


"""simple docstring"""


import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class     _UpperCamelCase    (  lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 =PhobertTokenizer
							__UpperCAmelCase		:       int							 =False

							def      snake_case							(	self      ):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										__lowerCAmelCase							       =     ["T@@", "i", "I", "R@@", "r", "e@@"]
										__lowerCAmelCase							       =     dict(zip(__a       ,  range(len(__a      )      )      )      )
										__lowerCAmelCase							       =     ["#version: 0.2", "l à</w>"]
										__lowerCAmelCase							       =     {"unk_token": "<unk>"}

										__lowerCAmelCase							       =     os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["vocab_file"]      )
										__lowerCAmelCase							       =     os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["merges_file"]      )

										with open(self.vocab_file       ,  "w"       ,  encoding="utf-8"      ) as fp:
													for token in vocab_tokens:
																fp.write(f"{token} {vocab_tokens[token]}\n"      )
										with open(self.merges_file       ,  "w"       ,  encoding="utf-8"      ) as fp:
													fp.write("\n".join(__a      )      )

							def      snake_case							(	self       ,  **__a      ):
										kwargs.update(self.special_tokens_map      )
										return PhobertTokenizer.from_pretrained(self.tmpdirname       ,  **__a      )

							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     "Tôi là VinAI Research"
										__lowerCAmelCase							       =     "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
										return input_text, output_text



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     PhobertTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map      )
										__lowerCAmelCase							       =     "Tôi là VinAI Research"
										__lowerCAmelCase							       =     "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
										__lowerCAmelCase							       =     tokenizer.tokenize(__a      )
										print(__a      )
										self.assertListEqual(__a       ,  __a      )

										__lowerCAmelCase							       =     tokens + [tokenizer.unk_token]

										__lowerCAmelCase							       =     [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__a      )       ,  __a      )








"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 ="""Speech2TextFeatureExtractor"""
							__UpperCAmelCase		:       Union[str, Any]							 ="""Speech2TextTokenizer"""

							def __init__(	self       ,  __a       ,  __a      ):
										super().__init__(__a       ,  __a      )
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False

							def __call__(	self       ,  *__a       ,  **__a      ):
										# For backward compatibility
										if self._in_target_context_manager:
													return self.current_processor(*__a       ,  **__a      )

										if "raw_speech" in kwargs:
													warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."      )
													__lowerCAmelCase							       =     kwargs.pop("raw_speech"      )
										else:
													__lowerCAmelCase							       =     kwargs.pop("audio"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("sampling_rate"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("text"       ,  __a      )
										if len(__a      ) > 0:
													__lowerCAmelCase							       =     args[0]
													__lowerCAmelCase							       =     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:
													__lowerCAmelCase							       =     self.feature_extractor(__a       ,  *__a       ,  sampling_rate=__a       ,  **__a      )
										if text is not None:
													__lowerCAmelCase							       =     self.tokenizer(__a       ,  **__a      )

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

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.batch_decode(*__a       ,  **__a      )

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.decode(*__a       ,  **__a      )



							@contextmanager
							def      snake_case							(	self      ):
										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."      )
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     self.tokenizer
										yield
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False




