




import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      fname.split(os.path.sep				)[-1]
     return re.search(r'''^(.*)_\d+\.jpg$'''			,						_lowercase				).groups()[0]

class   __a(					_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=None  )			->		List[Any]:
         UpperCAmelCase_      :							Optional[int]       =      file_names
         UpperCAmelCase_      :							List[Any]       =      image_transform
         UpperCAmelCase_      :							Dict       =      label_to_id





    def __len__(				self  )			->		int:
         return len(self.file_names  )





    def __getitem__(				self       ,_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      self.file_names[idx]
         UpperCAmelCase_      :							int       =      PIL.Image.open(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      raw_image.convert('''RGB'''  )
         if self.image_transform is not None:
              UpperCAmelCase_      :							Union[str, Any]       =      self.image_transform(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      extract_label(_SCREAMING_SNAKE_CASE  )
         if self.label_to_id is not None:
              UpperCAmelCase_      :							List[Any]       =      self.label_to_id[label]
         return {"image": image, "label": label}







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     if args.with_tracking:
          UpperCAmelCase_      :							List[Any]       =      Accelerator(
              cpu=args.cpu			,						mixed_precision=args.mixed_precision			,						log_with='''all'''			,						project_dir=args.project_dir				)
     else:
          UpperCAmelCase_      :							Any       =      Accelerator(cpu=args.cpu			,						mixed_precision=args.mixed_precision				)

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase_      :							Any       =      config['''lr''']
     UpperCAmelCase_      :							Dict       =      int(config['''num_epochs''']				)
     UpperCAmelCase_      :							Optional[Any]       =      int(config['''seed''']				)
     UpperCAmelCase_      :							Optional[int]       =      int(config['''batch_size''']				)
     UpperCAmelCase_      :							Optional[Any]       =      config['''image_size''']
     if not isinstance(_lowercase			,						(list, tuple)				):
          UpperCAmelCase_      :							List[str]       =      (image_size, image_size)

     # 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_      :							Tuple       =      args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               UpperCAmelCase_      :							Optional[int]       =      int(args.checkpointing_steps				)
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''				)
     else:
          UpperCAmelCase_      :							Optional[Any]       =      None

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          UpperCAmelCase_      :							int       =      os.path.split(_lowercase				)[-1].split('''.'''				)[0]
          accelerator.init_trackers(_lowercase			,						_lowercase				)

     # Grab all the image filenames
     UpperCAmelCase_      :							str       =      [os.path.join(args.data_dir			,						_lowercase				) for fname in os.listdir(args.data_dir				) if fname.endswith('''.jpg'''				)]

     # Build the label correspondences
     UpperCAmelCase_      :							Optional[int]       =      [extract_label(_lowercase				) for fname in file_names]
     UpperCAmelCase_      :							List[Any]       =      list(set(_lowercase				)				)
     id_to_label.sort()
     UpperCAmelCase_      :							Any       =      {lbl: i for i, lbl in enumerate(_lowercase				)}

     # Set the seed before splitting the data.
     np.random.seed(_lowercase				)
     torch.manual_seed(_lowercase				)
     torch.cuda.manual_seed_all(_lowercase				)

     # Split our filenames between train and validation
     UpperCAmelCase_      :							List[Any]       =      np.random.permutation(len(_lowercase				)				)
     UpperCAmelCase_      :							str       =      int(0.8 * len(_lowercase				)				)
     UpperCAmelCase_      :							Tuple       =      random_perm[:cut]
     UpperCAmelCase_      :							Any       =      random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     UpperCAmelCase_      :							Tuple       =      Compose([RandomResizedCrop(_lowercase			,						scale=(0.5, 1.0)				), ToTensor()]				)
     UpperCAmelCase_      :							List[Any]       =      PetsDataset(
         [file_names[i] for i in train_split]			,						image_transform=_lowercase			,						label_to_id=_lowercase				)

     # For evaluation, we use a deterministic Resize
     UpperCAmelCase_      :							Any       =      Compose([Resize(_lowercase				), ToTensor()]				)
     UpperCAmelCase_      :							Dict       =      PetsDataset([file_names[i] for i in eval_split]			,						image_transform=_lowercase			,						label_to_id=_lowercase				)

     # Instantiate dataloaders.
     UpperCAmelCase_      :							str       =      DataLoader(_lowercase			,						shuffle=_lowercase			,						batch_size=_lowercase			,						num_workers=4				)
     UpperCAmelCase_      :							Tuple       =      DataLoader(_lowercase			,						shuffle=_lowercase			,						batch_size=_lowercase			,						num_workers=4				)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase_      :							Dict       =      create_model('''resnet50d'''			,						pretrained=_lowercase			,						num_classes=len(_lowercase				)				)

     # 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_      :							str       =      model.to(accelerator.device				)

     # Freezing the base model
     for param in model.parameters():
          UpperCAmelCase_      :							List[str]       =      False
     for param in model.get_classifier().parameters():
          UpperCAmelCase_      :							Any       =      True

     # We normalize the batches of images to be a bit faster.
     UpperCAmelCase_      :							Optional[int]       =      torch.tensor(model.default_cfg['''mean''']				)[None, :, None, None].to(accelerator.device				)
     UpperCAmelCase_      :							Dict       =      torch.tensor(model.default_cfg['''std''']				)[None, :, None, None].to(accelerator.device				)

     # Instantiate optimizer
     UpperCAmelCase_      :							Dict       =      torch.optim.Adam(params=model.parameters()			,						lr=lr / 25				)

     # Instantiate learning rate scheduler
     UpperCAmelCase_      :							Union[str, Any]       =      OneCycleLR(optimizer=_lowercase			,						max_lr=_lowercase			,						epochs=_lowercase			,						steps_per_epoch=len(_lowercase				)				)

     # 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_      :							List[str]       =      accelerator.prepare(
         _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				)
     # We need to keep track of how many total steps we have iterated over
     UpperCAmelCase_      :							Union[str, Any]       =      0
     # We also need to keep track of the starting epoch so files are named properly
     UpperCAmelCase_      :							Union[str, Any]       =      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				)
               UpperCAmelCase_      :							Optional[Any]       =      os.path.basename(args.resume_from_checkpoint				)
          else:
               # Get the most recent checkpoint
               UpperCAmelCase_      :							List[str]       =      [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
               dirs.sort(key=os.path.getctime				)
               UpperCAmelCase_      :							str       =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          UpperCAmelCase_      :							Optional[int]       =      os.path.splitext(_lowercase				)[0]

          if "epoch" in training_difference:
               UpperCAmelCase_      :							List[Any]       =      int(training_difference.replace('''epoch_'''			,						''''''				)				) + 1
               UpperCAmelCase_      :							Any       =      None
          else:
               UpperCAmelCase_      :							Dict       =      int(training_difference.replace('''step_'''			,						''''''				)				)
               UpperCAmelCase_      :							str       =      resume_step // len(_lowercase				)
               resume_step -= starting_epoch * len(_lowercase				)

    # Now we train the model
     for epoch in range(_lowercase			,						_lowercase				):
          model.train()
          if args.with_tracking:
               UpperCAmelCase_      :							Optional[int]       =      0
          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_      :							List[str]       =      accelerator.skip_first_batches(_lowercase			,						_lowercase				)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               UpperCAmelCase_      :							Optional[int]       =      train_dataloader
          for batch in active_dataloader:
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               UpperCAmelCase_      :							int       =      {k: v.to(accelerator.device				) for k, v in batch.items()}
               UpperCAmelCase_      :							int       =      (batch['''image'''] - mean) / std
               UpperCAmelCase_      :							Dict       =      model(_lowercase				)
               UpperCAmelCase_      :							Union[str, Any]       =      torch.nn.functional.cross_entropy(_lowercase			,						batch['''label''']				)
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(_lowercase				)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               overall_step += 1
               if isinstance(_lowercase			,						_lowercase				):
                    UpperCAmelCase_      :							Tuple       =      f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              UpperCAmelCase_      :							List[Any]       =      os.path.join(args.output_dir			,						_lowercase				)
                         accelerator.save_state(_lowercase				)
          model.eval()
          UpperCAmelCase_      :							str       =      0
          UpperCAmelCase_      :							Dict       =      0
          for step, batch in enumerate(_lowercase				):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               UpperCAmelCase_      :							Optional[Any]       =      {k: v.to(accelerator.device				) for k, v in batch.items()}
               UpperCAmelCase_      :							str       =      (batch['''image'''] - mean) / std
               with torch.no_grad():
                    UpperCAmelCase_      :							Tuple       =      model(_lowercase				)
               UpperCAmelCase_      :							Union[str, Any]       =      outputs.argmax(dim=-1				)
               UpperCAmelCase_,   UpperCAmelCase_      :							Optional[int]       =      accelerator.gather_for_metrics((predictions, batch['''label'''])				)
               UpperCAmelCase_      :							Optional[Any]       =      predictions == references
               num_elems += accurate_preds.shape[0]
               accurate += accurate_preds.long().sum()

          UpperCAmelCase_      :							Dict       =      accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''				)
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': 100 * eval_metric,
                       '''train_loss''': total_loss.item() / len(_lowercase				),
                       '''epoch''': epoch,
                   }			,						step=_lowercase			,						)
          if checkpointing_steps == "epoch":
               UpperCAmelCase_      :							List[Any]       =      f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    UpperCAmelCase_      :							List[str]       =      os.path.join(args.output_dir			,						_lowercase				)
               accelerator.save_state(_lowercase				)

     if args.with_tracking:
          accelerator.end_training()







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Any       =      argparse.ArgumentParser(description='''Simple example of training script.'''				)
     parser.add_argument('''--data_dir'''			,						required=_lowercase			,						help='''The data folder on disk.'''				)
     parser.add_argument('''--fp16'''			,						action='''store_true'''			,						help='''If passed, will use FP16 training.'''				)
     parser.add_argument(
         '''--mixed_precision'''			,						type=_lowercase			,						default=_lowercase			,						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=_lowercase			,						default=_lowercase			,						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=_lowercase			,						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=_lowercase			,						default=_lowercase			,						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(
         '''--project_dir'''			,						type=_lowercase			,						default='''logs'''			,						help='''Location on where to store experiment tracking logs` and relevent project information'''			,						)
     UpperCAmelCase_      :							Tuple       =      parser.parse_args()
     UpperCAmelCase_      :							Any       =      {'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
     training_function(_lowercase			,						_lowercase				)


if __name__ == "__main__":
     main()





import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


__a          =			None
__a          =			'<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__a          =			[
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				True
    lowerCAmelCase        =				None
    # Automatically constructed
    lowerCAmelCase        =				"PIL.Image.Image"
    lowerCAmelCase        =				pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}       )
    lowerCAmelCase        =				field(default='''Image'''    ,						init=_a    ,						repr=_a       )





    def __call__(				self  )			->		Optional[Any]:
         return self.pa_type





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		dict:
         if config.PIL_AVAILABLE:
              import PIL.Image
         else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''  )

         if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							Optional[int]       =      np.array(_SCREAMING_SNAKE_CASE  )

         if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              return {"path": value, "bytes": None}
         elif isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              return {"path": None, "bytes": value}
         elif isinstance(_SCREAMING_SNAKE_CASE       ,np.ndarray  ):
              # convert the image array to PNG/TIFF bytes
              return encode_np_array(_SCREAMING_SNAKE_CASE  )
         elif isinstance(_SCREAMING_SNAKE_CASE       ,PIL.Image.Image  ):
              # convert the PIL image to bytes (default format is PNG/TIFF)
              return encode_pil_image(_SCREAMING_SNAKE_CASE  )
         elif value.get('''path'''  ) is not None and os.path.isfile(value['''path''']  ):
              # we set "bytes": None to not duplicate the data if they're already available locally
              return {"bytes": None, "path": value.get('''path'''  )}
         elif value.get('''bytes'''  ) is not None or value.get('''path'''  ) is not None:
              # store the image bytes, and path is used to infer the image format using the file extension
              return {"bytes": value.get('''bytes'''  ), "path": value.get('''path'''  )}
         else:
              raise ValueError(
                  f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None  )			->		"PIL.Image.Image":
         if not self.decode:
              raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''  )

         if config.PIL_AVAILABLE:
              import PIL.Image
         else:
              raise ImportError('''To support decoding images, please install \'Pillow\'.'''  )

         if token_per_repo_id is None:
              UpperCAmelCase_      :							str       =      {}

         UpperCAmelCase_,   UpperCAmelCase_      :							List[str]       =      value['''path'''], value['''bytes''']
         if bytes_ is None:
              if path is None:
                   raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''  )
              else:
                   if is_local_path(_SCREAMING_SNAKE_CASE  ):
                        UpperCAmelCase_      :							int       =      PIL.Image.open(_SCREAMING_SNAKE_CASE  )
                   else:
                        UpperCAmelCase_      :							int       =      path.split('''::'''  )[-1]
                        try:
                             UpperCAmelCase_      :							str       =      string_to_dict(_SCREAMING_SNAKE_CASE       ,config.HUB_DATASETS_URL  )['''repo_id''']
                             UpperCAmelCase_      :							Optional[Any]       =      token_per_repo_id.get(_SCREAMING_SNAKE_CASE  )
                        except ValueError:
                             UpperCAmelCase_      :							Any       =      None
                        with xopen(_SCREAMING_SNAKE_CASE       ,'''rb'''       ,use_auth_token=_SCREAMING_SNAKE_CASE  ) as f:
                             UpperCAmelCase_      :							List[str]       =      BytesIO(f.read()  )
                        UpperCAmelCase_      :							Optional[int]       =      PIL.Image.open(bytes_  )
         else:
              UpperCAmelCase_      :							Dict       =      PIL.Image.open(BytesIO(bytes_  )  )
         image.load()  # to avoid "Too many open files" errors
         return image





    def 							a__				(				self  )			->		Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value('''binary'''  ),
                 "path": Value('''string'''  ),
             }
         )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		pa.StructArray:
         if pa.types.is_string(storage.type  ):
              UpperCAmelCase_      :							Union[str, Any]       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.binary()  )
              UpperCAmelCase_      :							int       =      pa.StructArray.from_arrays([bytes_array, storage]       ,['''bytes''', '''path''']       ,mask=storage.is_null()  )
         elif pa.types.is_binary(storage.type  ):
              UpperCAmelCase_      :							Tuple       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.string()  )
              UpperCAmelCase_      :							int       =      pa.StructArray.from_arrays([storage, path_array]       ,['''bytes''', '''path''']       ,mask=storage.is_null()  )
         elif pa.types.is_struct(storage.type  ):
              if storage.type.get_field_index('''bytes'''  ) >= 0:
                   UpperCAmelCase_      :							str       =      storage.field('''bytes'''  )
              else:
                   UpperCAmelCase_      :							Union[str, Any]       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.binary()  )
              if storage.type.get_field_index('''path'''  ) >= 0:
                   UpperCAmelCase_      :							List[str]       =      storage.field('''path'''  )
              else:
                   UpperCAmelCase_      :							List[Any]       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.string()  )
              UpperCAmelCase_      :							Any       =      pa.StructArray.from_arrays([bytes_array, path_array]       ,['''bytes''', '''path''']       ,mask=storage.is_null()  )
         elif pa.types.is_list(storage.type  ):
              UpperCAmelCase_      :							Optional[Any]       =      pa.array(
                  [encode_np_array(np.array(_SCREAMING_SNAKE_CASE  )  )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]       ,type=pa.binary()       ,)
              UpperCAmelCase_      :							Any       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.string()  )
              UpperCAmelCase_      :							Any       =      pa.StructArray.from_arrays(
                  [bytes_array, path_array]       ,['''bytes''', '''path''']       ,mask=bytes_array.is_null()  )
         return array_cast(_SCREAMING_SNAKE_CASE       ,self.pa_type  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(_SCREAMING_SNAKE_CASE  ):
              with xopen(_SCREAMING_SNAKE_CASE       ,'''rb'''  ) as f:
                   UpperCAmelCase_      :							List[Any]       =      f.read()
              return bytes_

         UpperCAmelCase_      :							Dict       =      pa.array(
             [
                 (path_to_bytes(x['''path''']  ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                 for x in storage.to_pylist()
             ]       ,type=pa.binary()       ,)
         UpperCAmelCase_      :							Union[str, Any]       =      pa.array(
             [os.path.basename(_SCREAMING_SNAKE_CASE  ) if path is not None else None for path in storage.field('''path'''  ).to_pylist()]       ,type=pa.string()       ,)
         UpperCAmelCase_      :							Tuple       =      pa.StructArray.from_arrays([bytes_array, path_array]       ,['''bytes''', '''path''']       ,mask=bytes_array.is_null()  )
         return array_cast(_SCREAMING_SNAKE_CASE       ,self.pa_type  )







def lowerCamelCase__       (      ):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.'''				)

     global _IMAGE_COMPRESSION_FORMATS
     if _IMAGE_COMPRESSION_FORMATS is None:
          PIL.Image.init()
          UpperCAmelCase_      :							Optional[Any]       =      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
     return _IMAGE_COMPRESSION_FORMATS







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      BytesIO()
     if image.format in list_image_compression_formats():
          UpperCAmelCase_      :							List[str]       =      image.format
     else:
          UpperCAmelCase_      :							int       =      '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
     image.save(_lowercase			,						format=_lowercase				)
     return buffer.getvalue()







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if hasattr(_lowercase			,						'''filename'''				) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(_lowercase				)}







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.'''				)

     UpperCAmelCase_      :							int       =      array.dtype
     UpperCAmelCase_      :							Optional[Any]       =      dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
     UpperCAmelCase_      :							str       =      dtype.kind
     UpperCAmelCase_      :							int       =      dtype.itemsize

     UpperCAmelCase_      :							Optional[int]       =      None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          UpperCAmelCase_      :							str       =      np.dtype('''|u1'''				)
          if dtype_kind not in ["u", "i"]:
               raise TypeError(
                   f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''				)
          if dtype is not dest_dtype:
               warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''				)
    # Exact match
     elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
          UpperCAmelCase_      :							Dict       =      dtype
     else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
          while dtype_itemsize >= 1:
               UpperCAmelCase_      :							Optional[int]       =      dtype_byteorder + dtype_kind + str(_lowercase				)
               UpperCAmelCase_      :							Dict       =      np.dtype(_lowercase				)
               if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                    warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''				)
                    break
               else:
                    dtype_itemsize //= 2

     if dest_dtype is None:
          raise TypeError(
              f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''				)
     UpperCAmelCase_      :							Dict       =      PIL.Image.fromarray(array.astype(_lowercase				)				)
     return {"path": None, "bytes": image_to_bytes(_lowercase				)}







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.'''				)

     if objs:
          UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      first_non_null_value(_lowercase				)
          if isinstance(_lowercase			,						_lowercase				):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(_lowercase			,						np.ndarray				):
               UpperCAmelCase_      :							List[str]       =      no_op_if_value_is_null(_lowercase				)
               return [obj_to_image_dict_func(_lowercase				) for obj in objs]
          elif isinstance(_lowercase			,						PIL.Image.Image				):
               UpperCAmelCase_      :							Union[str, Any]       =      no_op_if_value_is_null(_lowercase				)
               return [obj_to_image_dict_func(_lowercase				) for obj in objs]
          else:
               return objs
     else:
          return objs