


'''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
UpperCAmelCase_		:   int							   =		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')

UpperCAmelCase_		:   Union[str, Any]							   =		list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCAmelCase_		:   Tuple							   =		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				with open(_lowercase							,      """rb"""     ) as f:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      Image.open(_lowercase     )
								return im.convert("""RGB"""     )





@dataclass
class     lowercase__  :





							'''simple docstring'''


							A_							:      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)."""
							    }     ,     )
							A_							:      Optional[str]      	=  field(
							    default=a__     ,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}					)
							A_							:      Optional[str]      	=  field(default=a__     ,     metadata={"""help""": """A folder containing the training data."""}					)
							A_							:      Optional[str]      	=  field(default=a__     ,     metadata={"""help""": """A folder containing the validation data."""}					)
							A_							:      Optional[float]      	=  field(
							    default=0.15     ,     metadata={"""help""": """Percent to split off of train for validation."""}					)
							A_							:      Optional[int]      	=  field(
							    default=a__     ,     metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of training examples to this """
							            """value if set."""
							        )
							    }     ,     )
							A_							:      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 UpperCAmelCase_	(    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     lowercase__  :





							'''simple docstring'''


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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      torch.stack([example["""pixel_values"""] for example in examples]     )
				_SCREAMING_SNAKE_CASE :       Dict     		=      torch.tensor([example["""labels"""] for example in examples]     )
				return {"pixel_values": pixel_values, "labels": labels}







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[Any]     		=      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.
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
				else:
								_SCREAMING_SNAKE_CASE :       Dict     		=      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()

				_SCREAMING_SNAKE_CASE :       List[str]     		=      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.
				_SCREAMING_SNAKE_CASE :       Tuple     		=      None
				if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
								_SCREAMING_SNAKE_CASE :       Optional[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:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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:
								_SCREAMING_SNAKE_CASE :       int     		=      {}
								if data_args.train_dir is not None:
												_SCREAMING_SNAKE_CASE :       List[Any]     		=      os.path.join(data_args.train_dir							,      """**"""     )
								if data_args.validation_dir is not None:
												_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      os.path.join(data_args.validation_dir							,      """**"""     )
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      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.
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      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:
								_SCREAMING_SNAKE_CASE :       Tuple     		=      dataset["train"].train_test_split(data_args.train_val_split     )
								_SCREAMING_SNAKE_CASE :       List[str]     		=      split["train"]
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      split["test"]

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

				# Load the accuracy metric from the datasets package
				_SCREAMING_SNAKE_CASE :       Optional[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(SCREAMING_SNAKE_CASE__     ):
								return metric.compute(predictions=np.argmax(p.predictions							,      axis=1     )							,      references=p.label_ids     )

				_SCREAMING_SNAKE_CASE :       str     		=      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							,      )
				_SCREAMING_SNAKE_CASE :       int     		=      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							,      )
				_SCREAMING_SNAKE_CASE :       Dict     		=      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:
								_SCREAMING_SNAKE_CASE :       str     		=      image_processor.size["shortest_edge"]
				else:
								_SCREAMING_SNAKE_CASE :       Any     		=      (image_processor.size["height"], image_processor.size["width"])
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      Normalize(mean=image_processor.image_mean							,      std=image_processor.image_std     )
				_SCREAMING_SNAKE_CASE :       List[Any]     		=      Compose(
				    [
				        RandomResizedCrop(_lowercase     ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      Compose(
				    [
				        Resize(_lowercase     ),
				        CenterCrop(_lowercase     ),
				        ToTensor(),
				        normalize,
				    ]     )

				def train_transforms(SCREAMING_SNAKE_CASE__     ):
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      [
								    _train_transforms(pil_img.convert("""RGB"""     )     ) for pil_img in example_batch["image"]
								]
								return example_batch

				def val_transforms(SCREAMING_SNAKE_CASE__     ):
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      [_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:
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      (
												    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:
												_SCREAMING_SNAKE_CASE :       Any     		=      (
												    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
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      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:
								_SCREAMING_SNAKE_CASE :       Any     		=      None
								if training_args.resume_from_checkpoint is not None:
												_SCREAMING_SNAKE_CASE :       Tuple     		=      training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      last_checkpoint
								_SCREAMING_SNAKE_CASE :       Dict     		=      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:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      trainer.evaluate()
								trainer.log_metrics("""eval"""							,      _lowercase     )
								trainer.save_metrics("""eval"""							,      _lowercase     )

				# Write model card and (optionally) push to hub
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {
				    "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 datetime import datetime

import requests






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->bytes:






	'''simple docstring'''






	a      :		Dict	=				"https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
	a      :		Dict	=				requests.get(base_url + url					).json()[0]["urls"][0]["src"]
	return requests.get(_lowercase					).content


if __name__ == "__main__":
		a						: str     	=			input('''Enter Video/IGTV url: ''').strip()
		a						: Any     	=			F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
		with open(file_name, '''wb''') as fp:
				fp.write(download_video(url))
		print(F'''Done. Video saved to disk as {file_name}.''')


