import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    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


# 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/text-classification/requirements.txt")

SCREAMING_SNAKE_CASE		:     Dict         =	logging.getLogger(__name__)

@dataclass
class        UpperCamelCase						:
						a__		:Optional[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.'''
						        )
						    }	,	)
						a__		:bool      =			field(
						    default=__a	,	metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}				)
						a__		:bool      =			field(
						    default=__a	,	metadata={
						        '''help''': (
						            '''Whether to pad all samples to `max_seq_length`. '''
						            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
						        )
						    }	,	)
						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.'''
						        )
						    }	,	)
						a__		:Optional[int]      =			field(
						    default=__a	,	metadata={
						        '''help''': (
						            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
						            '''value if set.'''
						        )
						    }	,	)


@dataclass
class        UpperCamelCase						:
						a__		:str      =			field(
						    default=__a	,	metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
						a__		:str      =			field(
						    default=__a	,	metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}				)
						a__		:Optional[str]      =			field(
						    default=__a	,	metadata={'''help''': '''Train language if it is different from the evaluation language.'''}				)
						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''': '''Pretrained tokenizer 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 huggingface.co'''}	,	)
						a__		:Optional[bool]      =			field(
						    default=__a	,	metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}	,	)
						a__		:bool      =			field(
						    default=__a	,	metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}	,	)
						a__		:str      =			field(
						    default='''main'''	,	metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}	,	)
						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     lowerCAmelCase_			(   ):
						# 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.

						UpperCamelCase_		:							List[str]  =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
						UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		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_xnli"""		, _SCREAMING_SNAKE_CASE			)

						# 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_		:							str  =		training_args.get_process_log_level()
						logger.setLevel(_SCREAMING_SNAKE_CASE			)
						datasets.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE			)
						transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE			)
						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[Any]  =		None
						if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
												UpperCamelCase_		:							int  =		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:
																		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			)

						# In distributed training, the load_dataset function guarantees that only one local process can concurrently
						# download the dataset.
						# Downloading and loading xnli dataset from the hub.
						if training_args.do_train:
												if model_args.train_language is None:
																		UpperCamelCase_		:							str  =		load_dataset(
																		    """xnli"""		, model_args.language		, split="""train"""		, cache_dir=model_args.cache_dir		, use_auth_token=True if model_args.use_auth_token else None		, )
												else:
																		UpperCamelCase_		:							List[Any]  =		load_dataset(
																		    """xnli"""		, model_args.train_language		, split="""train"""		, cache_dir=model_args.cache_dir		, use_auth_token=True if model_args.use_auth_token else None		, )
												UpperCamelCase_		:							Optional[Any]  =		train_dataset.features["""label"""].names

						if training_args.do_eval:
												UpperCamelCase_		:							Tuple  =		load_dataset(
												    """xnli"""		, model_args.language		, split="""validation"""		, cache_dir=model_args.cache_dir		, use_auth_token=True if model_args.use_auth_token else None		, )
												UpperCamelCase_		:							Optional[Any]  =		eval_dataset.features["""label"""].names

						if training_args.do_predict:
												UpperCamelCase_		:							Optional[int]  =		load_dataset(
												    """xnli"""		, model_args.language		, split="""test"""		, cache_dir=model_args.cache_dir		, use_auth_token=True if model_args.use_auth_token else None		, )
												UpperCamelCase_		:							Any  =		predict_dataset.features["""label"""].names

						# Labels
						UpperCamelCase_		:							str  =		len(_SCREAMING_SNAKE_CASE			)

						# Load pretrained model and tokenizer
						# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						UpperCamelCase_		:							Tuple  =		AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path		, num_labels=_SCREAMING_SNAKE_CASE		, idalabel={str(_SCREAMING_SNAKE_CASE			): label for i, label in enumerate(_SCREAMING_SNAKE_CASE			)}		, labelaid={label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE			)}		, finetuning_task="""xnli"""		, 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[str]  =		AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		, do_lower_case=model_args.do_lower_case		, cache_dir=model_args.cache_dir		, use_fast=model_args.use_fast_tokenizer		, revision=model_args.model_revision		, use_auth_token=True if model_args.use_auth_token else None		, )
						UpperCamelCase_		:							List[Any]  =		AutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path		, from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)		, config=_SCREAMING_SNAKE_CASE		, 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		, )

						# Preprocessing the datasets
						# Padding strategy
						if data_args.pad_to_max_length:
												UpperCamelCase_		:							Any  =		"""max_length"""
						else:
												# We will pad later, dynamically at batch creation, to the max sequence length in each batch
												UpperCamelCase_		:							Tuple  =		False

						def preprocess_function(_SCREAMING_SNAKE_CASE     :				List[str]			):
												# Tokenize the texts
												return tokenizer(
												    examples["""premise"""]		, examples["""hypothesis"""]		, padding=_SCREAMING_SNAKE_CASE		, max_length=data_args.max_seq_length		, truncation=_SCREAMING_SNAKE_CASE		, )

						if training_args.do_train:
												if data_args.max_train_samples is not None:
																		UpperCamelCase_		:							Optional[int]  =		min(len(_SCREAMING_SNAKE_CASE			)		, data_args.max_train_samples			)
																		UpperCamelCase_		:							str  =		train_dataset.select(range(_SCREAMING_SNAKE_CASE			)			)
												with training_args.main_process_first(desc="""train dataset map pre-processing"""			):
																		UpperCamelCase_		:							Optional[int]  =		train_dataset.map(
																		    _SCREAMING_SNAKE_CASE		, batched=_SCREAMING_SNAKE_CASE		, load_from_cache_file=not data_args.overwrite_cache		, desc="""Running tokenizer on train dataset"""		, )
												# Log a few random samples from the training set:
												for index in random.sample(range(len(_SCREAMING_SNAKE_CASE			)			)		, 3			):
																		logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''			)

						if training_args.do_eval:
												if data_args.max_eval_samples is not None:
																		UpperCamelCase_		:							Dict  =		min(len(_SCREAMING_SNAKE_CASE			)		, data_args.max_eval_samples			)
																		UpperCamelCase_		:							Any  =		eval_dataset.select(range(_SCREAMING_SNAKE_CASE			)			)
												with training_args.main_process_first(desc="""validation dataset map pre-processing"""			):
																		UpperCamelCase_		:							Tuple  =		eval_dataset.map(
																		    _SCREAMING_SNAKE_CASE		, batched=_SCREAMING_SNAKE_CASE		, load_from_cache_file=not data_args.overwrite_cache		, desc="""Running tokenizer on validation dataset"""		, )

						if training_args.do_predict:
												if data_args.max_predict_samples is not None:
																		UpperCamelCase_		:							List[Any]  =		min(len(_SCREAMING_SNAKE_CASE			)		, data_args.max_predict_samples			)
																		UpperCamelCase_		:							int  =		predict_dataset.select(range(_SCREAMING_SNAKE_CASE			)			)
												with training_args.main_process_first(desc="""prediction dataset map pre-processing"""			):
																		UpperCamelCase_		:							List[str]  =		predict_dataset.map(
																		    _SCREAMING_SNAKE_CASE		, batched=_SCREAMING_SNAKE_CASE		, load_from_cache_file=not data_args.overwrite_cache		, desc="""Running tokenizer on prediction dataset"""		, )

    # Get the metric function
						UpperCamelCase_		:							Union[str, Any]  =		evaluate.load("""xnli"""			)

						# You can define your custom 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     :				EvalPrediction			):
												UpperCamelCase_		:							Optional[int]  =		p.predictions[0] if isinstance(p.predictions		, _SCREAMING_SNAKE_CASE			) else p.predictions
												UpperCamelCase_		:							List[Any]  =		np.argmax(_SCREAMING_SNAKE_CASE		, axis=1			)
												return metric.compute(predictions=_SCREAMING_SNAKE_CASE		, references=p.label_ids			)

						# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
						if data_args.pad_to_max_length:
												UpperCamelCase_		:							Optional[Any]  =		default_data_collator
						elif training_args.fpaa:
												UpperCamelCase_		:							Dict  =		DataCollatorWithPadding(_SCREAMING_SNAKE_CASE		, pad_to_multiple_of=8			)
						else:
												UpperCamelCase_		:							Tuple  =		None

						# Initialize our Trainer
						UpperCamelCase_		:							List[str]  =		Trainer(
						    model=_SCREAMING_SNAKE_CASE		, args=_SCREAMING_SNAKE_CASE		, train_dataset=train_dataset if training_args.do_train else None		, eval_dataset=eval_dataset if training_args.do_eval else None		, compute_metrics=_SCREAMING_SNAKE_CASE		, tokenizer=_SCREAMING_SNAKE_CASE		, data_collator=_SCREAMING_SNAKE_CASE		, )

						# Training
						if training_args.do_train:
												UpperCamelCase_		:							Dict  =		None
												if training_args.resume_from_checkpoint is not None:
																		UpperCamelCase_		:							Dict  =		training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		UpperCamelCase_		:							Tuple  =		last_checkpoint
												UpperCamelCase_		:							List[str]  =		trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE			)
												UpperCamelCase_		:							Any  =		train_result.metrics
												UpperCamelCase_		:							Tuple  =		(
												    data_args.max_train_samples if data_args.max_train_samples is not None else len(_SCREAMING_SNAKE_CASE			)
												)
												UpperCamelCase_		:							Any  =		min(_SCREAMING_SNAKE_CASE		, len(_SCREAMING_SNAKE_CASE			)			)

												trainer.save_model()  # Saves the tokenizer too for easy upload

												trainer.log_metrics("""train"""		, _SCREAMING_SNAKE_CASE			)
												trainer.save_metrics("""train"""		, _SCREAMING_SNAKE_CASE			)
												trainer.save_state()

						# Evaluation
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""			)
												UpperCamelCase_		:							Dict  =		trainer.evaluate(eval_dataset=_SCREAMING_SNAKE_CASE			)

												UpperCamelCase_		:							Tuple  =		data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_SCREAMING_SNAKE_CASE			)
												UpperCamelCase_		:							Dict  =		min(_SCREAMING_SNAKE_CASE		, len(_SCREAMING_SNAKE_CASE			)			)

												trainer.log_metrics("""eval"""		, _SCREAMING_SNAKE_CASE			)
												trainer.save_metrics("""eval"""		, _SCREAMING_SNAKE_CASE			)

						# Prediction
						if training_args.do_predict:
												logger.info("""*** Predict ***"""			)
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							List[str]  =		trainer.predict(_SCREAMING_SNAKE_CASE		, metric_key_prefix="""predict"""			)

												UpperCamelCase_		:							List[str]  =		(
												    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_SCREAMING_SNAKE_CASE			)
												)
												UpperCamelCase_		:							int  =		min(_SCREAMING_SNAKE_CASE		, len(_SCREAMING_SNAKE_CASE			)			)

												trainer.log_metrics("""predict"""		, _SCREAMING_SNAKE_CASE			)
												trainer.save_metrics("""predict"""		, _SCREAMING_SNAKE_CASE			)

												UpperCamelCase_		:							Dict  =		np.argmax(_SCREAMING_SNAKE_CASE		, axis=1			)
												UpperCamelCase_		:							List[Any]  =		os.path.join(training_args.output_dir		, """predictions.txt"""			)
												if trainer.is_world_process_zero():
																		with open(_SCREAMING_SNAKE_CASE		, """w"""			) as writer:
																								writer.write("""index\tprediction\n"""			)
																								for index, item in enumerate(_SCREAMING_SNAKE_CASE			):
																														UpperCamelCase_		:							List[Any]  =		label_list[item]
																														writer.write(f'''{index}\t{item}\n'''			)


if __name__ == "__main__":
	main()







from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{"UserAgent": UserAgent().random}






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict			):
						UpperCamelCase_		:							Tuple  =		script.contents[0]
						UpperCamelCase_		:							List[Any]  =		json.loads(data[data.find("""{\"config\""""			) : -1]			)
						return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]

class        UpperCamelCase						:





						def __init__(self			,     __UpperCamelCase						)				->				List[str]:
												UpperCamelCase_		:							Union[str, Any]  =		f'''https://www.instagram.com/{username}/'''
												UpperCamelCase_		:							Optional[int]  =		self.get_json()





						def        A_							(self						)				->				dict:
												UpperCamelCase_		:							Optional[Any]  =		requests.get(self.url			,     headers=__UpperCamelCase						).text
												UpperCamelCase_		:							Any  =		BeautifulSoup(__UpperCamelCase			,     """html.parser"""						).find_all("""script"""						)
												try:
																		return extract_user_profile(scripts[4]						)
												except (json.decoder.JSONDecodeError, KeyError):
																		return extract_user_profile(scripts[3]						)





						def __repr__(self						)				->				str:
												return f'''{self.__class__.__name__}(\'{self.username}\')'''





						def __str__(self						)				->				str:
												return f'''{self.fullname} ({self.username}) is {self.biography}'''





						@property
						def        A_							(self						)				->				str:
												return self.user_data["username"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["full_name"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["biography"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["business_email"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["external_url"]





						@property
						def        A_							(self						)				->				int:
												return self.user_data["edge_followed_by"]["count"]





						@property
						def        A_							(self						)				->				int:
												return self.user_data["edge_follow"]["count"]





						@property
						def        A_							(self						)				->				int:
												return self.user_data["edge_owner_to_timeline_media"]["count"]





						@property
						def        A_							(self						)				->				str:
												return self.user_data["profile_pic_url_hd"]





						@property
						def        A_							(self						)				->				bool:
												return self.user_data["is_verified"]





						@property
						def        A_							(self						)				->				bool:
												return self.user_data["is_private"]






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str = "github"			):
						import os

						if os.environ.get("""CI"""			):
												return  # test failing on GitHub Actions
						UpperCamelCase_		:							Any  =		InstagramUser(_SCREAMING_SNAKE_CASE			)
						assert instagram_user.user_data
						assert isinstance(instagram_user.user_data		, _SCREAMING_SNAKE_CASE			)
						assert instagram_user.username == username
						if username != "github":
												return
						assert instagram_user.fullname == "GitHub"
						assert instagram_user.biography == "Built for developers."
						assert instagram_user.number_of_posts > 150
						assert instagram_user.number_of_followers > 12_0000
						assert instagram_user.number_of_followings > 15
						assert instagram_user.email == "support@github.com"
						assert instagram_user.website == "https://github.com/readme"
						assert instagram_user.profile_picture_url.startswith("""https://instagram."""			)
						assert instagram_user.is_verified is True
						assert instagram_user.is_private is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	SCREAMING_SNAKE_CASE		:     Dict         =	InstagramUser("github")
	print(instagram_user)
	print(F'''{instagram_user.number_of_posts = }''')
	print(F'''{instagram_user.number_of_followers = }''')
	print(F'''{instagram_user.number_of_followings = }''')
	print(F'''{instagram_user.email = }''')
	print(F'''{instagram_user.website = }''')
	print(F'''{instagram_user.profile_picture_url = }''')
	print(F'''{instagram_user.is_verified = }''')
	print(F'''{instagram_user.is_private = }''')






