






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :int          =	filter(lambda __magic_name__					: p.requires_grad	,			model.parameters()					)
	UpperCamelCase     :List[str]          =	sum([np.prod(p.size()					) for p in model_parameters]					)
	return params


UpperCAmelCase_ :						str      =      logging.getLogger(__name__)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Dict					)		->			Dict:






	"""simple docstring"""



	if metric == "rouge2":
		UpperCamelCase     :Tuple          =	"""{val_avg_rouge2:.4f}-{step_count}"""
	elif metric == "bleu":
		UpperCamelCase     :List[Any]          =	"""{val_avg_bleu:.4f}-{step_count}"""
	elif metric == "em":
		UpperCamelCase     :Tuple          =	"""{val_avg_em:.4f}-{step_count}"""
	elif metric == "loss":
		UpperCamelCase     :Dict          =	"""{val_avg_loss:.4f}-{step_count}"""
	else:
		raise NotImplementedError(
		    f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
		    """ function."""					)

	UpperCamelCase     :List[str]          =	ModelCheckpoint(
	    dirpath=__magic_name__	,			filename=__magic_name__	,			monitor=f"""val_{metric}"""	,			mode="""max"""	,			save_top_k=1	,			every_n_epochs=1	,			)

	return checkpoint_callback

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str					)		->			Optional[int]:






	"""simple docstring"""



	return EarlyStopping(
	    monitor=f"""val_{metric}"""	,			mode="""min""" if """loss""" in metric else """max"""	,			patience=__magic_name__	,			verbose=__magic_name__	,			)

class     _SCREAMING_SNAKE_CASE       (			pl.Callback      ):





	def 						_A (		self				:					str ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Any	):
		UpperCamelCase     :Optional[int]          =	{F"""lr_group_{i}""": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups	)}
		pl_module.logger.log_metrics(__lowerCamelCase	)





	@rank_zero_only
	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					pl.Trainer ,  __lowerCamelCase				:					pl.LightningModule ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					List[str]=True	):
		logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""	)
		UpperCamelCase     :str          =	trainer.callback_metrics
		trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}	)
		# Log results
		UpperCamelCase     :Union[str, Any]          =	Path(pl_module.hparams.output_dir	)
		if type_path == "test":
			UpperCamelCase     :Dict          =	od / """test_results.txt"""
			UpperCamelCase     :str          =	od / """test_generations.txt"""
		else:
			# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
			# If people want this it will be easy enough to add back.
			UpperCamelCase     :Any          =	od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
			UpperCamelCase     :List[str]          =	od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
			results_file.parent.mkdir(exist_ok=__lowerCamelCase	)
			generations_file.parent.mkdir(exist_ok=__lowerCamelCase	)
		with open(__lowerCamelCase ,  """a+"""	) as writer:
			for key in sorted(__lowerCamelCase	):
				if key in ["log", "progress_bar", "preds"]:
					continue
				UpperCamelCase     :int          =	metrics[key]
				if isinstance(__lowerCamelCase ,  torch.Tensor	):
					UpperCamelCase     :Any          =	val.item()
				UpperCamelCase     :Union[str, Any]          =	F"""{key}: {val:.6f}\n"""
				writer.write(__lowerCamelCase	)

		if not save_generations:
			return

		if "preds" in metrics:
			UpperCamelCase     :Any          =	"""\n""".join(metrics["""preds"""]	)
			generations_file.open("""w+"""	).write(__lowerCamelCase	)





	@rank_zero_only
	def 						_A (		self				:					str ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					str	):
		try:
			UpperCamelCase     :Union[str, Any]          =	pl_module.model.model.num_parameters()
		except AttributeError:
			UpperCamelCase     :int          =	pl_module.model.num_parameters()

		UpperCamelCase     :Union[str, Any]          =	count_trainable_parameters(__lowerCamelCase	)
		# mp stands for million parameters
		trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}	)





	@rank_zero_only
	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					pl.Trainer ,  __lowerCamelCase				:					pl.LightningModule	):
		save_json(pl_module.metrics ,  pl_module.metrics_save_path	)
		return self._write_logs(__lowerCamelCase ,  __lowerCamelCase ,  """test"""	)





	@rank_zero_only
	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					pl.Trainer ,  __lowerCamelCase				:					Union[str, Any]	):
		save_json(pl_module.metrics ,  pl_module.metrics_save_path	)
		# Uncommenting this will save val generations
		# return self._write_logs(trainer, pl_module, "valid")








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_ :						Any      =      logging.get_logger(__name__)

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Optional[int] ,  **__lowerCamelCase				:					Optional[int]	):
		requires_backends(self ,  ["""bs4"""]	)
		super().__init__(**__lowerCamelCase	)





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





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Tuple	):
		UpperCamelCase     :Any          =	BeautifulSoup(__lowerCamelCase ,  """html.parser"""	)

		UpperCamelCase     :Union[str, Any]          =	[]
		UpperCamelCase     :Tuple          =	[]
		UpperCamelCase     :Tuple          =	[]

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

				UpperCamelCase     :Any          =	html.unescape(__lowerCamelCase	).strip()
				if not text_in_this_tag:
					continue

				all_doc_strings.append(__lowerCamelCase	)

				UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	self.xpath_soup(__lowerCamelCase	)
				stringaxtag_seq.append(__lowerCamelCase	)
				stringaxsubs_seq.append(__lowerCamelCase	)

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

		return all_doc_strings, stringaxtag_seq, stringaxsubs_seq





	def 						_A (		self				:					int ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :Tuple          =	""""""
		for tagname, subs in zip(__lowerCamelCase ,  __lowerCamelCase	):
			xpath += F"""/{tagname}"""
			if subs != 0:
				xpath += F"""[{subs}]"""
		return xpath





	def __call__(		self				:					Any ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Any          =	False

		# Check that strings has a valid type
		if isinstance(__lowerCamelCase ,  __lowerCamelCase	):
			UpperCamelCase     :List[Any]          =	True
		elif isinstance(__lowerCamelCase ,  (list, tuple)	):
			if len(__lowerCamelCase	) == 0 or isinstance(html_strings[0] ,  __lowerCamelCase	):
				UpperCamelCase     :Any          =	True

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

		UpperCamelCase     :str          =	bool(isinstance(__lowerCamelCase ,  (list, tuple)	) and (isinstance(html_strings[0] ,  __lowerCamelCase	))	)

		if not is_batched:
			UpperCamelCase     :Any          =	[html_strings]

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

		# return as Dict
		UpperCamelCase     :Optional[int]          =	{"""nodes""": nodes, """xpaths""": xpaths}
		UpperCamelCase     :Any          =	BatchFeature(data=__lowerCamelCase ,  tensor_type=__lowerCamelCase	)

		return encoded_inputs
