






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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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__			:    Union[str, Any]         =				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-classification/requirements.txt')

A__			:    List[str]         =				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A__			:    List[str]         =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     '''rb'''       ) as f:
								lowercase__     				=    Image.open(lowerCamelCase_       )
								return im.convert('''RGB'''       )
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= field(
				    default=A__	,metadata={
				        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
				    }	,)
				lowercase__       							= field(
				    default=A__	,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}			)
				lowercase__       							= field(default=A__	,metadata={"""help""": """A folder containing the training data."""}			)
				lowercase__       							= field(default=A__	,metadata={"""help""": """A folder containing the validation data."""}			)
				lowercase__       							= field(
				    default=0.15	,metadata={"""help""": """Percent to split off of train for validation."""}			)
				lowercase__       							= field(
				    default=A__	,metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }	,)
				lowercase__       							= field(
				    default=A__	,metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }	,)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
												raise ValueError(
												    '''You must specify either a dataset name from the hub or a train and/or validation directory.'''						)



@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= field(
				    default="""google/vit-base-patch16-224-in21k"""	,metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	,)
				lowercase__       							= field(
				    default=A__	,metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(A__			)}	,)
				lowercase__       							= field(
				    default=A__	,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
				lowercase__       							= field(
				    default=A__	,metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}			)
				lowercase__       							= field(
				    default="""main"""	,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}	,)
				lowercase__       							= field(default=A__	,metadata={"""help""": """Name or path of preprocessor config."""}			)
				lowercase__       							= field(
				    default=A__	,metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }	,)
				lowercase__       							= field(
				    default=A__	,metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}	,)






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    torch.stack([example['''pixel_values'''] for example in examples]       )
				lowercase__     				=    torch.tensor([example['''labels'''] for example in examples]       )
				return {"pixel_values": pixel_values, "labels": labels}






def 						a    (		):






				'''simple docstring'''
				# 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.

				lowercase__     				=    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.
								lowercase__				,		lowercase__				,		lowercase__     				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								lowercase__				,		lowercase__				,		lowercase__     				=    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_image_classification'''	,     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()

				lowercase__     				=    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.
				lowercase__     				=    None
				if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
								lowercase__     				=    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.'''       )

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

				# Initialize our dataset and prepare it for the 'image-classification' task.
				if data_args.dataset_name is not None:
								lowercase__     				=    load_dataset(
								    data_args.dataset_name	,     data_args.dataset_config_name	,     cache_dir=model_args.cache_dir	,     task='''image-classification'''	,     use_auth_token=True if model_args.use_auth_token else None	,     )
				else:
								lowercase__     				=    {}
								if data_args.train_dir is not None:
												lowercase__     				=    os.path.join(data_args.train_dir	,     '''**'''       )
								if data_args.validation_dir is not None:
												lowercase__     				=    os.path.join(data_args.validation_dir	,     '''**'''       )
								lowercase__     				=    load_dataset(
								    '''imagefolder'''	,     data_files=lowerCamelCase_	,     cache_dir=model_args.cache_dir	,     task='''image-classification'''	,     )

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

				# Prepare label mappings.
				# We'll include these in the model's config to get human readable labels in the Inference API.
				lowercase__     				=    dataset['''train'''].features['''labels'''].names
				lowercase__				,		lowercase__     				=    {}, {}
				for i, label in enumerate(lowerCamelCase_       ):
								lowercase__     				=    str(lowerCamelCase_       )
								lowercase__     				=    label

				# Load the accuracy metric from the datasets package
				lowercase__     				=    evaluate.load('''accuracy'''       )

				# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(lowerCamelCase_       ):
								return metric.compute(predictions=np.argmax(p.predictions	,     axis=1       )	,     references=p.label_ids       )

				lowercase__     				=    AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path	,     num_labels=len(lowerCamelCase_       )	,     labelaid=lowerCamelCase_	,     idalabel=lowerCamelCase_	,     finetuning_task='''image-classification'''	,     cache_dir=model_args.cache_dir	,     revision=model_args.model_revision	,     use_auth_token=True if model_args.use_auth_token else None	,     )
				lowercase__     				=    AutoModelForImageClassification.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	,     ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,     )
				lowercase__     				=    AutoImageProcessor.from_pretrained(
				    model_args.image_processor_name or model_args.model_name_or_path	,     cache_dir=model_args.cache_dir	,     revision=model_args.model_revision	,     use_auth_token=True if model_args.use_auth_token else None	,     )

				# Define torchvision transforms to be applied to each image.
				if "shortest_edge" in image_processor.size:
								lowercase__     				=    image_processor.size['''shortest_edge''']
				else:
								lowercase__     				=    (image_processor.size['''height'''], image_processor.size['''width'''])
				lowercase__     				=    Normalize(mean=image_processor.image_mean	,     std=image_processor.image_std       )
				lowercase__     				=    Compose(
				    [
				        RandomResizedCrop(lowerCamelCase_       ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]       )
				lowercase__     				=    Compose(
				    [
				        Resize(lowerCamelCase_       ),
				        CenterCrop(lowerCamelCase_       ),
				        ToTensor(),
				        normalize,
				    ]       )

				def train_transforms(lowerCamelCase_       ):
								lowercase__     				=    [
								    _train_transforms(pil_img.convert('''RGB'''       )       ) for pil_img in example_batch['''image''']
								]
								return example_batch

				def val_transforms(lowerCamelCase_       ):
								lowercase__     				=    [_val_transforms(pil_img.convert('''RGB'''       )       ) for pil_img in example_batch['''image''']]
								return example_batch

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

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

				# Initalize our trainer
				lowercase__     				=    Trainer(
				    model=lowerCamelCase_	,     args=lowerCamelCase_	,     train_dataset=dataset['''train'''] if training_args.do_train else None	,     eval_dataset=dataset['''validation'''] if training_args.do_eval else None	,     compute_metrics=lowerCamelCase_	,     tokenizer=lowerCamelCase_	,     data_collator=lowerCamelCase_	,     )

				# Training
				if training_args.do_train:
								lowercase__     				=    None
								if training_args.resume_from_checkpoint is not None:
												lowercase__     				=    training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowercase__     				=    last_checkpoint
								lowercase__     				=    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:
								lowercase__     				=    trainer.evaluate()
								trainer.log_metrics('''eval'''	,     lowerCamelCase_       )
								trainer.save_metrics('''eval'''	,     lowerCamelCase_       )

				# Write model card and (optionally) push to hub
				lowercase__     				=    {
				    '''finetuned_from''': model_args.model_name_or_path,
				    '''tasks''': '''image-classification''',
				    '''dataset''': data_args.dataset_name,
				    '''tags''': ['''image-classification''', '''vision'''],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**lowerCamelCase_       )
				else:
								trainer.create_model_card(**lowerCamelCase_       )


if __name__ == "__main__":
							main()












# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
												return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




