





'''simple docstring'''




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__UpperCAmelCase			    =    logging.get_logger(__name__)  # pylint: disable=invalid-name



def 						__A						(   lowerCamelCase_     ):


		"""simple docstring"""



		if not path:
				return "pipe"

		for ext in PipelineDataFormat.SUPPORTED_FORMATS:
				if path.endswith(lowerCamelCase_     ):
						return ext

		raise Exception(
		    f'''Unable to determine file format from file extension {path}. '''
		    f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''     )



def 						__A						(   lowerCamelCase_     ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE      :							List[str]						=       pipeline(
		    task=args.task       ,       model=args.model if args.model else None       ,       config=args.config       ,       tokenizer=args.tokenizer       ,       device=args.device       ,       )
		SCREAMING_SNAKE_CASE      :							Optional[int]						=       try_infer_format_from_ext(args.input     ) if args.format == """infer""" else args.format
		SCREAMING_SNAKE_CASE      :							Dict						=       PipelineDataFormat.from_str(
		    format=lowerCamelCase_       ,       output_path=args.output       ,       input_path=args.input       ,       column=args.column if args.column else nlp.default_input_names       ,       overwrite=args.overwrite       ,       )
		return RunCommand(lowerCamelCase_       ,       lowerCamelCase_     )




class 			UpperCamelCase__ ( _a       ):




					"""simple docstring"""


					def __init__(		self							:       List[Any]	,		lowerCamelCase_							:       Pipeline	,		lowerCamelCase_							:       PipelineDataFormat		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							str						=       nlp
							SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       reader


					@staticmethod
					def lowerCamelCase_       (		lowerCamelCase_							:       ArgumentParser		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       parser.add_parser("""run"""	,		help="""Run a pipeline through the CLI"""		)
							run_parser.add_argument("""--task"""	,		choices=get_supported_tasks()	,		help="""Task to run"""		)
							run_parser.add_argument("""--input"""	,		type=__lowerCamelCase	,		help="""Path to the file to use for inference"""		)
							run_parser.add_argument("""--output"""	,		type=__lowerCamelCase	,		help="""Path to the file that will be used post to write results."""		)
							run_parser.add_argument("""--model"""	,		type=__lowerCamelCase	,		help="""Name or path to the model to instantiate."""		)
							run_parser.add_argument("""--config"""	,		type=__lowerCamelCase	,		help="""Name or path to the model's config to instantiate."""		)
							run_parser.add_argument(
							    """--tokenizer"""	,		type=__lowerCamelCase	,		help="""Name of the tokenizer to use. (default: same as the model name)"""		)
							run_parser.add_argument(
							    """--column"""	,		type=__lowerCamelCase	,		help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""	,		)
							run_parser.add_argument(
							    """--format"""	,		type=__lowerCamelCase	,		default="""infer"""	,		choices=PipelineDataFormat.SUPPORTED_FORMATS	,		help="""Input format to read from"""	,		)
							run_parser.add_argument(
							    """--device"""	,		type=__lowerCamelCase	,		default=-1	,		help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""	,		)
							run_parser.add_argument("""--overwrite"""	,		action="""store_true"""	,		help="""Allow overwriting the output file."""		)
							run_parser.set_defaults(func=__lowerCamelCase		)



					def lowerCamelCase_       (		self							:       Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE      :							Union[str, Any]						=       self._nlp, []

							for entry in self._reader:
									SCREAMING_SNAKE_CASE      :							Any						=       nlp(**__lowerCamelCase		) if self._reader.is_multi_columns else nlp(__lowerCamelCase		)
									if isinstance(__lowerCamelCase	,		__lowerCamelCase		):
											outputs.append(__lowerCamelCase		)
									else:
											outputs += output

        # Saving data
							if self._nlp.binary_output:
									SCREAMING_SNAKE_CASE      :							List[Any]						=       self._reader.save_binary(__lowerCamelCase		)
									logger.warning(f'''Current pipeline requires output to be in binary format, saving at {binary_path}'''		)
							else:
									self._reader.save(__lowerCamelCase		)











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
