




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""Speech2TextFeatureExtractor"""
						UpperCamelCase     						=					"""Speech2TextTokenizer"""

						def __init__(		self   :Any    , __snake_case   :int    , __snake_case   :Any ):
										'''simple docstring'''

										super().__init__(__snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=self.feature_extractor
										__magic_name__	:	Any								=False

						def __call__(		self   :Tuple    , *__snake_case   :List[str]    , **__snake_case   :List[str] ):
										'''simple docstring'''

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

										if "raw_speech" in kwargs:
														warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
														__magic_name__	:	Tuple								=kwargs.pop("""raw_speech""" )
										else:
														__magic_name__	:	List[Any]								=kwargs.pop("""audio"""    , __snake_case )
										__magic_name__	:	Union[str, Any]								=kwargs.pop("""sampling_rate"""    , __snake_case )
										__magic_name__	:	Dict								=kwargs.pop("""text"""    , __snake_case )
										if len(__snake_case ) > 0:
														__magic_name__	:	Union[str, Any]								=args[0]
														__magic_name__	:	int								=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 audio is not None:
														__magic_name__	:	List[str]								=self.feature_extractor(__snake_case    , *__snake_case    , sampling_rate=__snake_case    , **__snake_case )
										if text is not None:
														__magic_name__	:	Any								=self.tokenizer(__snake_case    , **__snake_case )

										if text is None:
														return inputs
										elif audio is None:
														return encodings
										else:
														__magic_name__	:	List[str]								=encodings["""input_ids"""]
														return inputs

						def     A__						(		self   :List[Any]    , *__snake_case   :str    , **__snake_case   :List[str] ):
										'''simple docstring'''

										return self.tokenizer.batch_decode(*__snake_case    , **__snake_case )

						def     A__						(		self   :int    , *__snake_case   :int    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										return self.tokenizer.decode(*__snake_case    , **__snake_case )


						@contextmanager
						def     A__						(		self   :int ):
										'''simple docstring'''

										warnings.warn(
										    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
										    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
										    """your audio inputs, or in a separate call.""" )
										__magic_name__	:	int								=True
										__magic_name__	:	List[Any]								=self.tokenizer
										yield
										__magic_name__	:	List[Any]								=self.feature_extractor
										__magic_name__	:	str								=False










import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					BlenderbotSmallTokenizer
						UpperCamelCase     						=					False

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										super().setUp()

										__magic_name__	:	Tuple								=["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
										__magic_name__	:	Optional[Any]								=dict(zip(__snake_case    , range(len(__snake_case ) ) ) )

										__magic_name__	:	List[Any]								=["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
										__magic_name__	:	int								={"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

										__magic_name__	:	Dict								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""vocab_file"""] )
										__magic_name__	:	int								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""merges_file"""] )
										with open(self.vocab_file    , """w"""    , encoding="""utf-8""" ) as fp:
														fp.write(json.dumps(__snake_case ) + """\n""" )
										with open(self.merges_file    , """w"""    , encoding="""utf-8""" ) as fp:
														fp.write("""\n""".join(__snake_case ) )

						def     A__						(		self   :Union[str, Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										kwargs.update(self.special_tokens_map )
										return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								="""adapt act apte"""
										__magic_name__	:	str								="""adapt act apte"""
										return input_text, output_text

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=BlenderbotSmallTokenizer(self.vocab_file    , self.merges_file    , **self.special_tokens_map )
										__magic_name__	:	List[str]								="""adapt act apte"""
										__magic_name__	:	Optional[int]								=["""adapt""", """act""", """ap@@""", """te"""]
										__magic_name__	:	List[Any]								=tokenizer.tokenize(__snake_case )
										self.assertListEqual(__snake_case    , __snake_case )

										__magic_name__	:	int								=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

										__magic_name__	:	Any								=[0, 1, 2, 3, 4, 5]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case )    , __snake_case )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Any								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										assert tok("""sam""" ).input_ids == [13_84]
										__magic_name__	:	Tuple								="""I am a small frog."""
										__magic_name__	:	Tuple								=tok([src_text]    , padding=__snake_case    , truncation=__snake_case )["""input_ids"""]
										__magic_name__	:	Optional[Any]								=tok.batch_decode(__snake_case    , skip_special_tokens=__snake_case    , clean_up_tokenization_spaces=__snake_case )[0]
										assert src_text != decoded  # I wish it did!
										assert decoded == "i am a small frog ."


						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										__magic_name__	:	int								="""I am a small frog ."""
										__magic_name__	:	List[Any]								="""."""
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]

										assert encoded[-1] == encoded_dot[0]




