
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_A	  =					16
_A	  =					32


def 				lowerCamelCase__				(						a__  :  Accelerator	,      a__  :  int = 16     )		->	Optional[Any]:
       UpperCamelCase_							       =       AutoTokenizer.from_pretrained("""bert-base-cased"""     )
       UpperCamelCase_							       =       load_dataset("""glue"""	,      """mrpc"""     )

       def tokenize_function(a__  :  Tuple     ):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase_							       =       tokenizer(examples["""sentence1"""]	,      examples["""sentence2"""]	,      truncation=a__	,      max_length=a__     )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCamelCase_							       =       datasets.map(
                  a__	,      batched=a__	,      remove_columns=["""idx""", """sentence1""", """sentence2"""]	,      )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       UpperCamelCase_							       =       tokenized_datasets.rename_column("""label"""	,      """labels"""     )

       def collate_fn(a__  :  Any     ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCamelCase_							       =       128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCamelCase_							       =       16
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase_							       =       8
              else:
                     UpperCamelCase_							       =       None

              return tokenizer.pad(
                  a__	,      padding="""longest"""	,      max_length=a__	,      pad_to_multiple_of=a__	,      return_tensors="""pt"""	,      )

       # Instantiate dataloaders.
       UpperCamelCase_							       =       DataLoader(
           tokenized_datasets["""train"""]	,      shuffle=a__	,      collate_fn=a__	,      batch_size=a__     )
       UpperCamelCase_							       =       DataLoader(
           tokenized_datasets["""validation"""]	,      shuffle=a__	,      collate_fn=a__	,      batch_size=a__     )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      _A	  =					mocked_dataloaders  # noqa: F811


def 				lowerCamelCase__				(						a__  :  List[str]	,      a__  :  Union[str, Any]     )		->	List[Any]:
       # For testing only
       if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,      a__     ) == "1":
              UpperCamelCase_							       =       2
       # Initialize accelerator
       UpperCamelCase_							       =       Accelerator(cpu=args.cpu	,      mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCamelCase_							       =       config["""lr"""]
       UpperCamelCase_							       =       int(config["""num_epochs"""]     )
       UpperCamelCase_							       =       int(config["""seed"""]     )
       UpperCamelCase_							       =       int(config["""batch_size"""]     )

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps	,      """isdigit"""     ):
              if args.checkpointing_steps == "epoch":
                     UpperCamelCase_							       =       args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCamelCase_							       =       int(args.checkpointing_steps     )
              else:
                     raise ValueError(
                         f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''     )
       else:
              UpperCamelCase_							       =       None

       set_seed(a__     )

       UpperCamelCase_       ,				UpperCamelCase_							       =       get_dataloaders(a__	,      a__     )
       UpperCamelCase_							       =       evaluate.load("""glue"""	,      """mrpc"""     )

       # If the batch size is too big we use gradient accumulation
       UpperCamelCase_							       =       1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCamelCase_							       =       batch_size // MAX_GPU_BATCH_SIZE
              UpperCamelCase_							       =       MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCamelCase_							       =       AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""	,      return_dict=a__     )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       UpperCamelCase_							       =       model.to(accelerator.device     )

       # Instantiate optimizer
       UpperCamelCase_							       =       AdamW(params=model.parameters()	,      lr=a__     )

       # Instantiate scheduler
       UpperCamelCase_							       =       get_linear_schedule_with_warmup(
           optimizer=a__	,      num_warmup_steps=100	,      num_training_steps=(len(a__     ) * num_epochs) // gradient_accumulation_steps	,      )

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       accelerator.prepare(
           a__	,      a__	,      a__	,      a__	,      a__     )

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       UpperCamelCase_							       =       0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCamelCase_							       =       0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''     )
                     accelerator.load_state(args.resume_from_checkpoint     )
                     UpperCamelCase_							       =       os.path.basename(args.resume_from_checkpoint     )
              else:
                     # Get the most recent checkpoint
                     UpperCamelCase_							       =       [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
                     dirs.sort(key=os.path.getctime     )
                     UpperCamelCase_							       =       dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCamelCase_							       =       os.path.splitext(a__     )[0]

              if "epoch" in training_difference:
                     UpperCamelCase_							       =       int(training_difference.replace("""epoch_"""	,      """"""     )     ) + 1
                     UpperCamelCase_							       =       None
              else:
                     UpperCamelCase_							       =       int(training_difference.replace("""step_"""	,      """"""     )     )
                     UpperCamelCase_							       =       resume_step // len(a__     )
                     resume_step -= starting_epoch * len(a__     )

    # Now we train the model
       for epoch in range(a__	,      a__     ):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     UpperCamelCase_							       =       accelerator.skip_first_batches(a__	,      a__     )
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCamelCase_							       =       train_dataloader
              for step, batch in enumerate(a__     ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device     )
                     UpperCamelCase_							       =       model(**a__     )
                     UpperCamelCase_							       =       outputs.loss
                     UpperCamelCase_							       =       loss / gradient_accumulation_steps
                     accelerator.backward(a__     )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(a__	,      a__     ):
                            UpperCamelCase_							       =       f'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCamelCase_							       =       os.path.join(args.output_dir	,      a__     )
                                   accelerator.save_state(a__     )

              model.eval()
              for step, batch in enumerate(a__     ):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device     )
                     with torch.no_grad():
                            UpperCamelCase_							       =       model(**a__     )
                     UpperCamelCase_							       =       outputs.logits.argmax(dim=-1     )
                     UpperCamelCase_       ,				UpperCamelCase_							       =       accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
                     metric.add_batch(
                         predictions=a__	,      references=a__	,      )

              UpperCamelCase_							       =       metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f'''epoch {epoch}:'''	,      a__     )

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     UpperCamelCase_							       =       f'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCamelCase_							       =       os.path.join(args.output_dir	,      a__     )
                     accelerator.save_state(a__     )


def 				lowerCamelCase__				(						)		->	Optional[int]:
       UpperCamelCase_							       =       argparse.ArgumentParser(description="""Simple example of training script."""     )
       parser.add_argument(
           """--mixed_precision"""	,      type=a__	,      default=a__	,      choices=["""no""", """fp16""", """bf16""", """fp8"""]	,      help="""Whether to use mixed precision. Choose"""
           """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
           """and an Nvidia Ampere GPU."""	,      )
       parser.add_argument("""--cpu"""	,      action="""store_true"""	,      help="""If passed, will train on the CPU."""     )
       parser.add_argument(
           """--checkpointing_steps"""	,      type=a__	,      default=a__	,      help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""	,      )
       parser.add_argument(
           """--output_dir"""	,      type=a__	,      default="""."""	,      help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,      )
       parser.add_argument(
           """--resume_from_checkpoint"""	,      type=a__	,      default=a__	,      help="""If the training should continue from a checkpoint folder."""	,      )
       UpperCamelCase_							       =       parser.parse_args()
       UpperCamelCase_							       =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(a__	,      a__     )


if __name__ == "__main__":
      main()





import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				List[str]         =    CONFIG_NAME

 @register_to_config
 def __init__(      self     ,	__UpperCamelCase = True     ,	__UpperCamelCase = 8     ,	__UpperCamelCase = "lanczos"     ,	__UpperCamelCase = True     ,	__UpperCamelCase = False     ,	):




        """simple docstring"""


        super().__init__()

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       (images * 2_5_5).round().astype("""uint8"""     )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               UpperCamelCase_							       =       [Image.fromarray(image.squeeze()     ,	mode="""L"""     ) for image in images]
        else:
               UpperCamelCase_							       =       [Image.fromarray(__UpperCamelCase     ) for image in images]

        return pil_images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if not isinstance(__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       [images]
        UpperCamelCase_							       =       [np.array(__UpperCamelCase     ).astype(np.floataa     ) / 255.0 for image in images]
        UpperCamelCase_							       =       np.stack(__UpperCamelCase     ,	axis=0     )

        return images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[..., None]

        UpperCamelCase_							       =       torch.from_numpy(images.transpose(0     ,	3     ,	1     ,	2     )     )
        return images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       images.cpu().permute(0     ,	2     ,	3     ,	1     ).float().numpy()
        return images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return 2.0 * images - 1.0

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return (images / 2 + 0.5).clamp(0     ,	1     )

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       image.convert("""RGB"""     )
        return image

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        if height is None:
               UpperCamelCase_							       =       image.height
        if width is None:
               UpperCamelCase_							       =       image.width

        UpperCamelCase_       ,				UpperCamelCase_							       =       (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        UpperCamelCase_							       =       image.resize((width, height)     ,	resample=PIL_INTERPOLATION[self.config.resample]     )
        return image

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        UpperCamelCase_							       =       (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(__UpperCamelCase     ,	__UpperCamelCase     ):
               UpperCamelCase_							       =       [image]
        elif not (isinstance(__UpperCamelCase     ,	__UpperCamelCase     ) and all(isinstance(__UpperCamelCase     ,	__UpperCamelCase     ) for i in image     )):
               raise ValueError(
                   f'''Input is in incorrect format: {[type(__UpperCamelCase     ) for i in image]}. Currently, we only support {", ".join(__UpperCamelCase     )}'''     )

        if isinstance(image[0]     ,	PIL.Image.Image     ):
               if self.config.do_convert_rgb:
                      UpperCamelCase_							       =       [self.convert_to_rgb(__UpperCamelCase     ) for i in image]
               if self.config.do_resize:
                      UpperCamelCase_							       =       [self.resize(__UpperCamelCase     ,	__UpperCamelCase     ,	__UpperCamelCase     ) for i in image]
               UpperCamelCase_							       =       self.pil_to_numpy(__UpperCamelCase     )  # to np
               UpperCamelCase_							       =       self.numpy_to_pt(__UpperCamelCase     )  # to pt

        elif isinstance(image[0]     ,	np.ndarray     ):
               UpperCamelCase_							       =       np.concatenate(__UpperCamelCase     ,	axis=0     ) if image[0].ndim == 4 else np.stack(__UpperCamelCase     ,	axis=0     )
               UpperCamelCase_							       =       self.numpy_to_pt(__UpperCamelCase     )
               UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       image.shape
               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f'''Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'''
                          f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''     )

        elif isinstance(image[0]     ,	torch.Tensor     ):
               UpperCamelCase_							       =       torch.cat(__UpperCamelCase     ,	axis=0     ) if image[0].ndim == 4 else torch.stack(__UpperCamelCase     ,	axis=0     )
               UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_							       =       image.shape

               # don't need any preprocess if the image is latents
               if channel == 4:
                      return image

               if self.config.do_resize and (
                   height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
               ):
                      raise ValueError(
                          f'''Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'''
                          f'''currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'''     )

        # expected range [0,1], normalize to [-1,1]
        UpperCamelCase_							       =       self.config.do_normalize
        if image.min() < 0:
               warnings.warn(
                   """Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] """
                   f'''when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'''     ,	__UpperCamelCase     ,	)
               UpperCamelCase_							       =       False

        if do_normalize:
               UpperCamelCase_							       =       self.normalize(__UpperCamelCase     )

        return image

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = "pil"     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        if not isinstance(__UpperCamelCase     ,	torch.Tensor     ):
               raise ValueError(
                   f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase     )}. We only support pytorch tensor'''     )
        if output_type not in ["latent", "pt", "np", "pil"]:
               UpperCamelCase_							       =       (
                   f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                   """`pil`, `np`, `pt`, `latent`"""
               )
               deprecate("""Unsupported output_type"""     ,	"""1.0.0"""     ,	__UpperCamelCase     ,	standard_warn=__UpperCamelCase     )
               UpperCamelCase_							       =       """np"""

        if output_type == "latent":
               return image

        if do_denormalize is None:
               UpperCamelCase_							       =       [self.config.do_normalize] * image.shape[0]

        UpperCamelCase_							       =       torch.stack(
            [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

        if output_type == "pt":
               return image

        UpperCamelCase_							       =       self.pt_to_numpy(__UpperCamelCase     )

        if output_type == "np":
               return image

        if output_type == "pil":
               return self.numpy_to_pil(__UpperCamelCase     )



class 		lowercase_       (       __SCREAMING_SNAKE_CASE				):
 A__						:				List[Any]         =    CONFIG_NAME

 @register_to_config
 def __init__(      self     ,	__UpperCamelCase = True     ,	__UpperCamelCase = 8     ,	__UpperCamelCase = "lanczos"     ,	__UpperCamelCase = True     ,	):




        """simple docstring"""


        super().__init__()

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       (images * 2_5_5).round().astype("""uint8"""     )
        if images.shape[-1] == 1:
               # special case for grayscale (single channel) images
               UpperCamelCase_							       =       [Image.fromarray(image.squeeze()     ,	mode="""L"""     ) for image in images]
        else:
               UpperCamelCase_							       =       [Image.fromarray(image[:, :, :3]     ) for image in images]

        return pil_images

 @staticmethod
 def lowerCamelCase_				(      __UpperCamelCase     ):




        """simple docstring"""


        return image[:, :, 1] * 2**8 + image[:, :, 2]

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        if images.ndim == 3:
               UpperCamelCase_							       =       images[None, ...]
        UpperCamelCase_							       =       images[:, :, :, 3:]
        if images.shape[-1] == 6:
               UpperCamelCase_							       =       (images_depth * 2_5_5).round().astype("""uint8"""     )
               UpperCamelCase_							       =       [
                   Image.fromarray(self.rgblike_to_depthmap(__UpperCamelCase     )     ,	mode="""I;16"""     ) for image_depth in images_depth
               ]
        elif images.shape[-1] == 4:
               UpperCamelCase_							       =       (images_depth * 65_535.0).astype(np.uintaa     )
               UpperCamelCase_							       =       [Image.fromarray(__UpperCamelCase     ,	mode="""I;16"""     ) for image_depth in images_depth]
        else:
               raise Exception("""Not supported"""     )

        return pil_images

 def lowerCamelCase_				(      self     ,	__UpperCamelCase     ,	__UpperCamelCase = "pil"     ,	__UpperCamelCase = None     ,	):




        """simple docstring"""


        if not isinstance(__UpperCamelCase     ,	torch.Tensor     ):
               raise ValueError(
                   f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase     )}. We only support pytorch tensor'''     )
        if output_type not in ["latent", "pt", "np", "pil"]:
               UpperCamelCase_							       =       (
                   f'''the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '''
                   """`pil`, `np`, `pt`, `latent`"""
               )
               deprecate("""Unsupported output_type"""     ,	"""1.0.0"""     ,	__UpperCamelCase     ,	standard_warn=__UpperCamelCase     )
               UpperCamelCase_							       =       """np"""

        if do_denormalize is None:
               UpperCamelCase_							       =       [self.config.do_normalize] * image.shape[0]

        UpperCamelCase_							       =       torch.stack(
            [self.denormalize(image[i]     ) if do_denormalize[i] else image[i] for i in range(image.shape[0]     )]     )

        UpperCamelCase_							       =       self.pt_to_numpy(__UpperCamelCase     )

        if output_type == "np":
               if image.shape[-1] == 6:
                      UpperCamelCase_							       =       np.stack([self.rgblike_to_depthmap(im[:, :, 3:]     ) for im in image]     ,	axis=0     )
               else:
                      UpperCamelCase_							       =       image[:, :, :, 3:]
               return image[:, :, :, :3], image_depth

        if output_type == "pil":
               return self.numpy_to_pil(__UpperCamelCase     ), self.numpy_to_depth(__UpperCamelCase     )
        else:
               raise Exception(f'''This type {output_type} is not supported'''     )



