


"""simple docstring"""







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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = None  ,						):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    {}

					if train_file is not None:
										__SCREAMING_SNAKE_CASE						       =    [train_file]
					if eval_file is not None:
										__SCREAMING_SNAKE_CASE						       =    [eval_file]
					if test_file is not None:
										__SCREAMING_SNAKE_CASE						       =    [test_file]

					__SCREAMING_SNAKE_CASE						       =    datasets.load_dataset("csv"  ,						data_files=lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    list(ds[list(files.keys()       )[0]].features.keys()       )
					__SCREAMING_SNAKE_CASE						       =    features_name.pop(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    list(set(ds[list(files.keys()       )[0]][label_name]       )       )
					__SCREAMING_SNAKE_CASE						       =    {label: i for i, label in enumerate(lowerCAmelCase_       )}
					__SCREAMING_SNAKE_CASE						       =    tokenizer.model_input_names
					__SCREAMING_SNAKE_CASE						       =    {}

					if len(lowerCAmelCase_       ) == 1:
										for k in files.keys():
															__SCREAMING_SNAKE_CASE						       =    ds[k].map(
															    lambda lowerCAmelCase_       : tokenizer.batch_encode_plus(
															        example[features_name[0]]  ,						truncation=lowerCAmelCase_  ,						max_length=lowerCAmelCase_  ,						padding="max_length"       )  ,						batched=lowerCAmelCase_  ,						)
					elif len(lowerCAmelCase_       ) == 2:
										for k in files.keys():
															__SCREAMING_SNAKE_CASE						       =    ds[k].map(
															    lambda lowerCAmelCase_       : tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]])  ,						truncation=lowerCAmelCase_  ,						max_length=lowerCAmelCase_  ,						padding="max_length"  ,						)  ,						batched=lowerCAmelCase_  ,						)

					def gen_train():
										for ex in transformed_ds[datasets.Split.TRAIN]:
															__SCREAMING_SNAKE_CASE						       =    {k: v for k, v in ex.items() if k in input_names}
															__SCREAMING_SNAKE_CASE						       =    labelaid[ex[label_name]]
															yield (d, label)

					def gen_val():
										for ex in transformed_ds[datasets.Split.VALIDATION]:
															__SCREAMING_SNAKE_CASE						       =    {k: v for k, v in ex.items() if k in input_names}
															__SCREAMING_SNAKE_CASE						       =    labelaid[ex[label_name]]
															yield (d, label)

					def gen_test():
										for ex in transformed_ds[datasets.Split.TEST]:
															__SCREAMING_SNAKE_CASE						       =    {k: v for k, v in ex.items() if k in input_names}
															__SCREAMING_SNAKE_CASE						       =    labelaid[ex[label_name]]
															yield (d, label)

					__SCREAMING_SNAKE_CASE						       =    (
					    tf.data.Dataset.from_generator(
					        lowerCAmelCase_  ,						({k: tf.intaa for k in input_names}, tf.intaa)  ,						({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,						)
					    if datasets.Split.TRAIN in transformed_ds
					    else None
					)

					if train_ds is not None:
										__SCREAMING_SNAKE_CASE						       =    train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

					__SCREAMING_SNAKE_CASE						       =    (
					    tf.data.Dataset.from_generator(
					        lowerCAmelCase_  ,						({k: tf.intaa for k in input_names}, tf.intaa)  ,						({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,						)
					    if datasets.Split.VALIDATION in transformed_ds
					    else None
					)

					if val_ds is not None:
										__SCREAMING_SNAKE_CASE						       =    val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

					__SCREAMING_SNAKE_CASE						       =    (
					    tf.data.Dataset.from_generator(
					        lowerCAmelCase_  ,						({k: tf.intaa for k in input_names}, tf.intaa)  ,						({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,						)
					    if datasets.Split.TEST in transformed_ds
					    else None
					)

					if test_ds is not None:
										__SCREAMING_SNAKE_CASE						       =    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

					return train_ds, val_ds, test_ds, labelaid


a__    :							int            =					logging.getLogger(__name__)




@dataclass
class 			UpperCamelCase_     :

	"""simple docstring"""





	snake_case__    :  int    =      field(metadata={"help": "Which column contains the label"})
	snake_case__    :  str    =      field(default=UpperCamelCase      ,  metadata={"help": "The path of the training file"})
	snake_case__    :  Optional[str]    =      field(default=UpperCamelCase      ,  metadata={"help": "The path of the development file"})
	snake_case__    :  Optional[str]    =      field(default=UpperCamelCase      ,  metadata={"help": "The path of the test file"})
	snake_case__    :  int    =      field(
	    default=128      ,  metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }      ,  )
	snake_case__    :  bool    =      field(
	    default=UpperCamelCase      ,  metadata={"help": "Overwrite the cached training and evaluation sets"})




@dataclass
class 			UpperCamelCase_     :

	"""simple docstring"""





	snake_case__    :  str    =      field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
	snake_case__    :  Optional[str]    =      field(
	    default=UpperCamelCase      ,  metadata={"help": "Pretrained config name or path if not the same as model_name"})
	snake_case__    :  Optional[str]    =      field(
	    default=UpperCamelCase      ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
	snake_case__    :  bool    =      field(default=UpperCamelCase      ,  metadata={"help": "Set this flag to use fast tokenization."})
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	snake_case__    :  Optional[str]    =      field(
	    default=UpperCamelCase      ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,  )




def        UpperCAmelCase__	():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
					__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir       )
					    and os.listdir(training_args.output_dir       )
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
										    " --overwrite_output_dir to overcome."       )

					# Setup logging
					logging.basicConfig(
					    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,						datefmt="%m/%d/%Y %H:%M:%S"  ,						level=logging.INFO  ,						)
					logger.info(
					    f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1       )}, """
					    f"""16-bits training: {training_args.fpaa}"""       )
					logger.info(f"""Training/evaluation parameters {training_args}"""       )

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					__SCREAMING_SNAKE_CASE						       =    AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,						cache_dir=model_args.cache_dir  ,						)

					__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    get_tfds(
					    train_file=data_args.train_file  ,						eval_file=data_args.dev_file  ,						test_file=data_args.test_file  ,						tokenizer=lowerCAmelCase_  ,						label_column_id=data_args.label_column_id  ,						max_seq_length=data_args.max_seq_length  ,						)

					__SCREAMING_SNAKE_CASE						       =    AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,						num_labels=len(lowerCAmelCase_       )  ,						labelaid=lowerCAmelCase_  ,						idalabel={id: label for label, id in labelaid.items()}  ,						finetuning_task="text-classification"  ,						cache_dir=model_args.cache_dir  ,						)

					with training_args.strategy.scope():
										__SCREAMING_SNAKE_CASE						       =    TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path  ,						from_pt=bool(".bin" in model_args.model_name_or_path       )  ,						config=lowerCAmelCase_  ,						cache_dir=model_args.cache_dir  ,						)

					def compute_metrics(lowerCAmelCase_       ) -> Dict:
										__SCREAMING_SNAKE_CASE						       =    np.argmax(p.predictions  ,						axis=1       )

										return {"acc": (preds == p.label_ids).mean()}

					# Initialize our Trainer
					__SCREAMING_SNAKE_CASE						       =    TFTrainer(
					    model=lowerCAmelCase_  ,						args=lowerCAmelCase_  ,						train_dataset=lowerCAmelCase_  ,						eval_dataset=lowerCAmelCase_  ,						compute_metrics=lowerCAmelCase_  ,						)

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir       )

					# Evaluation
					__SCREAMING_SNAKE_CASE						       =    {}
					if training_args.do_eval:
										logger.info("*** Evaluate ***"       )
										__SCREAMING_SNAKE_CASE						       =    trainer.evaluate()
										__SCREAMING_SNAKE_CASE						       =    os.path.join(training_args.output_dir  ,						"eval_results.txt"       )

										with open(lowerCAmelCase_  ,						"w"       ) as writer:
															logger.info("***** Eval results *****"       )

															for key, value in result.items():
																				logger.info(f"""  {key} = {value}"""       )
																				writer.write(f"""{key} = {value}\n"""       )

															results.update(lowerCAmelCase_       )

					return results


if __name__ == "__main__":
		main()




"""simple docstring"""







from datetime import datetime

import requests




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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


if __name__ == "__main__":
		a__    :							str            =					input('''Enter Video/IGTV url: ''').strip()
		a__    :							List[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}.")
