

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class 						__magic_name__					(							lowercase_				):
     UpperCamelCase_	:Dict										=      """EncodecFeatureExtractor"""
     UpperCamelCase_	:Optional[int]										=      ("""T5Tokenizer""", """T5TokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(lowerCamelCase_		,						lowerCamelCase_	)
         UpperCamelCase_			  =						self.feature_extractor
         UpperCamelCase_			  =						False






     def 	UpperCAmelCase_				(				self		,						_lowercase=None		,						_lowercase=None		,						_lowercase=True	)->     int:
         return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase_		,						language=lowerCamelCase_		,						no_timestamps=lowerCamelCase_	)






     def __call__(				self		,						*_lowercase		,						**_lowercase	)->     Optional[int]:



         if self._in_target_context_manager:
             return self.current_processor(*lowerCamelCase_		,						**lowerCamelCase_	)

         UpperCamelCase_			  =						kwargs.pop("audio"		,						lowerCamelCase_	)
         UpperCamelCase_			  =						kwargs.pop("sampling_rate"		,						lowerCamelCase_	)
         UpperCamelCase_			  =						kwargs.pop("text"		,						lowerCamelCase_	)
         if len(lowerCamelCase_	) > 0:
             UpperCamelCase_			  =						args[0]
             UpperCamelCase_			  =						args[1:]

         if audio is None and text is None:
             raise ValueError("You need to specify either an `audio` or `text` input to process."	)

         if text is not None:
             UpperCamelCase_			  =						self.tokenizer(lowerCamelCase_		,						**lowerCamelCase_	)

         if audio is not None:
             UpperCamelCase_			  =						self.feature_extractor(lowerCamelCase_		,						*lowerCamelCase_		,						sampling_rate=lowerCamelCase_		,						**lowerCamelCase_	)

         if audio is None:
             return inputs

         elif text is None:
             return audio_inputs

         else:
             UpperCamelCase_			  =						audio_inputs["""input_values"""]
             if "padding_mask" in audio_inputs:
                 UpperCamelCase_			  =						audio_inputs["""padding_mask"""]
             return inputs






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Dict:
         UpperCamelCase_			  =						kwargs.pop("audio"		,						lowerCamelCase_	)
         UpperCamelCase_			  =						kwargs.pop("padding_mask"		,						lowerCamelCase_	)

         if len(lowerCamelCase_	) > 0:
             UpperCamelCase_			  =						args[0]
             UpperCamelCase_			  =						args[1:]

         if audio_values is not None:
             return self._decode_audio(lowerCamelCase_		,						padding_mask=lowerCamelCase_	)
         else:
             return self.tokenizer.batch_decode(*lowerCamelCase_		,						**lowerCamelCase_	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.decode(*lowerCamelCase_		,						**lowerCamelCase_	)






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Any:
         UpperCamelCase_			  =						to_numpy(lowerCamelCase_	)
         UpperCamelCase_			  =						audio_values.shape

         if padding_mask is None:
             return list(lowerCamelCase_	)

         UpperCamelCase_			  =						to_numpy(lowerCamelCase_	)

         # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
         # token (so that the generated audio values are **not** treated as padded tokens)
         UpperCamelCase_			  =						seq_len - padding_mask.shape[-1]
         UpperCamelCase_			  =						1 - self.feature_extractor.padding_value
         UpperCamelCase_			  =						np.pad(lowerCamelCase_		,						((0, 0), (0, difference))		,						"constant"		,						constant_values=lowerCamelCase_	)

         UpperCamelCase_			  =						audio_values.tolist()
         for i in range(lowerCamelCase_	):
             UpperCamelCase_			  =						np.asarray(audio_values[i]	)[
                 padding_mask[i][None, :] != self.feature_extractor.padding_value
             ]
             UpperCamelCase_			  =						sliced_audio.reshape(lowerCamelCase_		,						-1	)

         return audio_values







'''simple docstring'''




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      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			parent



					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							return {}







def 		__A		(  ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				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>"""

		SCREAMING_SNAKE_CASE :				int		=			"""
    <!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      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	MarkupLMFeatureExtractor if is_bsa_available() else None


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			MarkupLMFeatureExtractionTester(self						)


					@property
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							return self.feature_extract_tester.prepare_feat_extract_dict()



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.feature_extraction_class()

							# Test not batched input
							SCREAMING_SNAKE_CASE :				Optional[int]		=			get_html_strings()[0]
							SCREAMING_SNAKE_CASE :				List[Any]		=			feature_extractor(lowerCamelCase_						)

							# fmt: off
							SCREAMING_SNAKE_CASE :				Optional[int]		=			[["""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"""]]
							SCREAMING_SNAKE_CASE :				List[str]		=			[["""/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      ,						lowerCamelCase_						)
							self.assertEqual(encoding.xpaths      ,						lowerCamelCase_						)

							# Test batched
							SCREAMING_SNAKE_CASE :				str		=			get_html_strings()
							SCREAMING_SNAKE_CASE :				str		=			feature_extractor(lowerCamelCase_						)

							# fmt: off
							SCREAMING_SNAKE_CASE :				int		=			expected_nodes + [["""My First Heading""", """My first paragraph."""]]
							SCREAMING_SNAKE_CASE :				List[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      ,						lowerCamelCase_						)
							self.assertEqual(encoding.xpaths      ,						lowerCamelCase_						)



