
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_lowerCamelCase					   =logging.getLogger(__name__)
_lowerCamelCase					   =list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_lowerCamelCase					   =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class 							A__		:
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
              )
          }  ,   )
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(__SCREAMING_SNAKE_CASE)}  ,   )
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   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"""
              )
          }  ,   )
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}  ,   )
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}  ,   )
      _UpperCAmelCase				:    str           =			field(
          default="""main"""  ,   metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}  ,   )
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                  """with private models)."""
              )
          }  ,   )
      def 						UpperCamelCase__							(      self      ):
       if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
        raise ValueError(
            """--config_overrides can't be used in combination with --config_name or --model_name_or_path"""      )





@dataclass
class 							A__		:
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
      _UpperCAmelCase				:    Optional[str]           =			field(default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """The input training data file (a text file)."""})
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}  ,   )
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}  ,   )
      _UpperCAmelCase				:    Optional[str]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}  ,   )
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
      _UpperCAmelCase				:    Optional[int]           =			field(
          default=5  ,   metadata={
              """help""": """The percentage of the train set used as validation set in case there's no validation split"""
          }  ,   )
      _UpperCAmelCase				:    Optional[int]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated. Default to the max input length of the model."""
              )
          }  ,   )
      _UpperCAmelCase				:    Optional[int]           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={"""help""": """The number of processes to use for the preprocessing."""}  ,   )
      _UpperCAmelCase				:    float           =			field(
          default=0.15  ,   metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
      _UpperCAmelCase				:    bool           =			field(
          default=__SCREAMING_SNAKE_CASE  ,   metadata={
              """help""": (
                  """Whether to pad all samples to `max_seq_length`. """
                  """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
              )
          }  ,   )
      def 						UpperCamelCase__							(      self      ):
       if self.train_file is not None:
        lowerCamelCase		:	Any							=      self.train_file.split("""."""      )[-1]
        assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
       if self.validation_file is not None:
        lowerCamelCase		:	Union[str, Any]							=      self.validation_file.split("""."""      )[-1]
        assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 with open(lowerCamelCase,	"""r""",	encoding="""utf-8"""							) as f:
  lowerCamelCase		:	Optional[int]							=      [json.loads(lowerCamelCase							) for line in f.read().splitlines() if (len(lowerCamelCase							) > 0 and not line.isspace())]
 assert len(lowerCamelCase							) == len(lowerCamelCase							)

 lowerCamelCase		:	Any							=      {c: dataset[c] for c in dataset.column_names}
 lowerCamelCase		:	Dict							=      refs
 return Dataset.from_dict(lowerCamelCase							)





def     _a					(						):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowerCamelCase		:	str							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
 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.
  lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
 else:
  lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[str]							=      parser.parse_args_into_dataclasses()

 # Detecting last checkpoint.
 lowerCamelCase		:	Optional[Any]							=      None
 if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
  lowerCamelCase		:	Dict							=      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:
   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."""							)

    # 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							)],	)
 logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

 # 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}'''							)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank							):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info("""Training/evaluation parameters %s""",	lowerCamelCase							)

 # Set seed before initializing model.
 set_seed(training_args.seed							)

 # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
 # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
 # (the dataset will be downloaded automatically from the datasets Hub).
 #
 # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
 # 'text' is found. You can easily tweak this behavior (see below).
 #
 # In distributed training, the load_dataset function guarantee that only one local process can concurrently
 # download the dataset.
 if data_args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  lowerCamelCase		:	Dict							=      load_dataset(data_args.dataset_name,	data_args.dataset_config_name							)
  if "validation" not in datasets.keys():
   lowerCamelCase		:	Tuple							=      load_dataset(
       data_args.dataset_name,	data_args.dataset_config_name,	split=F'''train[:{data_args.validation_split_percentage}%]''',	)
   lowerCamelCase		:	Tuple							=      load_dataset(
       data_args.dataset_name,	data_args.dataset_config_name,	split=F'''train[{data_args.validation_split_percentage}%:]''',	)
 else:
  lowerCamelCase		:	Any							=      {}
  if data_args.train_file is not None:
   lowerCamelCase		:	List[str]							=      data_args.train_file
  if data_args.validation_file is not None:
   lowerCamelCase		:	int							=      data_args.validation_file
  lowerCamelCase		:	List[str]							=      data_args.train_file.split("""."""							)[-1]
  if extension == "txt":
   lowerCamelCase		:	Tuple							=      """text"""
  lowerCamelCase		:	Optional[int]							=      load_dataset(lowerCamelCase,	data_files=lowerCamelCase							)
 # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
 # https://huggingface.co/docs/datasets/loading_datasets.html.

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 lowerCamelCase		:	List[str]							=      {
     """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:
  lowerCamelCase		:	Any							=      AutoConfig.from_pretrained(model_args.config_name,	**lowerCamelCase							)
 elif model_args.model_name_or_path:
  lowerCamelCase		:	Dict							=      AutoConfig.from_pretrained(model_args.model_name_or_path,	**lowerCamelCase							)
 else:
  lowerCamelCase		:	Optional[int]							=      CONFIG_MAPPING[model_args.model_type]()
  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}'''							)

 lowerCamelCase		:	str							=      {
     """cache_dir""": model_args.cache_dir,
     """use_fast""": model_args.use_fast_tokenizer,
     """revision""": model_args.model_revision,
     """use_auth_token""": True if model_args.use_auth_token else None,
 }
 if model_args.tokenizer_name:
  lowerCamelCase		:	Tuple							=      AutoTokenizer.from_pretrained(model_args.tokenizer_name,	**lowerCamelCase							)
 elif model_args.model_name_or_path:
  lowerCamelCase		:	Dict							=      AutoTokenizer.from_pretrained(model_args.model_name_or_path,	**lowerCamelCase							)
 else:
  raise ValueError(
      """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
      """You can do it from another script, save it, and load it from here, using --tokenizer_name."""							)

 if model_args.model_name_or_path:
  lowerCamelCase		:	Optional[Any]							=      AutoModelForMaskedLM.from_pretrained(
      model_args.model_name_or_path,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path							),	config=lowerCamelCase,	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"""							)
  lowerCamelCase		:	Dict							=      AutoModelForMaskedLM.from_config(lowerCamelCase							)

 model.resize_token_embeddings(len(lowerCamelCase							)							)

 # Preprocessing the datasets.
 # First we tokenize all the texts.
 if training_args.do_train:
  lowerCamelCase		:	Union[str, Any]							=      datasets["""train"""].column_names
 else:
  lowerCamelCase		:	List[Any]							=      datasets["""validation"""].column_names
 lowerCamelCase		:	Dict							=      """text""" if """text""" in column_names else column_names[0]

 lowerCamelCase		:	Dict							=      """max_length""" if data_args.pad_to_max_length else False

 def tokenize_function(lowerCamelCase							):
  # Remove empty lines
  lowerCamelCase		:	List[Any]							=      [line for line in examples["""text"""] if len(lowerCamelCase							) > 0 and not line.isspace()]
  return tokenizer(examples["""text"""],	padding=lowerCamelCase,	truncation=lowerCamelCase,	max_length=data_args.max_seq_length							)

 lowerCamelCase		:	Any							=      datasets.map(
     lowerCamelCase,	batched=lowerCamelCase,	num_proc=data_args.preprocessing_num_workers,	remove_columns=[text_column_name],	load_from_cache_file=not data_args.overwrite_cache,	)

 # Add the chinese references if provided
 if data_args.train_ref_file is not None:
  lowerCamelCase		:	Optional[int]							=      add_chinese_references(tokenized_datasets["""train"""],	data_args.train_ref_file							)
 if data_args.validation_ref_file is not None:
  lowerCamelCase		:	Union[str, Any]							=      add_chinese_references(
      tokenized_datasets["""validation"""],	data_args.validation_ref_file							)
 # If we have ref files, need to avoid it removed by trainer
 lowerCamelCase		:	Optional[int]							=      data_args.train_ref_file or data_args.validation_ref_file
 if has_ref:
  lowerCamelCase		:	Optional[Any]							=      False

 # Data collator
 # This one will take care of randomly masking the tokens.
 lowerCamelCase		:	List[str]							=      DataCollatorForWholeWordMask(tokenizer=lowerCamelCase,	mlm_probability=data_args.mlm_probability							)

 # Initialize our Trainer
 lowerCamelCase		:	int							=      Trainer(
     model=lowerCamelCase,	args=lowerCamelCase,	train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None,	eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None,	tokenizer=lowerCamelCase,	data_collator=lowerCamelCase,	)

 # Training
 if training_args.do_train:
  if last_checkpoint is not None:
   lowerCamelCase		:	Any							=      last_checkpoint
  elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path							):
   lowerCamelCase		:	List[str]							=      model_args.model_name_or_path
  else:
   lowerCamelCase		:	str							=      None
  lowerCamelCase		:	str							=      trainer.train(resume_from_checkpoint=lowerCamelCase							)
  trainer.save_model()  # Saves the tokenizer too for easy upload

  lowerCamelCase		:	Tuple							=      os.path.join(training_args.output_dir,	"""train_results.txt"""							)
  if trainer.is_world_process_zero():
   with open(lowerCamelCase,	"""w"""							) as writer:
    logger.info("""***** Train results *****"""							)
    for key, value in sorted(train_result.metrics.items()							):
     logger.info(F'''  {key} = {value}'''							)
     writer.write(F'''{key} = {value}\n'''							)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
   trainer.state.save_to_json(os.path.join(training_args.output_dir,	"""trainer_state.json"""							)							)

    # Evaluation
 lowerCamelCase		:	Optional[int]							=      {}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""							)

  lowerCamelCase		:	List[str]							=      trainer.evaluate()

  lowerCamelCase		:	List[Any]							=      math.exp(eval_output["""eval_loss"""]							)
  lowerCamelCase		:	int							=      perplexity

  lowerCamelCase		:	List[str]							=      os.path.join(training_args.output_dir,	"""eval_results_mlm_wwm.txt"""							)
  if trainer.is_world_process_zero():
   with open(lowerCamelCase,	"""w"""							) as writer:
    logger.info("""***** Eval results *****"""							)
    for key, value in sorted(results.items()							):
     logger.info(F'''  {key} = {value}'''							)
     writer.write(F'''{key} = {value}\n'''							)

 return results





def     _a					(						lowerCamelCase							):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			["""pixel_values"""]
      def __init__(      self    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = PILImageResampling.BILINEAR    ,  __magic_name__ = True    ,  __magic_name__ = 1 / 2_5_5    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      size if size is not None else {"""shortest_edge""": 3_8_4}
       lowerCamelCase		:	Tuple							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	Dict							=      do_resize
       lowerCamelCase		:	List[Any]							=      size
       # Default value set here for backwards compatibility where the value in config is None
       lowerCamelCase		:	Any							=      crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       lowerCamelCase		:	Union[str, Any]							=      resample
       lowerCamelCase		:	str							=      do_rescale
       lowerCamelCase		:	Union[str, Any]							=      rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase		:	int							=      image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = PILImageResampling.BICUBIC    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	Union[str, Any]							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )
       if "shortest_edge" not in size:
        raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''      )
       lowerCamelCase		:	str							=      size["""shortest_edge"""]

       if shortest_edge < 3_8_4:
        # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
        lowerCamelCase		:	List[str]							=      int(shortest_edge / crop_pct      )
        lowerCamelCase		:	Optional[Any]							=      get_resize_output_image_size(__magic_name__    ,  size=__magic_name__    ,  default_to_square=__magic_name__      )
        lowerCamelCase		:	Optional[int]							=      resize(image=__magic_name__    ,  size=__magic_name__    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
        # then crop to (shortest_edge, shortest_edge)
        return center_crop(image=__magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  data_format=__magic_name__    ,  **__magic_name__      )
       else:
        # warping (no cropping) when evaluated at 384 or larger
        return resize(
            __magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return rescale(__magic_name__    ,  scale=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return normalize(__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = ChannelDimension.FIRST    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	str							=      do_resize if do_resize is not None else self.do_resize
       lowerCamelCase		:	Optional[Any]							=      crop_pct if crop_pct is not None else self.crop_pct
       lowerCamelCase		:	Optional[int]							=      resample if resample is not None else self.resample
       lowerCamelCase		:	Optional[int]							=      do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase		:	List[str]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else self.image_mean
       lowerCamelCase		:	Tuple							=      image_std if image_std is not None else self.image_std

       lowerCamelCase		:	Dict							=      size if size is not None else self.size
       lowerCamelCase		:	str							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	List[str]							=      make_list_of_images(__magic_name__      )

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

       if do_resize and size is None or resample is None:
        raise ValueError("""Size and resample must be specified if do_resize is True."""      )

       if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
        raise ValueError("""crop_pct must be specified if size < 384."""      )

       if do_rescale and rescale_factor is None:
        raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

       # All transformations expect numpy arrays.
       lowerCamelCase		:	Optional[Any]							=      [to_numpy_array(__magic_name__      ) for image in images]

       if do_resize:
        lowerCamelCase		:	List[Any]							=      [self.resize(image=__magic_name__    ,  size=__magic_name__    ,  crop_pct=__magic_name__    ,  resample=__magic_name__      ) for image in images]

       if do_rescale:
        lowerCamelCase		:	Union[str, Any]							=      [self.rescale(image=__magic_name__    ,  scale=__magic_name__      ) for image in images]

       if do_normalize:
        lowerCamelCase		:	List[Any]							=      [self.normalize(image=__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__      ) for image in images]

       lowerCamelCase		:	Optional[int]							=      [to_channel_dimension_format(__magic_name__    ,  __magic_name__      ) for image in images]

       lowerCamelCase		:	List[str]							=      {"""pixel_values""": images}
       return BatchFeature(data=__magic_name__    ,  tensor_type=__magic_name__      )




