
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
		from fairscale.optim import OSS


lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}





class    __A(					UpperCAmelCase							):

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  Tuple=None ,       __UpperCamelCase       :  Dict=None ,       *__UpperCamelCase       :  Optional[Any] ,       **__UpperCamelCase       :  List[Any] ):
												super().__init__(*__UpperCamelCase ,       **__UpperCamelCase )

												if config is None:
																			assert isinstance(self.model ,       __UpperCamelCase ), (
																			    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
																			    F''' {self.model.__class__}'''
																			)
																			lowerCamelCase_					=       self.model.config
												else:
																			lowerCamelCase_					=       config

												lowerCamelCase_					=       data_args
												lowerCamelCase_					=       self.config.tgt_vocab_size if isinstance(self.config ,       __UpperCamelCase ) else self.config.vocab_size

												if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
																			assert self.config.pad_token_id is not None, (
																			    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
																			    " calculation or doing label smoothing."
																			)

												if self.config.pad_token_id is None and self.config.eos_token_id is not None:
																			logger.warning(
																			    F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
																			    """ padding..""" )

												if self.args.label_smoothing == 0:
																			lowerCamelCase_					=       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
												else:
																			# dynamically import label_smoothed_nll_loss
																			from utils import label_smoothed_nll_loss

																			lowerCamelCase_					=       label_smoothed_nll_loss

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  int ):
												if self.optimizer is None:
																			lowerCamelCase_					=       ["""bias""", """LayerNorm.weight"""]
																			lowerCamelCase_					=       [
																			    {
																			        """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
																			        """weight_decay""": self.args.weight_decay,
																			    },
																			    {
																			        """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
																			        """weight_decay""": 0.0,
																			    },
																			]
																			lowerCamelCase_					=       Adafactor if self.args.adafactor else AdamW
																			if self.args.adafactor:
																										lowerCamelCase_					=       Adafactor
																										lowerCamelCase_					=       {"""scale_parameter""": False, """relative_step""": False}
																			else:
																										lowerCamelCase_					=       AdamW
																										lowerCamelCase_					=       {
																										    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
																										    """eps""": self.args.adam_epsilon,
																										}
																			lowerCamelCase_					=       self.args.learning_rate
																			if self.sharded_ddp:
																										lowerCamelCase_					=       OSS(
																										    params=__UpperCamelCase ,       optim=__UpperCamelCase ,       **__UpperCamelCase ,       )
																			else:
																										lowerCamelCase_					=       optimizer_cls(__UpperCamelCase ,       **__UpperCamelCase )

												if self.lr_scheduler is None:
																			lowerCamelCase_					=       self._get_lr_scheduler(__UpperCamelCase )
												else:  # ignoring --lr_scheduler
																			logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.""" )

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       arg_to_scheduler[self.args.lr_scheduler]
												if self.args.lr_scheduler == "constant":
																			lowerCamelCase_					=       schedule_func(self.optimizer )
												elif self.args.lr_scheduler == "constant_w_warmup":
																			lowerCamelCase_					=       schedule_func(self.optimizer ,       num_warmup_steps=self.args.warmup_steps )
												else:
																			lowerCamelCase_					=       schedule_func(
																			    self.optimizer ,       num_warmup_steps=self.args.warmup_steps ,       num_training_steps=__UpperCamelCase )
												return scheduler

					def  lowercase__   (						self       :  Union[str, Any] ):
												if isinstance(self.train_dataset ,       torch.utils.data.IterableDataset ):
																			return None
												elif is_torch_tpu_available():
																			return get_tpu_sampler(self.train_dataset )
												else:
																			if self.args.sortish_sampler:
																										self.train_dataset.make_sortish_sampler(
																										    self.args.per_device_train_batch_size ,       distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) ,       )

																			return (
																			    RandomSampler(self.train_dataset )
																			    if self.args.local_rank == -1
																			    else DistributedSampler(self.train_dataset )
																			)

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  List[str] ):
												if self.args.label_smoothing == 0:
																			if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																										# force training to ignore pad token
																										lowerCamelCase_					=       model(**__UpperCamelCase ,       use_cache=__UpperCamelCase )[0]
																										lowerCamelCase_					=       self.loss_fn(logits.view(-1 ,       logits.shape[-1] ) ,       labels.view(-1 ) )
																			else:
																										# compute usual loss via models
																										lowerCamelCase_	, lowerCamelCase_					=       model(**__UpperCamelCase ,       labels=__UpperCamelCase ,       use_cache=__UpperCamelCase )[:2]
												else:
																			# compute label smoothed loss
																			lowerCamelCase_					=       model(**__UpperCamelCase ,       use_cache=__UpperCamelCase )[0]
																			lowerCamelCase_					=       torch.nn.functional.log_softmax(__UpperCamelCase ,       dim=-1 )
																			lowerCamelCase_	, lowerCamelCase_					=       self.loss_fn(__UpperCamelCase ,       __UpperCamelCase ,       self.args.label_smoothing ,       ignore_index=self.config.pad_token_id )
												return loss, logits

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Any ):
												lowerCamelCase_					=       inputs.pop("""labels""" )
												lowerCamelCase_	, lowerCamelCase_					=       self._compute_loss(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
												return loss

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  nn.Module ,       __UpperCamelCase       :  Dict[str, Union[torch.Tensor, Any]] ,       __UpperCamelCase       :  bool ,       __UpperCamelCase       :  Optional[List[str]] = None ,       ):
												lowerCamelCase_					=       self._prepare_inputs(__UpperCamelCase )

												lowerCamelCase_					=       {
												    """max_length""": self.data_args.val_max_target_length
												    if self.data_args is not None
												    else self.config.max_length,
												    """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
												}

												if self.args.predict_with_generate and not self.args.prediction_loss_only:
																			lowerCamelCase_					=       self.model.generate(
																			    inputs["""input_ids"""] ,       attention_mask=inputs["""attention_mask"""] ,       **__UpperCamelCase ,       )
																			# in case the batch is shorter than max length, the output should be padded
																			if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																										lowerCamelCase_					=       self._pad_tensors_to_max_len(__UpperCamelCase ,       gen_kwargs["""max_length"""] )

												lowerCamelCase_					=       inputs.pop("""labels""" )
												with torch.no_grad():
																			# compute loss on predict data
																			lowerCamelCase_	, lowerCamelCase_					=       self._compute_loss(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

												lowerCamelCase_					=       loss.mean().detach()
												if self.args.prediction_loss_only:
																			return (loss, None, None)

												lowerCamelCase_					=       generated_tokens if self.args.predict_with_generate else logits

												if labels.shape[-1] < gen_kwargs["max_length"]:
																			lowerCamelCase_					=       self._pad_tensors_to_max_len(__UpperCamelCase ,       gen_kwargs["""max_length"""] )

												return (loss, logits, labels)



					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Union[str, Any] ):
												# If PAD token is not defined at least EOS token has to be defined
												lowerCamelCase_					=       self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

												if pad_token_id is None:
																			raise ValueError(
																			    """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
																			    F''' padded to `max_length`={max_length}''' )

												lowerCamelCase_					=       pad_token_id * torch.ones(
												    (tensor.shape[0], max_length) ,       dtype=tensor.dtype ,       device=tensor.device )
												lowerCamelCase_					=       tensor
												return padded_tensor







from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase   =	{'''UserAgent''': UserAgent().random}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     dict:
							lowerCamelCase_					=       script.contents[0]
							lowerCamelCase_					=       json.loads(data[data.find("""{\"config\""""							) : -1]							)
							return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class    __A:

					def __init__(						self       :  int ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       F'''https://www.instagram.com/{username}/'''
												lowerCamelCase_					=       self.get_json()

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       requests.get(self.url ,       headers=__UpperCamelCase ).text
												lowerCamelCase_					=       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       :  Dict ):
												return F'''{self.__class__.__name__}(\'{self.username}\')'''

					def __str__(						self       :  Optional[Any] ):
												return F'''{self.fullname} ({self.username}) is {self.biography}'''

					@property
					def  lowercase__   (						self       :  Dict ):
												return self.user_data["username"]

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.user_data["full_name"]

					@property
					def  lowercase__   (						self       :  Any ):
												return self.user_data["biography"]

					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["business_email"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["external_url"]

					@property
					def  lowercase__   (						self       :  List[Any] ):
												return self.user_data["edge_followed_by"]["count"]

					@property
					def  lowercase__   (						self       :  str ):
												return self.user_data["edge_follow"]["count"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["edge_owner_to_timeline_media"]["count"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["profile_pic_url_hd"]

					@property
					def  lowercase__   (						self       :  Optional[Any] ):
												return self.user_data["is_verified"]



					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["is_private"]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "github"							)	->     None:
							import os

							if os.environ.get("""CI"""							):
														return  # test failing on GitHub Actions
							lowerCamelCase_					=       InstagramUser(UpperCAmelCase__							)
							assert instagram_user.user_data
							assert isinstance(instagram_user.user_data      ,					UpperCAmelCase__							)
							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 > 1_5_0
							assert instagram_user.number_of_followers > 1_2_0_0_0_0
							assert instagram_user.number_of_followings > 1_5
							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()
		lowercase   =	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 = }""")





