



"""simple docstring"""
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
__SCREAMING_SNAKE_CASE  :						Tuple    							=      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''')

__SCREAMING_SNAKE_CASE  :						List[Any]    							=      list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__SCREAMING_SNAKE_CASE  :						Tuple    							=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def        lowerCAmelCase_(   lowercase_     :							str							)	->       Dict:
				with open(lowercase_						,   '''rb'''							) as f:
								_lowerCamelCase        =				Image.open(lowercase_							)
								return im.convert('''RGB'''							)


@dataclass
class 							lowerCamelCase_:





					'''simple docstring'''



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

					def 			snake_case__      (    self   ):
									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 							lowerCamelCase_:





					'''simple docstring'''



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



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]							)	->       Tuple:
				_lowerCamelCase        =				torch.stack([example['''pixel_values'''] for example in examples]							)
				_lowerCamelCase        =				torch.tensor([example['''labels'''] for example in examples]							)
				return {"pixel_values": pixel_values, "labels": labels}



def        lowerCAmelCase_(   )	->       int:
				# 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        =				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        =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
				else:
								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				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'''						,   lowercase_						,   lowercase_							)

				# 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()

				_lowerCamelCase        =				training_args.get_process_log_level()
				logger.setLevel(lowercase_							)
				transformers.utils.logging.set_verbosity(lowercase_							)
				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.
				_lowerCamelCase        =				None
				if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
								_lowerCamelCase        =				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:
								_lowerCamelCase        =				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:
								_lowerCamelCase        =				{}
								if data_args.train_dir is not None:
												_lowerCamelCase        =				os.path.join(data_args.train_dir						,   '''**'''							)
								if data_args.validation_dir is not None:
												_lowerCamelCase        =				os.path.join(data_args.validation_dir						,   '''**'''							)
								_lowerCamelCase        =				load_dataset(
								    '''imagefolder'''						,   data_files=lowercase_						,   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.
				_lowerCamelCase        =				None if '''validation''' in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split						,   lowercase_							) and data_args.train_val_split > 0.0:
								_lowerCamelCase        =				dataset['''train'''].train_test_split(data_args.train_val_split							)
								_lowerCamelCase        =				split['''train''']
								_lowerCamelCase        =				split['''test''']

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

				# Load the accuracy metric from the datasets package
				_lowerCamelCase        =				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(lowercase_     :							Union[str, Any]							):
								return metric.compute(predictions=np.argmax(p.predictions						,   axis=1							)						,   references=p.label_ids							)

				_lowerCamelCase        =				AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path						,   num_labels=len(lowercase_							)						,   labelaid=lowercase_						,   idalabel=lowercase_						,   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						,   )
				_lowerCamelCase        =				AutoModelForImageClassification.from_pretrained(
				    model_args.model_name_or_path						,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)						,   config=lowercase_						,   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						,   )
				_lowerCamelCase        =				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:
								_lowerCamelCase        =				image_processor.size['''shortest_edge''']
				else:
								_lowerCamelCase        =				(image_processor.size['''height'''], image_processor.size['''width'''])
				_lowerCamelCase        =				Normalize(mean=image_processor.image_mean						,   std=image_processor.image_std							)
				_lowerCamelCase        =				Compose(
				    [
				        RandomResizedCrop(lowercase_							),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]							)
				_lowerCamelCase        =				Compose(
				    [
				        Resize(lowercase_							),
				        CenterCrop(lowercase_							),
				        ToTensor(),
				        normalize,
				    ]							)

				def train_transforms(lowercase_     :							Optional[Any]							):
								_lowerCamelCase        =				[
								    _train_transforms(pil_img.convert('''RGB'''							)							) for pil_img in example_batch['''image''']
								]
								return example_batch

				def val_transforms(lowercase_     :							Tuple							):
								_lowerCamelCase        =				[_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:
												_lowerCamelCase        =				(
												    dataset['''train'''].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
												)
								# Set the training transforms
								dataset["train"].set_transform(lowercase_							)

				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:
												_lowerCamelCase        =				(
												    dataset['''validation'''].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
												)
								# Set the validation transforms
								dataset["validation"].set_transform(lowercase_							)

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

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

				# Write model card and (optionally) push to hub
				_lowerCamelCase        =				{
				    '''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(**lowercase_							)
				else:
								trainer.create_model_card(**lowercase_							)


if __name__ == "__main__":
			main()





"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
			from .pipeline_kandinsky import KandinskyPipeline
			from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
			from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
			from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
			from .text_encoder import MultilingualCLIP
