"""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__   =					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 				lowerCAmelCase__      (     _UpperCamelCase    :			str   )   ->			Dict:





						"""simple docstring"""






						with open(_UpperCamelCase	,     'rb'   ) as f:
												snake_case        =     Image.open(_UpperCamelCase   )
												return im.convert('RGB'   )



@dataclass
class 					lowerCAmelCase_      :



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

				def 						snake_case	(			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 					lowerCAmelCase_      :



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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]   )   ->			str:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     )   ->			str:





						"""simple docstring"""






						snake_case        =     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.
												snake_case   ,snake_case   ,snake_case        =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
						else:
												snake_case   ,snake_case   ,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_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()

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

    # 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:
												snake_case        =     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:
												snake_case        =     {}
												if data_args.train_dir is not None:
																		snake_case        =     os.path.join(data_args.train_dir	,     '**'   )
												if data_args.validation_dir is not None:
																		snake_case        =     os.path.join(data_args.validation_dir	,     '**'   )
												snake_case        =     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.
						snake_case        =     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:
												snake_case        =     dataset['train'].train_test_split(data_args.train_val_split   )
												snake_case        =     split['train']
												snake_case        =     split['test']

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

						# Load the accuracy metric from the datasets package
						snake_case        =     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(_UpperCamelCase    :			Any   ):
												return metric.compute(predictions=np.argmax(p.predictions	,     axis=1   )	,     references=p.label_ids   )

						snake_case        =     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	,     )
						snake_case        =     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	,     )
						snake_case        =     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:
												snake_case        =     image_processor.size['shortest_edge']
						else:
												snake_case        =     (image_processor.size['height'], image_processor.size['width'])
						snake_case        =     Normalize(mean=image_processor.image_mean	,     std=image_processor.image_std   )
						snake_case        =     Compose(
						    [
						        RandomResizedCrop(_UpperCamelCase   ),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]   )
						snake_case        =     Compose(
						    [
						        Resize(_UpperCamelCase   ),
						        CenterCrop(_UpperCamelCase   ),
						        ToTensor(),
						        normalize,
						    ]   )

						def train_transforms(_UpperCamelCase    :			List[Any]   ):
												snake_case        =     [
												    _train_transforms(pil_img.convert('RGB'   )   ) for pil_img in example_batch['image']
												]
												return example_batch

						def val_transforms(_UpperCamelCase    :			List[Any]   ):
												snake_case        =     [_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:
																		snake_case        =     (
																		    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:
																		snake_case        =     (
																		    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
						snake_case        =     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:
												snake_case        =     None
												if training_args.resume_from_checkpoint is not None:
																		snake_case        =     training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		snake_case        =     last_checkpoint
												snake_case        =     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:
												snake_case        =     trainer.evaluate()
												trainer.log_metrics('eval'	,     _UpperCamelCase   )
												trainer.save_metrics('eval'	,     _UpperCamelCase   )

						# Write model card and (optionally) push to hub
						snake_case        =     {
						    '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()



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any   )   ->			int:





						"""simple docstring"""






						snake_case        =     {}

						snake_case        =     job['started_at']
						snake_case        =     job['completed_at']

						snake_case        =     date_parser.parse(_UpperCamelCase   )
						snake_case        =     date_parser.parse(_UpperCamelCase   )

						snake_case        =     round((end_datetime - start_datetime).total_seconds() / 60.0   )

						snake_case        =     start
						snake_case        =     end
						snake_case        =     duration_in_min

						return job_info





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Any=None   )   ->			Union[str, Any]:





						"""simple docstring"""






						snake_case        =     None
						if token is not None:
												snake_case        =     {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

						snake_case        =     f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
						snake_case        =     requests.get(_UpperCamelCase	,     headers=_UpperCamelCase   ).json()
						snake_case        =     {}

						try:
												job_time.update({job['name']: extract_time_from_single_job(_UpperCamelCase   ) for job in result['jobs']}   )
												snake_case        =     math.ceil((result['total_count'] - 1_0_0) / 1_0_0   )

												for i in range(_UpperCamelCase   ):
																		snake_case        =     requests.get(url + f"""&page={i + 2}"""	,     headers=_UpperCamelCase   ).json()
																		job_time.update({job['name']: extract_time_from_single_job(_UpperCamelCase   ) for job in result['jobs']}   )

												return job_time
						except Exception:
												print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

						return {}


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()

					SCREAMING_SNAKE_CASE__   =					get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE__   =					dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

					for k, v in job_time.items():
										print(f"""{k}: {v['duration']}""")


