

"""simple docstring"""





import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
							import bsa
							from bsa import BeautifulSoup


__UpperCamelCase  :      int							      =		logging.get_logger(__name__)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,**lowercase_			:				str		):
						requires_backends(self   ,['''bs4''']		)
						super().__init__(**lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				Optional[Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	[]
						lowerCAmelCase__	:						str  				=	[]
						lowerCAmelCase__	:						Optional[int]  				=	element if element.name else element.parent
						for parent in child.parents:  # type: bs4.element.Tag
							lowerCAmelCase__	:						Union[str, Any]  				=	parent.find_all(child.name   ,recursive=lowercase_		)
							xpath_tags.append(child.name		)
							xpath_subscripts.append(
							    0 if 1 == len(lowercase_		) else next(i for i, s in enumerate(lowercase_   ,1		) if s is child		)		)
							lowerCAmelCase__	:						Tuple  				=	parent
						xpath_tags.reverse()
						xpath_subscripts.reverse()
						return xpath_tags, xpath_subscripts







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				int		):
						lowerCAmelCase__	:						str  				=	BeautifulSoup(lowercase_   ,'''html.parser'''		)

						lowerCAmelCase__	:						List[str]  				=	[]
						lowerCAmelCase__	:						Union[str, Any]  				=	[]
						lowerCAmelCase__	:						Optional[int]  				=	[]

						for element in html_code.descendants:
							if type(lowercase_		) == bsa.element.NavigableString:
								if type(element.parent		) != bsa.element.Tag:
									continue

								lowerCAmelCase__	:						str  				=	html.unescape(lowercase_		).strip()
								if not text_in_this_tag:
									continue

								all_doc_strings.append(lowercase_		)

								lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.xpath_soup(lowercase_		)
								stringaxtag_seq.append(lowercase_		)
								stringaxsubs_seq.append(lowercase_		)

						if len(lowercase_		) != len(lowercase_		):
							raise ValueError('''Number of doc strings and xtags does not correspond'''		)
						if len(lowercase_		) != len(lowercase_		):
							raise ValueError('''Number of doc strings and xsubs does not correspond'''		)

						return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				int   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						List[Any]  				=	''''''
						for tagname, subs in zip(lowercase_   ,lowercase_		):
							xpath += F'/{tagname}'
							if subs != 0:
								xpath += F'[{subs}]'
						return xpath







					def __call__(       self			:				int   ,lowercase_			:				Optional[int]		):
						lowerCAmelCase__	:						Optional[int]  				=	False

						# Check that strings has a valid type
						if isinstance(lowercase_   ,lowercase_		):
							lowerCAmelCase__	:						Dict  				=	True
						elif isinstance(lowercase_   ,(list, tuple)		):
							if len(lowercase_		) == 0 or isinstance(html_strings[0]   ,lowercase_		):
								lowerCAmelCase__	:						Optional[int]  				=	True

						if not valid_strings:
							raise ValueError(
							    '''HTML strings must of type `str`, `List[str]` (batch of examples), '''
							    F'but is of type {type(lowercase_		)}.'		)

						lowerCAmelCase__	:						Dict  				=	bool(isinstance(lowercase_   ,(list, tuple)		) and (isinstance(html_strings[0]   ,lowercase_		))		)

						if not is_batched:
							lowerCAmelCase__	:						Dict  				=	[html_strings]

						# Get nodes + xpaths
						lowerCAmelCase__	:						Optional[int]  				=	[]
						lowerCAmelCase__	:						Union[str, Any]  				=	[]
						for html_string in html_strings:
							lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	self.get_three_from_single(lowercase_		)
							nodes.append(lowercase_		)
							lowerCAmelCase__	:						List[str]  				=	[]
							for node, tag_list, sub_list in zip(lowercase_   ,lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						str  				=	self.construct_xpath(lowercase_   ,lowercase_		)
								xpath_strings.append(lowercase_		)
							xpaths.append(lowercase_		)

						# return as Dict
						lowerCAmelCase__	:						str  				=	{'''nodes''': nodes, '''xpaths''': xpaths}
						lowerCAmelCase__	:						Dict  				=	BatchFeature(data=lowercase_   ,tensor_type=lowercase_		)

						return encoded_inputs







"""simple docstring"""





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCamelCase  :      List[str]							      =		'''true'''

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=16 ):
	set_seed(42 )
	lowerCAmelCase__	:						Union[str, Any]  				=	RegressionModel()
	lowerCAmelCase__	:						Optional[int]  				=	deepcopy(A_ )
	lowerCAmelCase__	:						Any  				=	RegressionDataset(length=A_ )
	lowerCAmelCase__	:						List[str]  				=	DataLoader(A_						,     batch_size=A_ )
	model.to(accelerator.device )
	lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	accelerator.prepare(A_						,     A_ )
	return model, ddp_model, dataloader

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						List[str]  				=	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
	lowerCAmelCase__	:						List[str]  				=	load_dataset('''glue'''						,     '''mrpc'''						,     split='''validation''' )

	def tokenize_function(A_ ):
		lowerCAmelCase__	:						Optional[Any]  				=	tokenizer(examples['''sentence1''']						,     examples['''sentence2''']						,     truncation=A_						,     max_length=A_ )
		return outputs

	with accelerator.main_process_first():
		lowerCAmelCase__	:						Dict  				=	dataset.map(
		    A_						,     batched=A_						,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,     )

	lowerCAmelCase__	:						int  				=	tokenized_datasets.rename_column('''label'''						,     '''labels''' )

	def collate_fn(A_ ):
		if use_longest:
			return tokenizer.pad(A_						,     padding='''longest'''						,     return_tensors='''pt''' )
		return tokenizer.pad(A_						,     padding='''max_length'''						,     max_length=1_28						,     return_tensors='''pt''' )

	return DataLoader(A_						,     shuffle=A_						,     collate_fn=A_						,     batch_size=16 )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	Accelerator(dispatch_batches=A_						,     split_batches=A_ )
	lowerCAmelCase__	:						str  				=	get_dataloader(A_						,     not dispatch_batches )
	lowerCAmelCase__	:						Optional[int]  				=	AutoModelForSequenceClassification.from_pretrained(
	    '''hf-internal-testing/mrpc-bert-base-cased'''						,     return_dict=A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	accelerator.prepare(A_						,     A_ )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	[]
	for batch in dataloader:
		lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	batch.values()
		with torch.no_grad():
			lowerCAmelCase__	:						List[str]  				=	model(A_ )
			lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	accelerator.gather_for_metrics((logit, target) )
			logits_and_targets.append((logit, target) )
	lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	[], []
	for logit, targ in logits_and_targets:
		logits.append(A_ )
		targs.append(A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	torch.cat(A_ ), torch.cat(A_ )
	return logits, targs

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=False						,     A_=False						,     A_=16 ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	get_basic_setup(A_						,     A_						,     A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	generate_predictions(A_						,     A_						,     A_ )
	assert (
	    len(A_ ) == num_samples
	), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_ )}'

def   __SCREAMING_SNAKE_CASE  (      A_ = False						,     A_ = False ):
	lowerCAmelCase__	:						Optional[Any]  				=	evaluate.load('''glue'''						,     '''mrpc''' )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	get_mrpc_setup(A_						,     A_ )
	# First do baseline
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	setup['''no''']
	model.to(A_ )
	model.eval()
	for batch in dataloader:
		batch.to(A_ )
		with torch.inference_mode():
			lowerCAmelCase__	:						Optional[int]  				=	model(**A_ )
		lowerCAmelCase__	:						Dict  				=	outputs.logits.argmax(dim=-1 )
		metric.add_batch(predictions=A_						,     references=batch['''labels'''] )
	lowerCAmelCase__	:						Dict  				=	metric.compute()

	# Then do distributed
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	setup['''ddp''']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			lowerCAmelCase__	:						Union[str, Any]  				=	model(**A_ )
		lowerCAmelCase__	:						int  				=	outputs.logits.argmax(dim=-1 )
		lowerCAmelCase__	:						int  				=	batch['''labels''']
		lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	accelerator.gather_for_metrics((preds, references) )
		metric.add_batch(predictions=A_						,     references=A_ )
	lowerCAmelCase__	:						List[Any]  				=	metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]						,     distributed[key] ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('''**Testing gather_for_metrics**''' )
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`' )
				test_mrpc(A_						,     A_ )
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test torch metrics**''' )
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			lowerCAmelCase__	:						Optional[Any]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
			if accelerator.is_local_main_process:
				print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
			test_torch_metrics(A_						,     99 )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test last batch is not dropped when perfectly divisible**''' )
	lowerCAmelCase__	:						List[str]  				=	Accelerator()
	test_torch_metrics(A_						,     5_12 )
	accelerator.state._reset_state()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()




