
'''simple docstring'''

import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


UpperCamelCase_            =    16
UpperCamelCase_            =    32


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Dict						)      ->  Dict:
  return int(x / 2**20						)


class 					a_					:




       def __enter__(       self					):
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         _lowerCAmelCase    :					Union[str, Any]							=   torch.cuda.memory_allocated()
         _lowerCAmelCase    :					List[str]							=   psutil.Process()

         _lowerCAmelCase    :					Any							=   self.cpu_mem_used()
         _lowerCAmelCase    :					Union[str, Any]							=   True
         _lowerCAmelCase    :					Any							=   threading.Thread(target=self.peak_monitor_func					)
         _lowerCAmelCase    :					Any							=   True
         peak_monitor_thread.start()
         return self




       def 							__UpperCamelCase    (       self					):
         return self.process.memory_info().rss




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   -1

         while True:
           _lowerCAmelCase    :					Dict							=   max(self.cpu_mem_used()				,				self.cpu_peak					)

           # can't sleep or will not catch the peak right (this comment is here on purpose)
           # time.sleep(0.001) # 1msec

           if not self.peak_monitoring:
             break







       def __exit__(       self				,				*snake_case_					):
         _lowerCAmelCase    :					Dict							=   False

         gc.collect()
         torch.cuda.empty_cache()
         _lowerCAmelCase    :					Optional[int]							=   torch.cuda.memory_allocated()
         _lowerCAmelCase    :					Union[str, Any]							=   torch.cuda.max_memory_allocated()
         _lowerCAmelCase    :					Optional[int]							=   bamb(self.end - self.begin					)
         _lowerCAmelCase    :					Optional[Any]							=   bamb(self.peak - self.begin					)

         _lowerCAmelCase    :					int							=   self.cpu_mem_used()
         _lowerCAmelCase    :					Dict							=   bamb(self.cpu_end - self.cpu_begin					)
         _lowerCAmelCase    :					Optional[Any]							=   bamb(self.cpu_peak - self.cpu_begin					)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

     UpperCamelCase_            =    mocked_dataloaders  # noqa: F811


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[Any]					, _lowerCamelCase  :    str						)      ->  List[Any]:
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					, _lowerCamelCase						) == "1":
    _lowerCAmelCase    :					Dict							=   2

  # New Code #
  # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  _lowerCAmelCase    :					Dict							=   FullyShardedDataParallelPlugin(
      state_dict_config=FullStateDictConfig(offload_to_cpu=_lowerCamelCase					, ranka_only=_lowerCamelCase						)					, optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_lowerCamelCase					, ranka_only=_lowerCamelCase						)					, )

  # Initialize accelerator
  if args.with_tracking:
    _lowerCAmelCase    :					List[str]							=   Accelerator(
        cpu=args.cpu					, mixed_precision=args.mixed_precision					, log_with="""wandb"""					, project_dir=args.logging_dir					, fsdp_plugin=_lowerCamelCase					, )
  else:
    _lowerCAmelCase    :					Optional[Any]							=   Accelerator(fsdp_plugin=_lowerCamelCase						)
  accelerator.print(accelerator.distributed_type						)

  if hasattr(args.checkpointing_steps					, """isdigit"""						):
    if args.checkpointing_steps == "epoch":
      _lowerCAmelCase    :					Optional[Any]							=   args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowerCAmelCase    :					List[Any]							=   int(args.checkpointing_steps						)
    else:
      raise ValueError(
          f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'						)
  else:
    _lowerCAmelCase    :					Optional[Any]							=   None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowerCAmelCase    :					Optional[int]							=   config["""lr"""]
  _lowerCAmelCase    :					Tuple							=   int(config["""num_epochs"""]						)
  _lowerCAmelCase    :					int							=   int(config["""seed"""]						)
  _lowerCAmelCase    :					Tuple							=   int(config["""batch_size"""]						)

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    _lowerCAmelCase    :					Tuple							=   vars(_lowerCamelCase						)
    accelerator.init_trackers("""fsdp_glue_no_trainer"""					, _lowerCamelCase						)

  _lowerCAmelCase    :					Dict							=   AutoTokenizer.from_pretrained(args.model_name_or_path						)
  _lowerCAmelCase    :					str							=   load_dataset("""glue"""					, """mrpc"""						)
  _lowerCAmelCase    :					List[Any]							=   evaluate.load("""glue"""					, """mrpc"""						)

  def tokenize_function(_lowerCamelCase  :    Optional[int]						):
    # max_length=None => use the model max length (it's actually the default)
    _lowerCAmelCase    :					Any							=   tokenizer(examples["""sentence1"""]					, examples["""sentence2"""]					, truncation=_lowerCamelCase					, max_length=_lowerCamelCase						)
    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():
    _lowerCAmelCase    :					List[Any]							=   datasets.map(
        _lowerCamelCase					, batched=_lowerCamelCase					, 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
  _lowerCAmelCase    :					Optional[Any]							=   tokenized_datasets.rename_column("""label"""					, """labels"""						)

  # If the batch size is too big we use gradient accumulation
  _lowerCAmelCase    :					str							=   1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    _lowerCAmelCase    :					List[Any]							=   batch_size // MAX_GPU_BATCH_SIZE
    _lowerCAmelCase    :					Tuple							=   MAX_GPU_BATCH_SIZE

  def collate_fn(_lowerCamelCase  :    int						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowerCAmelCase    :					str							=   1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowerCAmelCase    :					Any							=   16
    elif accelerator.mixed_precision != "no":
      _lowerCAmelCase    :					Optional[Any]							=   8
    else:
      _lowerCAmelCase    :					int							=   None

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

  # Instantiate dataloaders.
  _lowerCAmelCase    :					List[str]							=   DataLoader(
      tokenized_datasets["""train"""]					, shuffle=_lowerCamelCase					, collate_fn=_lowerCamelCase					, batch_size=_lowerCamelCase						)
  _lowerCAmelCase    :					str							=   DataLoader(
      tokenized_datasets["""validation"""]					, shuffle=_lowerCamelCase					, collate_fn=_lowerCamelCase					, batch_size=_lowerCamelCase						)

  set_seed(_lowerCamelCase						)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowerCAmelCase    :					Optional[int]							=   AutoModelForSequenceClassification.from_pretrained(
      args.model_name_or_path					, return_dict=_lowerCamelCase					, low_cpu_mem_usage=_lowerCamelCase						)

  # New Code #
  # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  _lowerCAmelCase    :					List[str]							=   accelerator.prepare(_lowerCamelCase						)
  accelerator.print(_lowerCamelCase						)

  # Instantiate optimizer
  # New Code #
  # For FSDP feature, at present it doesn't support multiple parameter groups,
  # so we need to create a single parameter group for the whole model
  _lowerCAmelCase    :					Dict							=   torch.optim.AdamW(params=model.parameters()					, lr=_lowerCamelCase					, weight_decay=2e-4						)

  # Instantiate scheduler
  _lowerCAmelCase    :					str							=   get_linear_schedule_with_warmup(
      optimizer=_lowerCamelCase					, num_warmup_steps=10					, num_training_steps=(len(_lowerCamelCase						) * num_epochs) // gradient_accumulation_steps					, )

  # New Code #
  # For FSDP feature, prepare everything except the model as we have already prepared the model
  # before creating the optimizer
  # 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.
  _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase    :					int							=   accelerator.prepare(
      _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase						)

  _lowerCAmelCase    :					List[str]							=   0

  # Potentially load in the weights and states from a previous save
  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						)
      _lowerCAmelCase    :					Tuple							=   os.path.basename(args.resume_from_checkpoint						)
    else:
      # Get the most recent checkpoint
      _lowerCAmelCase    :					Tuple							=   [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
      dirs.sort(key=os.path.getctime						)
      _lowerCAmelCase    :					Union[str, Any]							=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _lowerCAmelCase    :					List[Any]							=   os.path.splitext(_lowerCamelCase						)[0]

    if "epoch" in training_difference:
      num_epochs -= int(training_difference.replace("""epoch_"""					, """"""						)						)
      _lowerCAmelCase    :					Union[str, Any]							=   None
    else:
      _lowerCAmelCase    :					Any							=   int(training_difference.replace("""step_"""					, """"""						)						)
      num_epochs -= resume_step // len(_lowerCamelCase						)
      # If resuming by step, we also need to know exactly how far into the DataLoader we went
      _lowerCAmelCase    :					int							=   (num_epochs * len(_lowerCamelCase						)) - resume_step

    # Now we train the model
  for epoch in range(_lowerCamelCase						):
    # New Code #
    # context manager to track the peak memory usage during the training epoch
    with TorchTracemalloc() as tracemalloc:
      model.train()
      if args.with_tracking:
        _lowerCAmelCase    :					Tuple							=   0
      for step, batch in enumerate(_lowerCamelCase						):
        # We need to skip steps until we reach the resumed step
        if args.resume_from_checkpoint and epoch == 0:
          if resume_step is not None and step < resume_step:
            pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device						)
        _lowerCAmelCase    :					Optional[int]							=   model(**_lowerCamelCase						)
        _lowerCAmelCase    :					Optional[Any]							=   outputs.loss
        _lowerCAmelCase    :					Union[str, Any]							=   loss / gradient_accumulation_steps
        # We keep track of the loss at each epoch
        if args.with_tracking:
          total_loss += loss.detach().float()
        accelerator.backward(_lowerCamelCase						)
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
          # accelerator.print(lr_scheduler.get_lr())

        overall_step += 1

        if isinstance(_lowerCamelCase					, _lowerCamelCase						):
          _lowerCAmelCase    :					str							=   f'step_{overall_step}'
          if overall_step % checkpointing_steps == 0:
            if args.output_dir is not None:
              _lowerCAmelCase    :					Optional[Any]							=   os.path.join(args.output_dir					, _lowerCamelCase						)
            accelerator.save_state(_lowerCamelCase						)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin						)						)						)
    accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used						)						)
    accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked						)						)
    accelerator.print(
        """Total Peak Memory consumed during the train (max): {}""".format(
            tracemalloc.peaked + bamb(tracemalloc.begin						)						)						)
    # Logging the peak memory usage of the GPU to the tracker
    if args.with_tracking:
      accelerator.log(
          {
              """train_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin						),
          }					, step=_lowerCamelCase					, )

    # New Code #
    # context manager to track the peak memory usage during the evaluation
    with TorchTracemalloc() as tracemalloc:
      model.eval()
      for step, batch in enumerate(_lowerCamelCase						):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device						)
        with torch.no_grad():
          _lowerCAmelCase    :					str							=   model(**_lowerCamelCase						)
        _lowerCAmelCase    :					List[Any]							=   outputs.logits.argmax(dim=-1						)
        _lowerCAmelCase			,   _lowerCAmelCase    :					str							=   accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
        metric.add_batch(
            predictions=_lowerCamelCase					, references=_lowerCamelCase					, )

      _lowerCAmelCase    :					Optional[int]							=   metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'epoch {epoch}:'					, _lowerCamelCase						)
      if args.with_tracking:
        accelerator.log(
            {
                """accuracy""": eval_metric["""accuracy"""],
                """f1""": eval_metric["""f1"""],
                """train_loss""": total_loss.item() / len(_lowerCamelCase						),
            }					, step=_lowerCamelCase					, )

      if checkpointing_steps == "epoch":
        _lowerCAmelCase    :					Any							=   f'epoch_{epoch}'
        if args.output_dir is not None:
          _lowerCAmelCase    :					Tuple							=   os.path.join(args.output_dir					, _lowerCamelCase						)
        accelerator.save_state(_lowerCamelCase						)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print("""Memory before entering the eval : {}""".format(bamb(tracemalloc.begin						)						)						)
    accelerator.print("""Memory consumed at the end of the eval (end-begin): {}""".format(tracemalloc.used						)						)
    accelerator.print("""Peak Memory consumed during the eval (max-begin): {}""".format(tracemalloc.peaked						)						)
    accelerator.print(
        """Total Peak Memory consumed during the eval (max): {}""".format(tracemalloc.peaked + bamb(tracemalloc.begin						)						)						)
    # Logging the peak memory usage of the GPU to the tracker
    if args.with_tracking:
      accelerator.log(
          {
              """eval_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin						),
          }					, step=_lowerCamelCase					, )

  if args.with_tracking:
    accelerator.end_training()


def 		_UpperCAmelCase			(					)      ->  Optional[int]:
  _lowerCAmelCase    :					Any							=   argparse.ArgumentParser(description="""Simple example of training script."""						)
  parser.add_argument(
      """--mixed_precision"""					, type=_lowerCamelCase					, default=_lowerCamelCase					, 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=_lowerCamelCase					, default=_lowerCamelCase					, help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""					, )
  parser.add_argument(
      """--resume_from_checkpoint"""					, type=_lowerCamelCase					, default=_lowerCamelCase					, help="""If the training should continue from a checkpoint folder."""					, )
  parser.add_argument(
      """--with_tracking"""					, action="""store_true"""					, help="""Whether to load in all available experiment trackers from the environment and use them for logging."""					, )
  parser.add_argument(
      """--output_dir"""					, type=_lowerCamelCase					, default="""."""					, help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""					, )
  parser.add_argument(
      """--logging_dir"""					, type=_lowerCamelCase					, default="""logs"""					, help="""Location on where to store experiment tracking logs`"""					, )
  parser.add_argument(
      """--model_name_or_path"""					, type=_lowerCamelCase					, help="""Path to pretrained model or model identifier from huggingface.co/models."""					, required=_lowerCamelCase					, )
  _lowerCAmelCase    :					int							=   parser.parse_args()
  _lowerCAmelCase    :					Optional[Any]							=   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(_lowerCamelCase					, _lowerCamelCase						)


if __name__ == "__main__":
     main()




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ConvNextImageProcessor


class 					a_					(unittest.TestCase      ):




       def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=0.875				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
         _lowerCAmelCase    :					List[str]							=   size if size is not None else {"""shortest_edge""": 2_0}
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					List[str]							=   batch_size
         _lowerCAmelCase    :					List[str]							=   num_channels
         _lowerCAmelCase    :					Tuple							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   min_resolution
         _lowerCAmelCase    :					Tuple							=   max_resolution
         _lowerCAmelCase    :					List[Any]							=   do_resize
         _lowerCAmelCase    :					Dict							=   size
         _lowerCAmelCase    :					str							=   crop_pct
         _lowerCAmelCase    :					List[str]							=   do_normalize
         _lowerCAmelCase    :					Any							=   image_mean
         _lowerCAmelCase    :					Any							=   image_std







       def 							__UpperCamelCase    (       self					):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }



@require_torch
@require_vision
class 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = ConvNextImageProcessor if is_vision_available() else None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ConvNextImageProcessingTester(self					)




       @property
       def 							__UpperCamelCase    (       self					):
         return self.image_processor_tester.prepare_image_processor_dict()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(snake_case_				,				"""do_resize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""size"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""crop_pct"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""do_normalize"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_mean"""					)					)
         self.assertTrue(hasattr(snake_case_				,				"""image_std"""					)					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 2_0}					)

         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 4_2}					)




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         _lowerCAmelCase    :					str							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				Image.Image					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[int]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					int							=   self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         _lowerCAmelCase    :					Any							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				numpify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				np.ndarray					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					List[str]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)







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

         # Test not batched input
         _lowerCAmelCase    :					List[str]							=   image_processing(image_inputs[0]				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)

         # Test batched
         _lowerCAmelCase    :					Optional[int]							=   image_processing(snake_case_				,				return_tensors="""pt"""					).pixel_values
         self.assertEqual(
             encoded_images.shape				,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,				)


