

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_            = 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(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
SCREAMING_SNAKE_CASE_            = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[Any]	):


				with open(_UpperCAmelCase					, "rb"	) as f:
								_UpperCAmelCase    :						Tuple							=      Image.open(_UpperCAmelCase	)
								return im.convert("RGB"	)

@dataclass
class 			a					:
						_lowercase        =     field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
						    }							,	)
						_lowercase        =     field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
						_lowercase        =     field(default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "A folder containing the training data."}			)
						_lowercase        =     field(default=SCREAMING_SNAKE_CASE__							,	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=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of training examples to this "
						            "value if set."
						        )
						    }							,	)
						_lowercase        =     field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
						            "value if set."
						        )
						    }							,	)


						def     _UpperCAmelCase			(    self		):







										'''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 			a					:
						_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=SCREAMING_SNAKE_CASE__							,	metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(SCREAMING_SNAKE_CASE__			)}							,	)
						_lowercase        =     field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
						_lowercase        =     field(
						    default=SCREAMING_SNAKE_CASE__							,	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=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Name or path of preprocessor config."}			)
						_lowercase        =     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)."
						        )
						    }							,	)
						_lowercase        =     field(
						    default=SCREAMING_SNAKE_CASE__							,	metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}							,	)







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	):
				_UpperCAmelCase    :						Optional[int]							=      torch.stack([example["pixel_values"] for example in examples]	)
				_UpperCAmelCase    :						Dict							=      torch.tensor([example["labels"] for example in examples]	)
				return {"pixel_values": pixel_values, "labels": labels}

def 							__SCREAMING_SNAKE_CASE							(			):
				_UpperCAmelCase    :						Tuple							=      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.
								_UpperCAmelCase    :						Dict							=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
				else:
								_UpperCAmelCase    :						Optional[Any]							=      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"					, _UpperCAmelCase					, _UpperCAmelCase	)

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

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

				_UpperCAmelCase    :						Optional[int]							=      training_args.get_process_log_level()
				logger.setLevel(_UpperCAmelCase	)
				transformers.utils.logging.set_verbosity(_UpperCAmelCase	)
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

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

				# Detecting last checkpoint.
				_UpperCAmelCase    :						List[str]							=      None
				if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
								_UpperCAmelCase    :						List[Any]							=      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:
								_UpperCAmelCase    :						Optional[Any]							=      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:
								_UpperCAmelCase    :						Optional[Any]							=      {}
								if data_args.train_dir is not None:
												_UpperCAmelCase    :						Optional[int]							=      os.path.join(data_args.train_dir					, "**"	)
								if data_args.validation_dir is not None:
												_UpperCAmelCase    :						Any							=      os.path.join(data_args.validation_dir					, "**"	)
								_UpperCAmelCase    :						List[str]							=      load_dataset(
								    "imagefolder"					, data_files=_UpperCAmelCase					, 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.
				_UpperCAmelCase    :						int							=      None if 'validation' in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split					, _UpperCAmelCase	) and data_args.train_val_split > 0.0:
								_UpperCAmelCase    :						Optional[int]							=      dataset['train'].train_test_split(data_args.train_val_split	)
								_UpperCAmelCase    :						int							=      split['train']
								_UpperCAmelCase    :						str							=      split['test']

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

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

				_UpperCAmelCase    :						List[Any]							=      AutoConfig.from_pretrained(
				    model_args.config_name or model_args.model_name_or_path					, num_labels=len(_UpperCAmelCase	)					, labelaid=_UpperCAmelCase					, idalabel=_UpperCAmelCase					, 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					, )
				_UpperCAmelCase    :						List[Any]							=      AutoModelForImageClassification.from_pretrained(
				    model_args.model_name_or_path					, from_tf=bool(".ckpt" in model_args.model_name_or_path	)					, config=_UpperCAmelCase					, cache_dir=model_args.cache_dir					, revision=model_args.model_revision					, use_auth_token=True if model_args.use_auth_token else None					, ignore_mismatched_sizes=model_args.ignore_mismatched_sizes					, )
				_UpperCAmelCase    :						List[Any]							=      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:
								_UpperCAmelCase    :						Union[str, Any]							=      image_processor.size['shortest_edge']
				else:
								_UpperCAmelCase    :						Optional[Any]							=      (image_processor.size['height'], image_processor.size['width'])
				_UpperCAmelCase    :						int							=      Normalize(mean=image_processor.image_mean					, std=image_processor.image_std	)
				_UpperCAmelCase    :						str							=      Compose(
				    [
				        RandomResizedCrop(_UpperCAmelCase	),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]	)
				_UpperCAmelCase    :						Optional[Any]							=      Compose(
				    [
				        Resize(_UpperCAmelCase	),
				        CenterCrop(_UpperCAmelCase	),
				        ToTensor(),
				        normalize,
				    ]	)

				def train_transforms(lowerCAmelCase:					int	):
								_UpperCAmelCase    :						int							=      [
								    _train_transforms(pil_img.convert("RGB"	)	) for pil_img in example_batch['image']
								]
								return example_batch

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

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

				# Initalize our trainer
				_UpperCAmelCase    :						Union[str, Any]							=      Trainer(
				    model=_UpperCAmelCase					, args=_UpperCAmelCase					, train_dataset=dataset["train"] if training_args.do_train else None					, eval_dataset=dataset["validation"] if training_args.do_eval else None					, compute_metrics=_UpperCAmelCase					, tokenizer=_UpperCAmelCase					, data_collator=_UpperCAmelCase					, )

				# Training
				if training_args.do_train:
								_UpperCAmelCase    :						int							=      None
								if training_args.resume_from_checkpoint is not None:
												_UpperCAmelCase    :						Union[str, Any]							=      training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_UpperCAmelCase    :						Tuple							=      last_checkpoint
								_UpperCAmelCase    :						List[Any]							=      trainer.train(resume_from_checkpoint=_UpperCAmelCase	)
								trainer.save_model()
								trainer.log_metrics("train"					, train_result.metrics	)
								trainer.save_metrics("train"					, train_result.metrics	)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								_UpperCAmelCase    :						str							=      trainer.evaluate()
								trainer.log_metrics("eval"					, _UpperCAmelCase	)
								trainer.save_metrics("eval"					, _UpperCAmelCase	)

				# Write model card and (optionally) push to hub
				_UpperCAmelCase    :						Optional[Any]							=      {
				    '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(**_UpperCAmelCase	)
				else:
								trainer.create_model_card(**_UpperCAmelCase	)


if __name__ == "__main__":
					main()





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    '--original_config_file',
					    default=None,
					    type=str,
					    help='The YAML config file corresponding to the original architecture.',
					)
					parser.add_argument(
					    '--num_in_channels',
					    default=None,
					    type=int,
					    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
					)
					parser.add_argument(
					    '--scheduler_type',
					    default='pndm',
					    type=str,
					    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
					)
					parser.add_argument(
					    '--pipeline_type',
					    default=None,
					    type=str,
					    help=(
					        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
					        '. If `None` pipeline will be automatically inferred.'
					    ),
					)
					parser.add_argument(
					    '--image_size',
					    default=None,
					    type=int,
					    help=(
					        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
					        ' Base. Use 768 for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--prediction_type',
					    default=None,
					    type=str,
					    help=(
					        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
					        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--extract_ema',
					    action='store_true',
					    help=(
					        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
					        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
					        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
					    ),
					)
					parser.add_argument(
					    '--upcast_attention',
					    action='store_true',
					    help=(
					        'Whether the attention computation should always be upcasted. This is necessary when running stable'
					        ' diffusion 2.1.'
					    ),
					)
					parser.add_argument(
					    '--from_safetensors',
					    action='store_true',
					    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
					)
					parser.add_argument(
					    '--to_safetensors',
					    action='store_true',
					    help='Whether to store pipeline in safetensors format or not.',
					)
					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
					parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
					parser.add_argument(
					    '--stable_unclip',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
					)
					parser.add_argument(
					    '--stable_unclip_prior',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
					)
					parser.add_argument(
					    '--clip_stats_path',
					    type=str,
					    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
					    required=False,
					)
					parser.add_argument(
					    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
					)
					parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
					parser.add_argument(
					    '--vae_path',
					    type=str,
					    default=None,
					    required=False,
					    help='Set to a path, hub id to an already converted vae to not convert it again.',
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()

					SCREAMING_SNAKE_CASE_            = download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


