
import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
				from transformers import MarkupLMFeatureExtractor




class 				_lowerCamelCase (      unittest.TestCase ):





					"""simple docstring"""







					def __init__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   str:




											"""simple docstring"""







											UpperCamelCase__    :   int					=				parent






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




											"""simple docstring"""







											return {}


def 							SCREAMING_SNAKE_CASE_      (			):
						UpperCamelCase__    :   Union[str, Any]					=				'''<HTML>

    <HEAD>
    <TITLE>sample document</TITLE>
    </HEAD>

    <BODY BGCOLOR="FFFFFF">
    <HR>
    <a href="http://google.com">Goog</a>
    <H1>This is one header</H1>
    <H2>This is a another Header</H2>
    <P>Travel from
        <P>
        <B>SFO to JFK</B>
        <BR>
        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
        <HR>
        <div style="color:#0000FF">
            <h3>Traveler <b> name </b> is
            <p> John Doe </p>
        </div>'''

						UpperCamelCase__    :   Optional[Any]					=				'''
    <!DOCTYPE html>
    <html>
    <body>

    <h1>My First Heading</h1>
    <p>My first paragraph.</p>

    </body>
    </html>
    '''

						return [html_string_a, html_string_a]




@require_bsa
class 				_lowerCamelCase (      _UpperCAmelCase	,							unittest.TestCase ):





					"""simple docstring"""







					SCREAMING_SNAKE_CASE_		   =  MarkupLMFeatureExtractor if is_bsa_available() else None






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




											"""simple docstring"""







											UpperCamelCase__    :   Optional[int]					=				MarkupLMFeatureExtractionTester(self  )






					@property
					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




											"""simple docstring"""







											return self.feature_extract_tester.prepare_feat_extract_dict()






					def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




											"""simple docstring"""







											UpperCamelCase__    :   int					=				self.feature_extraction_class()

											# Test not batched input
											UpperCamelCase__    :   str					=				get_html_strings()[0]
											UpperCamelCase__    :   Tuple					=				feature_extractor(A_  )

											# fmt: off
											UpperCamelCase__    :   Optional[Any]					=				[['''sample document''', '''Goog''', '''This is one header''', '''This is a another Header''', '''Travel from''', '''SFO to JFK''', '''on May 2, 2015 at 2:00 pm. For details go to confirm.com''', '''Traveler''', '''name''', '''is''', '''John Doe''']]
											UpperCamelCase__    :   Tuple					=				[['''/html/head/title''', '''/html/body/a''', '''/html/body/h1''', '''/html/body/h2''', '''/html/body/p''', '''/html/body/p/p/b[1]''', '''/html/body/p/p/b[2]/i''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/b''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/p''']]
											# fmt: on

											self.assertEqual(encoding.nodes						,						A_  )
											self.assertEqual(encoding.xpaths						,						A_  )

											# Test batched
											UpperCamelCase__    :   int					=				get_html_strings()
											UpperCamelCase__    :   Optional[Any]					=				feature_extractor(A_  )

											# fmt: off
											UpperCamelCase__    :   str					=				expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
											UpperCamelCase__    :   Optional[Any]					=				expected_xpaths + [['''/html/body/h1''', '''/html/body/p''']]

											self.assertEqual(len(encoding.nodes  )						,						2  )
											self.assertEqual(len(encoding.xpaths  )						,						2  )

											self.assertEqual(encoding.nodes						,						A_  )
											self.assertEqual(encoding.xpaths						,						A_  )




from argparse import ArgumentParser

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


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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(UpperCamelCase__		):
                  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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      UpperCamelCase__    :   Any					=				try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
      UpperCamelCase__    :   Tuple					=				PipelineDataFormat.from_str(
          format=UpperCamelCase__ ,		output_path=args.output ,		input_path=args.input ,		column=args.column if args.column else nlp.default_input_names ,		overwrite=args.overwrite ,		)
      return RunCommand(UpperCamelCase__ ,		UpperCamelCase__		)




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[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=__SCREAMING_SNAKE_CASE						,						help='''Path to the file to use for inference'''  )
           run_parser.add_argument('''--output'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file that will be used post to write results.'''  )
           run_parser.add_argument('''--model'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model to instantiate.'''  )
           run_parser.add_argument('''--config'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model\'s config to instantiate.'''  )
           run_parser.add_argument(
               '''--tokenizer'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the tokenizer to use. (default: same as the model name)'''  )
           run_parser.add_argument(
               '''--column'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''						,						)
           run_parser.add_argument(
               '''--format'''						,						type=__SCREAMING_SNAKE_CASE						,						default='''infer'''						,						choices=PipelineDataFormat.SUPPORTED_FORMATS						,						help='''Input format to read from'''						,						)
           run_parser.add_argument(
               '''--device'''						,						type=__SCREAMING_SNAKE_CASE						,						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=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self._nlp, []

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				nlp(**__SCREAMING_SNAKE_CASE  ) if self._reader.is_multi_columns else nlp(__SCREAMING_SNAKE_CASE  )
                 if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                       outputs.append(__SCREAMING_SNAKE_CASE  )
                 else:
                       outputs += output

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


