

'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCamelCase :      Optional[int]					 =     logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")







@dataclass
class 							A__    :
  A__		 =				field(
      default='cifar10'     ,   metadata={'help': 'Name of a dataset from the datasets package'}  )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}  )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'The column name of the images in the files.'}  )
  A__		 =				field(default=A__     ,   metadata={'help': 'A folder containing the training data.'}  )
  A__		 =				field(default=A__     ,   metadata={'help': 'A folder containing the validation data.'}  )
  A__		 =				field(
      default=0.15     ,   metadata={'help': 'Percent to split off of train for validation.'}  )
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of training examples to this '
              'value if set.'
          )
      }     ,   )
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
              'value if set.'
          )
      }     ,   )





  def 						A  (		self      :	Union[str, Any] )	->    int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ={}
       if self.train_dir is not None:
            _SCREAMING_SNAKE_CASE							    =self.train_dir
       if self.validation_dir is not None:
            _SCREAMING_SNAKE_CASE							    =self.validation_dir
       _SCREAMING_SNAKE_CASE							    =data_files if data_files else None







@dataclass
class 							A__    :
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
          )
      }     ,   )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}  )
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'Override some existing default config settings when a model is trained from scratch. Example: '
              'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
          )
      }     ,   )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}  )
  A__		 =				field(
      default='main'     ,   metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,   )
  A__		 =				field(default=A__     ,   metadata={'help': 'Name or path of preprocessor config.'}  )
  A__		 =				field(
      default=A__     ,   metadata={
          'help': (
              'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
              'with private models).'
          )
      }     ,   )
  A__		 =				field(
      default=0.75     ,   metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}  )
  A__		 =				field(
      default=A__     ,   metadata={'help': 'Whether or not to train with normalized pixel values as target.'}  )







@dataclass
class 							A__    (       A__  ):
  A__		 =				field(
      default=1E-3     ,   metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}  )


def        _lowerCAmelCase					(						_UpperCamelCase						:     int				)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =torch.stack([example['pixel_values'] for example in examples]				)
     return {"pixel_values": pixel_values}


def        _lowerCAmelCase					(						)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)				)
     if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('run_mae'							, _UpperCamelCase							, _UpperCamelCase				)

     # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							, datefmt='%m/%d/%Y %H:%M:%S'							, handlers=[logging.StreamHandler(sys.stdout				)]							, )

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     _SCREAMING_SNAKE_CASE							    =training_args.get_process_log_level()
     logger.setLevel(_UpperCamelCase				)
     transformers.utils.logging.set_verbosity(_UpperCamelCase				)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # Log on each process the small summary:
     logger.warning(
         f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
         + f"distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"				)
     logger.info(f"Training/evaluation parameters {training_args}"				)

     # Detecting last checkpoint.
     _SCREAMING_SNAKE_CASE							    =None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          _SCREAMING_SNAKE_CASE							    =get_last_checkpoint(training_args.output_dir				)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
               raise ValueError(
                   f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                   'Use --overwrite_output_dir to overcome.'				)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
               logger.info(
                   f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)

    # Initialize our dataset.
     _SCREAMING_SNAKE_CASE							    =load_dataset(
         data_args.dataset_name							, data_args.dataset_config_name							, data_files=data_args.data_files							, cache_dir=model_args.cache_dir							, use_auth_token=True if model_args.use_auth_token else None							, )

     # If we don't have a validation split, split off a percentage of train as validation.
     _SCREAMING_SNAKE_CASE							    =None if 'validation' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split							, _UpperCamelCase				) and data_args.train_val_split > 0.0:
          _SCREAMING_SNAKE_CASE							    =ds['train'].train_test_split(data_args.train_val_split				)
          _SCREAMING_SNAKE_CASE							    =split['train']
          _SCREAMING_SNAKE_CASE							    =split['test']

     # Load pretrained model and image processor
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     _SCREAMING_SNAKE_CASE							    ={
         'cache_dir': model_args.cache_dir,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          _SCREAMING_SNAKE_CASE							    =ViTMAEConfig.from_pretrained(model_args.config_name							, **_UpperCamelCase				)
     elif model_args.model_name_or_path:
          _SCREAMING_SNAKE_CASE							    =ViTMAEConfig.from_pretrained(model_args.model_name_or_path							, **_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =ViTMAEConfig()
          logger.warning('You are instantiating a new config instance from scratch.'				)
          if model_args.config_overrides is not None:
               logger.info(f"Overriding config: {model_args.config_overrides}"				)
               config.update_from_string(model_args.config_overrides				)
               logger.info(f"New config: {config}"				)

    # adapt config
     config.update(
         {
             'mask_ratio': model_args.mask_ratio,
             'norm_pix_loss': model_args.norm_pix_loss,
         }				)

     # create image processor
     if model_args.image_processor_name:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor.from_pretrained(model_args.image_processor_name							, **_UpperCamelCase				)
     elif model_args.model_name_or_path:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor.from_pretrained(model_args.model_name_or_path							, **_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          _SCREAMING_SNAKE_CASE							    =ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path							, from_tf=bool('.ckpt' in model_args.model_name_or_path				)							, config=_UpperCamelCase							, cache_dir=model_args.cache_dir							, revision=model_args.model_revision							, use_auth_token=True if model_args.use_auth_token else None							, )
     else:
          logger.info('Training new model from scratch'				)
          _SCREAMING_SNAKE_CASE							    =ViTMAEForPreTraining(_UpperCamelCase				)

     if training_args.do_train:
          _SCREAMING_SNAKE_CASE							    =ds['train'].column_names
     else:
          _SCREAMING_SNAKE_CASE							    =ds['validation'].column_names

     if data_args.image_column_name is not None:
          _SCREAMING_SNAKE_CASE							    =data_args.image_column_name
     elif "image" in column_names:
          _SCREAMING_SNAKE_CASE							    ='image'
     elif "img" in column_names:
          _SCREAMING_SNAKE_CASE							    ='img'
     else:
          _SCREAMING_SNAKE_CASE							    =column_names[0]

     # transformations as done in original MAE paper
     # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
     if "shortest_edge" in image_processor.size:
          _SCREAMING_SNAKE_CASE							    =image_processor.size['shortest_edge']
     else:
          _SCREAMING_SNAKE_CASE							    =(image_processor.size['height'], image_processor.size['width'])
     _SCREAMING_SNAKE_CASE							    =Compose(
         [
             Lambda(lambda _UpperCamelCase				: img.convert('RGB'				) if img.mode != "RGB" else img				),
             RandomResizedCrop(_UpperCamelCase							, scale=(0.2, 1.0)							, interpolation=InterpolationMode.BICUBIC				),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean							, std=image_processor.image_std				),
         ]				)

     def preprocess_images(_UpperCamelCase						:     Dict				):
          _SCREAMING_SNAKE_CASE							    =[transforms(_UpperCamelCase				) for image in examples[image_column_name]]
          return examples

     if training_args.do_train:
          if "train" not in ds:
               raise ValueError('--do_train requires a train dataset'				)
          if data_args.max_train_samples is not None:
               _SCREAMING_SNAKE_CASE							    =ds['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
          # Set the training transforms
          ds["train"].set_transform(_UpperCamelCase				)

     if training_args.do_eval:
          if "validation" not in ds:
               raise ValueError('--do_eval requires a validation dataset'				)
          if data_args.max_eval_samples is not None:
               _SCREAMING_SNAKE_CASE							    =(
                   ds['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_UpperCamelCase				)

     # Compute absolute learning rate
     _SCREAMING_SNAKE_CASE							    =(
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          _SCREAMING_SNAKE_CASE							    =training_args.base_learning_rate * total_train_batch_size / 2_56

     # Initialize our trainer
     _SCREAMING_SNAKE_CASE							    =Trainer(
         model=_UpperCamelCase							, args=_UpperCamelCase							, train_dataset=ds['train'] if training_args.do_train else None							, eval_dataset=ds['validation'] if training_args.do_eval else None							, tokenizer=_UpperCamelCase							, data_collator=_UpperCamelCase							, )

     # Training
     if training_args.do_train:
          _SCREAMING_SNAKE_CASE							    =None
          if training_args.resume_from_checkpoint is not None:
               _SCREAMING_SNAKE_CASE							    =training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _SCREAMING_SNAKE_CASE							    =last_checkpoint
          _SCREAMING_SNAKE_CASE							    =trainer.train(resume_from_checkpoint=_UpperCamelCase				)
          trainer.save_model()
          trainer.log_metrics('train'							, train_result.metrics				)
          trainer.save_metrics('train'							, train_result.metrics				)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          _SCREAMING_SNAKE_CASE							    =trainer.evaluate()
          trainer.log_metrics('eval'							, _UpperCamelCase				)
          trainer.save_metrics('eval'							, _UpperCamelCase				)

     # Write model card and (optionally) push to hub
     _SCREAMING_SNAKE_CASE							    ={
         'tasks': 'masked-auto-encoding',
         'dataset': data_args.dataset_name,
         'tags': ['masked-auto-encoding'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_UpperCamelCase				)
     else:
          trainer.create_model_card(**_UpperCamelCase				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]				)      ->			Optional[int]:
     """simple docstring"""




     main()


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      :    Optional[List[str]]		=					None
A      :    str		=					"<" 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      :    str		=					[
    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 			_lowercase :


       """simple docstring"""




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





       def __call__(   self							:			Any       ):


             '''simple docstring'''



             return self.pa_type





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]       ):


             '''simple docstring'''



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

             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				str    =  np.array(__lowerCamelCase       )

             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   return {"path": value, "bytes": None}
             elif isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   return {"path": None, "bytes": value}
             elif isinstance(__lowerCamelCase					,      np.ndarray       ):
                   # convert the image array to PNG/TIFF bytes
                   return encode_np_array(__lowerCamelCase       )
             elif isinstance(__lowerCamelCase					,      PIL.Image.Image       ):
                   # convert the PIL image to bytes (default format is PNG/TIFF)
                   return encode_pil_image(__lowerCamelCase       )
             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 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			dict					,      __lowerCamelCase							:			List[Any]=None       ):


             '''simple docstring'''



             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:
                   lowerCamelCase__		:				Union[str, Any]    =  {}

             lowerCamelCase__    ,   lowerCamelCase__		:				Optional[int]    =  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(__lowerCamelCase       ):
                               lowerCamelCase__		:				Union[str, Any]    =  PIL.Image.open(__lowerCamelCase       )
                         else:
                               lowerCamelCase__		:				Tuple    =  path.split("::"       )[-1]
                               try:
                                     lowerCamelCase__		:				str    =  string_to_dict(__lowerCamelCase					,      config.HUB_DATASETS_URL       )["repo_id"]
                                     lowerCamelCase__		:				Any    =  token_per_repo_id.get(__lowerCamelCase       )
                               except ValueError:
                                     lowerCamelCase__		:				int    =  None
                               with xopen(__lowerCamelCase					,      "rb"					,      use_auth_token=__lowerCamelCase       ) as f:
                                     lowerCamelCase__		:				List[str]    =  BytesIO(f.read()       )
                               lowerCamelCase__		:				Optional[int]    =  PIL.Image.open(bytes_       )
             else:
                   lowerCamelCase__		:				Dict    =  PIL.Image.open(BytesIO(bytes_       )       )
             image.load()  # to avoid "Too many open files" errors
             return image





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             from .features import Value

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





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			Union[pa.StringArray, pa.StructArray, pa.ListArray]       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			pa.StructArray       ):


             '''simple docstring'''



             @no_op_if_value_is_null
             def path_to_bytes(__lowerCamelCase							:			Union[str, Any]       ):
                   with xopen(__lowerCamelCase					,      "rb"       ) as f:
                         lowerCamelCase__		:				str    =  f.read()
                   return bytes_

             lowerCamelCase__		:				List[Any]    =  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()					,      )
             lowerCamelCase__		:				Optional[int]    =  pa.array(
                 [os.path.basename(__lowerCamelCase       ) if path is not None else None for path in storage.field("path"       ).to_pylist()]					,      type=pa.string()					,      )
             lowerCamelCase__		:				Tuple    =  pa.StructArray.from_arrays([bytes_array, path_array]					,      ["bytes", "path"]					,      mask=bytes_array.is_null()       )
             return array_cast(__lowerCamelCase					,      self.pa_type       )



def       lowercase_		(		):

      """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()
            lowerCamelCase__		:				List[str]    =  list(set(PIL.Image.OPEN.keys()						) & set(PIL.Image.SAVE.keys()						)						)
      return _IMAGE_COMPRESSION_FORMATS

def       lowercase_		(		_A			:    "PIL.Image.Image"						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  BytesIO()
      if image.format in list_image_compression_formats():
            lowerCamelCase__		:				int    =  image.format
      else:
            lowerCamelCase__		:				int    =  "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
      image.save(_A    ,					format=_A						)
      return buffer.getvalue()

def       lowercase_		(		_A			:    "PIL.Image.Image"						):

      """simple docstring"""




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

def       lowercase_		(		_A			:    np.ndarray						):

      """simple docstring"""




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

      lowerCamelCase__		:				int    =  array.dtype
      lowerCamelCase__		:				List[str]    =  dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
      lowerCamelCase__		:				List[str]    =  dtype.kind
      lowerCamelCase__		:				Optional[Any]    =  dtype.itemsize

      lowerCamelCase__		:				Dict    =  None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            lowerCamelCase__		:				List[Any]    =  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:
            lowerCamelCase__		:				Any    =  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:
                  lowerCamelCase__		:				Optional[Any]    =  dtype_byteorder + dtype_kind + str(_A						)
                  lowerCamelCase__		:				int    =  np.dtype(_A						)
                  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}"						)
      lowerCamelCase__		:				List[Any]    =  PIL.Image.fromarray(array.astype(_A						)						)
      return {"path": None, "bytes": image_to_bytes(_A						)}


def       lowercase_		(		_A			:    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]						):

      """simple docstring"""




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

      if objs:
            lowerCamelCase__    ,   lowerCamelCase__		:				int    =  first_non_null_value(_A						)
            if isinstance(_A    ,					_A						):
                  return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
            if isinstance(_A    ,					np.ndarray						):
                  lowerCamelCase__		:				Optional[Any]    =  no_op_if_value_is_null(_A						)
                  return [obj_to_image_dict_func(_A						) for obj in objs]
            elif isinstance(_A    ,					PIL.Image.Image						):
                  lowerCamelCase__		:				int    =  no_op_if_value_is_null(_A						)
                  return [obj_to_image_dict_func(_A						) for obj in objs]
            else:
                  return objs
      else:
            return objs


