


"""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
a__       :							str        =  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 				UpperCamelCase_			:

	"""simple docstring"""





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




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[str]:
						__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 				UpperCamelCase_			:

	"""simple docstring"""





	snake_case__       :						str					      =    field(
	    default=UpperCamelCase    ,						metadata={
	        "help": (
	            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
	        )
	    }    ,						)
	snake_case__       :						Optional[str]					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"})
	snake_case__       :						Optional[str]					      =    field(
	    default=UpperCamelCase    ,						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"
	        )
	    }    ,						)
	snake_case__       :						Optional[str]					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Where do you want to store the pretrained models downloaded from s3"})
	snake_case__       :						str					      =    field(
	    default="main"    ,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,						)
	snake_case__       :						str					      =    field(default=UpperCamelCase    ,						metadata={"help": "Name or path of preprocessor config."})
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }    ,						)
	snake_case__       :						float					      =    field(
	    default=0.75    ,						metadata={"help": "The ratio of the number of masked tokens in the input sequence."})
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Whether or not to train with normalized pixel values as target."})




@dataclass
class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						float					      =    field(
	    default=1E-3    ,						metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."})




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ ():




					'''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"						,		lowerCAmelCase_						,		lowerCAmelCase_     )

					# 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(lowerCAmelCase_     )
					transformers.utils.logging.set_verbosity(lowerCAmelCase_     )
					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						,		lowerCAmelCase_     ) 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						,		**lowerCAmelCase_     )
					elif model_args.model_name_or_path:
										__SCREAMING_SNAKE_CASE		     =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,		**lowerCAmelCase_     )
					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						,		**lowerCAmelCase_     )
					elif model_args.model_name_or_path:
										__SCREAMING_SNAKE_CASE		     =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,		**lowerCAmelCase_     )
					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=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"     )
										__SCREAMING_SNAKE_CASE		     =       ViTMAEForPreTraining(lowerCAmelCase_     )

					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 lowerCAmelCase_     : img.convert("RGB"     ) if img.mode != "RGB" else img     ),
					        RandomResizedCrop(lowerCAmelCase_						,		scale=(0.2, 1.0)						,		interpolation=InterpolationMode.BICUBIC     ),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean						,		std=image_processor.image_std     ),
					    ]     )

					def preprocess_images(lowerCAmelCase_     ):
										__SCREAMING_SNAKE_CASE		     =       [transforms(lowerCAmelCase_     ) 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(lowerCAmelCase_     )

					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(lowerCAmelCase_     )

					# 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 / 256

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

					# 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=lowerCAmelCase_     )
										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"						,		lowerCAmelCase_     )
										trainer.save_metrics("eval"						,		lowerCAmelCase_     )

					# 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(**lowerCAmelCase_     )
					else:
										trainer.create_model_card(**lowerCAmelCase_     )




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					main()


if __name__ == "__main__":
		main()




"""simple docstring"""







import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


a__       :							Dict        =  logging.get_logger(__name__)




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					def constraint_to_multiple_of(lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=0						,		lowerCAmelCase_=None     ):
										__SCREAMING_SNAKE_CASE		     =       round(val / multiple     ) * multiple

										if max_val is not None and x > max_val:
															__SCREAMING_SNAKE_CASE		     =       math.floor(val / multiple     ) * multiple

										if x < min_val:
															__SCREAMING_SNAKE_CASE		     =       math.ceil(val / multiple     ) * multiple

										return x

					__SCREAMING_SNAKE_CASE		     =       (output_size, output_size) if isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) else output_size

					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       get_image_size(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       output_size

					# determine new height and width
					__SCREAMING_SNAKE_CASE		     =       output_height / input_height
					__SCREAMING_SNAKE_CASE		     =       output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width     ) < abs(1 - scale_height     ):
															# fit width
															__SCREAMING_SNAKE_CASE		     =       scale_width
										else:
															# fit height
															__SCREAMING_SNAKE_CASE		     =       scale_height

					__SCREAMING_SNAKE_CASE		     =       constraint_to_multiple_of(scale_height * input_height						,		multiple=lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       constraint_to_multiple_of(scale_width * input_width						,		multiple=lowerCAmelCase_     )

					return (new_height, new_width)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						List[Any]					      =    ["pixel_values"]




	def __init__(				self    :       int    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Dict[str, int] = None    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BILINEAR    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       int = 1    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Union[int, float] = 1 / 2_5_5    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    **UpperCAmelCase__    :       Optional[Any]    ,    ) ->			None:
						super().__init__(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else {"height": 3_8_4, "width": 3_8_4}
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       do_resize
						__SCREAMING_SNAKE_CASE		     =       size
						__SCREAMING_SNAKE_CASE		     =       keep_aspect_ratio
						__SCREAMING_SNAKE_CASE		     =       ensure_multiple_of
						__SCREAMING_SNAKE_CASE		     =       resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Dict[str, int]    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       int = 1    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BICUBIC    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Dict    ,    ) ->			np.ndarray:
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						if "height" not in size or "width" not in size:
											raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""     )
						__SCREAMING_SNAKE_CASE		     =       get_resize_output_image_size(
						    UpperCAmelCase__    ,    output_size=(size["height"], size["width"])    ,    keep_aspect_ratio=UpperCAmelCase__    ,    multiple=UpperCAmelCase__    ,    )
						return resize(UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    resample=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[int, float]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			Any:
						return rescale(UpperCAmelCase__    ,    scale=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Optional[Any]    ,    ) ->			np.ndarray:
						return normalize(UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]    ,    UpperCAmelCase__    :       ImageInput    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       int = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       int = None    ,    UpperCAmelCase__    :       PILImageResampling = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       float = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[str, TensorType]] = None    ,    UpperCAmelCase__    :       ChannelDimension = ChannelDimension.FIRST    ,    **UpperCAmelCase__    :       int    ,    ) ->			PIL.Image.Image:
						__SCREAMING_SNAKE_CASE		     =       do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else self.size
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						__SCREAMING_SNAKE_CASE		     =       ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						__SCREAMING_SNAKE_CASE		     =       resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE		     =       make_list_of_images(UpperCAmelCase__     )

						if not valid_images(UpperCAmelCase__     ):
											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_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.
						__SCREAMING_SNAKE_CASE		     =       [to_numpy_array(UpperCAmelCase__     ) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE		     =       [self.resize(image=UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    resample=UpperCAmelCase__     ) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE		     =       [self.rescale(image=UpperCAmelCase__    ,    scale=UpperCAmelCase__     ) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE		     =       [self.normalize(image=UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       [to_channel_dimension_format(UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__    ,    tensor_type=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Optional[int]    ,    UpperCAmelCase__    :       List[str]    ,    UpperCAmelCase__    :       List[Tuple] = None     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
											if len(UpperCAmelCase__     ) != len(UpperCAmelCase__     ):
																raise ValueError(
																    "Make sure that you pass in as many target sizes as the batch dimension of the logits"     )

											if is_torch_tensor(UpperCAmelCase__     ):
																__SCREAMING_SNAKE_CASE		     =       target_sizes.numpy()

											__SCREAMING_SNAKE_CASE		     =       []

											for idx in range(len(UpperCAmelCase__     )     ):
																__SCREAMING_SNAKE_CASE		     =       torch.nn.functional.interpolate(
																    logits[idx].unsqueeze(dim=0     )    ,    size=target_sizes[idx]    ,    mode="bilinear"    ,    align_corners=UpperCAmelCase__     )
																__SCREAMING_SNAKE_CASE		     =       resized_logits[0].argmax(dim=0     )
																semantic_segmentation.append(UpperCAmelCase__     )
						else:
											__SCREAMING_SNAKE_CASE		     =       logits.argmax(dim=1     )
											__SCREAMING_SNAKE_CASE		     =       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

						return semantic_segmentation
