperCAmelCase				:      Tuple	 =    torch.tensor([1, 2, 3]   )
        __UpperCAmelCase				:      Dict	 =    torch.tensor([2, 3, 4]   )
        __UpperCAmelCase				:      Union[str, Any]	 =    DummyModel()
        __UpperCAmelCase				:      str	 =    torch.optim.Adam(net.parameters()   )
        __UpperCAmelCase				:      str	 =    Accelerator()
        with self.assertRaises(__lowercase   ) as ve:
          accelerator.register_for_checkpointing(__lowercase						, __lowercase						, __lowercase						, __lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    str(ve.exception   )
        self.assertTrue("""Item at index 0""" in message   )
        self.assertTrue("""Item at index 1""" in message   )
        self.assertFalse("""Item at index 2""" in message   )
        self.assertFalse("""Item at index 3""" in message   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Any:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      int	 =    DummyModel()
          __UpperCAmelCase				:      int	 =    torch.optim.Adam(params=model.parameters()						, lr=1e-3   )
          __UpperCAmelCase				:      Dict	 =    torch.optim.lr_scheduler.StepLR(__lowercase						, step_size=1						, gamma=0.99   )
          __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    dummy_dataloaders()
          __UpperCAmelCase				:      List[str]	 =    ProjectConfiguration(automatic_checkpoint_naming=__lowercase   )
          # Train baseline
          __UpperCAmelCase				:      Optional[Any]	 =    Accelerator(project_dir=__lowercase						, project_config=__lowercase   )
          __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    accelerator.prepare(
              __lowercase						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          # Save initial
          accelerator.save_state()
          __UpperCAmelCase				:      Optional[int]	 =    scheduler.state_dict()
          train(3						, __lowercase						, __lowercase						, __lowercase						, __lowercase						, __lowercase   )
          self.assertNotEqual(__lowercase						, scheduler.state_dict()   )

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(__lowercase						, """checkpoints"""						, """checkpoint_0"""   )   )
          self.assertEqual(__lowercase						, scheduler.state_dict()   )
      def       UpperCAmelCase ( self   :     Any   )				-> List[Any]:
        with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(42   )
          __UpperCAmelCase				:      Optional[int]	 =    DummyModel()
          __UpperCAmelCase				:      Optional[int]	 =    ProjectConfiguration(automatic_checkpoint_naming=__lowercase						, total_limit=2   )
          # Train baseline
          __UpperCAmelCase				:      Dict	 =    Accelerator(project_dir=__lowercase						, project_config=__lowercase   )
          __UpperCAmelCase				:      int	 =    accelerator.prepare(__lowercase   )
          # Save 3 states:
          for _ in range(11   ):
            accelerator.save_state()
          self.assertTrue(not os.path.exists(os.path.join(__lowercase						, """checkpoints"""						, """checkpoint_0"""   )   )   )
          self.assertTrue(os.path.exists(os.path.join(__lowercase						, """checkpoints"""						, """checkpoint_9"""   )   )   )
          self.assertTrue(os.path.exists(os.path.join(__lowercase						, """checkpoints"""						, """checkpoint_10"""   )   )   )




      @require_cuda
      def       UpperCAmelCase ( self   :     Dict   )				-> Optional[Any]:
        __UpperCAmelCase				:      List[str]	 =    ["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__   )]
        execute_subprocess_async(__lowercase						, env=os.environ.copy()   )


if __name__ == "__main__":
       a						:			Union[str, Any]     	=					"/tmp/accelerate/state_checkpointing"
       a						:			Any     	=					DummyModel()
       a						:			str     	=					torch.optim.Adam(params=model.parameters(), lr=1e-3)
       a						:			List[str]     	=					torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
       a	,a						:			List[Any]     	=					dummy_dataloaders()
       a						:			List[str]     	=					ProjectConfiguration(automatic_checkpoint_naming=True)
       # Train baseline
       a						:			Dict     	=					Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
       if accelerator.process_index == 0:
              if os.path.exists(savedir):
                     shutil.rmtree(savedir)
              os.makedirs(savedir)
       a	,a	,a	,a	,a						:			Dict     	=					accelerator.prepare(
           model, optimizer, train_dataloader, valid_dataloader, scheduler
       )
       a	,a						:			str     	=					accelerator.prepare(model, optimizer)
       train(3, model, train_dataloader, optimizer, accelerator, scheduler)
       # Check that the intial optimizer is loaded on the GPU
       for group in optimizer.param_groups:
              a						:			Union[str, Any]     	=					group["params"][0].device
              break
       assert param_device.type == accelerator.device.type
       a						:			str     	=					model.cpu()
       accelerator.wait_for_everyone()
       accelerator.save_state()
       accelerator.wait_for_everyone()

       # Check CPU state
       accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
       for group in optimizer.param_groups:
              a						:			Union[str, Any]     	=					group["params"][0].device
              break
       assert (
           param_device.type == torch.device("cpu").type
       ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

       # Check device state
       model.to(accelerator.device)
       accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
       for group in optimizer.param_groups:
              a						:			Dict     	=					group["params"][0].device
              break
       assert (
           param_device.type == accelerator.device.type
       ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

       # Check error
       with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
              accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
       accelerator.wait_for_everyone()
       if accelerator.process_index == 0:
              shutil.rmtree(savedir)
       accelerator.wait_for_everyone()









from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

UpperCAmelCase_							=				logging.get_logger(__name__)





def  __magic_name__  (				lowercase				)				-> List[List[ImageInput]]:

							"""simple docstring"""



							if isinstance(lowercase				,       (list, tuple)				) and isinstance(videos[0]				,       (list, tuple)				) and is_valid_image(videos[0][0]				):
														return videos

							elif isinstance(lowercase				,       (list, tuple)				) and is_valid_image(videos[0]				):
														return [videos]

							elif is_valid_image(lowercase				):
														return [[videos]]

							raise ValueError(f"""Could not make batched video from {videos}"""				)




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[Any]       =	["""pixel_values"""]






		def __init__(							self,   snake_case__ = True,   snake_case__ = None,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = True,   snake_case__ = None,   snake_case__ = True,   snake_case__ = 1 / 2_55,   snake_case__ = True,   snake_case__ = True,   snake_case__ = None,   snake_case__ = None,   **snake_case__,   )       -> None:






									"""simple docstring"""
									super().__init__(**snake_case__  )
									lowercase_	:   str  				=     size if size is not None else {"""shortest_edge""": 2_56}
									lowercase_	:   int  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									lowercase_	:   int  				=     crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
									lowercase_	:   Optional[int]  				=     get_size_dict(snake_case__,   param_name="""crop_size"""  )

									lowercase_	:   List[Any]  				=     do_resize
									lowercase_	:   int  				=     size
									lowercase_	:   int  				=     do_center_crop
									lowercase_	:   Optional[Any]  				=     crop_size
									lowercase_	:   str  				=     resample
									lowercase_	:   Any  				=     do_rescale
									lowercase_	:   Dict  				=     rescale_factor
									lowercase_	:   List[Any]  				=     offset
									lowercase_	:   int  				=     do_normalize
									lowercase_	:   str  				=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase_	:   Tuple  				=     image_std if image_std is not None else IMAGENET_STANDARD_STD






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									lowercase_	:   int  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									if "shortest_edge" in size:
																lowercase_	:   Union[str, Any]  				=     get_resize_output_image_size(snake_case__,   size["""shortest_edge"""],   default_to_square=snake_case__  )
									elif "height" in size and "width" in size:
																lowercase_	:   Dict  				=     (size["""height"""], size["""width"""])
									else:
																raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""  )
									return resize(snake_case__,   size=snake_case__,   resample=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     get_size_dict(snake_case__  )
									if "height" not in size or "width" not in size:
																raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""  )
									return center_crop(snake_case__,   size=(size["""height"""], size["""width"""]),   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = True,   snake_case__ = None,   **snake_case__,   )       -> str:






									"""simple docstring"""
									lowercase_	:   Dict  				=     image.astype(np.floataa  )
									if offset:
																lowercase_	:   Optional[Any]  				=     image - (scale / 2)
									return rescale(snake_case__,   scale=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									return normalize(snake_case__,   mean=snake_case__,   std=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   )       -> np.ndarray:






									"""simple docstring"""
									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_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."""  )

									if offset and not do_rescale:
																raise ValueError("""For offset, do_rescale must also be set to True."""  )

									# All transformations expect numpy arrays.
									lowercase_	:   Optional[int]  				=     to_numpy_array(snake_case__  )

									if do_resize:
																lowercase_	:   Dict  				=     self.resize(image=snake_case__,   size=snake_case__,   resample=snake_case__  )

									if do_center_crop:
																lowercase_	:   Optional[int]  				=     self.center_crop(snake_case__,   size=snake_case__  )

									if do_rescale:
																lowercase_	:   Dict  				=     self.rescale(image=snake_case__,   scale=snake_case__,   offset=snake_case__  )

									if do_normalize:
																lowercase_	:   Optional[Any]  				=     self.normalize(image=snake_case__,   mean=snake_case__,   std=snake_case__  )

									lowercase_	:   Tuple  				=     to_channel_dimension_format(snake_case__,   snake_case__  )
									return image






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   **snake_case__,   )       -> PIL.Image.Image:






									"""simple docstring"""
									lowercase_	:   Any  				=     do_resize if do_resize is not None else self.do_resize
									lowercase_	:   str  				=     resample if resample is not None else self.resample
									lowercase_	:   List[str]  				=     do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase_	:   Optional[Any]  				=     do_rescale if do_rescale is not None else self.do_rescale
									lowercase_	:   str  				=     rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase_	:   List[Any]  				=     offset if offset is not None else self.offset
									lowercase_	:   Optional[Any]  				=     do_normalize if do_normalize is not None else self.do_normalize
									lowercase_	:   Any  				=     image_mean if image_mean is not None else self.image_mean
									lowercase_	:   Optional[Any]  				=     image_std if image_std is not None else self.image_std

									lowercase_	:   Optional[Any]  				=     size if size is not None else self.size
									lowercase_	:   Dict  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									lowercase_	:   Union[str, Any]  				=     crop_size if crop_size is not None else self.crop_size
									lowercase_	:   Tuple  				=     get_size_dict(snake_case__,   param_name="""crop_size"""  )

									if not valid_images(snake_case__  ):
																raise ValueError(
																    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

									lowercase_	:   Optional[Any]  				=     make_batched(snake_case__  )

									lowercase_	:   Optional[Any]  				=     [
									    [
									        self._preprocess_image(
									            image=snake_case__,   do_resize=snake_case__,   size=snake_case__,   resample=snake_case__,   do_center_crop=snake_case__,   crop_size=snake_case__,   do_rescale=snake_case__,   rescale_factor=snake_case__,   offset=snake_case__,   do_normalize=snake_case__,   image_mean=snake_case__,   image_std=snake_case__,   data_format=snake_case__,   )
									        for img in video
									    ]
									    for video in videos
									]

									lowercase_	:   List[str]  				=     {"""pixel_values""": videos}
									return BatchFeature(data=snake_case__,   tensor_type=snake_case__  )