



"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"Wav2Vec2FeatureExtractor"
							__lowerCAmelCase    					=					"AutoTokenizer"






							def __init__(   self			,     a_			,     a_      ):
									super().__init__(a_			,     a_      )
									a_     :      str 						=     self.feature_extractor
									a_     :      Optional[Any] 						=     False






							@classmethod
							def snake_case_  (   cls			,     a_			,     **a_      ):
									try:
											return super().from_pretrained(a_			,     **a_      )
									except OSError:
											warnings.warn(
											    F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
											    " include a `tokenizer_class` attribute is deprecated and will be "
											    "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
											    " attribute to either your `config.json` or `tokenizer_config.json` "
											    "file to suppress this warning: "			,     a_			,     )

											a_     :      Union[str, Any] 						=     WavaVecaFeatureExtractor.from_pretrained(a_			,     **a_      )
											a_     :      List[Any] 						=     WavaVecaCTCTokenizer.from_pretrained(a_			,     **a_      )

											return cls(feature_extractor=a_			,     tokenizer=a_      )






							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."      )
											a_     :      Optional[Any] 						=     kwargs.pop("raw_speech"      )
									else:
											a_     :      List[str] 						=     kwargs.pop("audio"			,     a_      )
									a_     :      Optional[Any] 						=     kwargs.pop("sampling_rate"			,     a_      )
									a_     :      str 						=     kwargs.pop("text"			,     a_      )
									if len(a_      ) > 0:
											a_     :      str 						=     args[0]
											a_     :      Any 						=     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:
											a_     :      Any 						=     self.feature_extractor(a_			,     *a_			,     sampling_rate=a_			,     **a_      )
									if text is not None:
											a_     :      Optional[int] 						=     self.tokenizer(a_			,     **a_      )

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






							def snake_case_  (   self			,     *a_			,     **a_      ):
									# For backward compatibility
									if self._in_target_context_manager:
											return self.current_processor.pad(*a_			,     **a_      )

									a_     :      Dict 						=     kwargs.pop("input_features"			,     a_      )
									a_     :      List[Any] 						=     kwargs.pop("labels"			,     a_      )
									if len(a_      ) > 0:
											a_     :      Tuple 						=     args[0]
											a_     :      str 						=     args[1:]

									if input_features is not None:
											a_     :      Any 						=     self.feature_extractor.pad(a_			,     *a_			,     **a_      )
									if labels is not None:
											a_     :      List[str] 						=     self.tokenizer.pad(a_			,     **a_      )

									if labels is None:
											return input_features
									elif input_features is None:
											return labels
									else:
											a_     :      List[Any] 						=     labels["input_ids"]
											return input_features






							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."      )
									a_     :      List[Any] 						=     True
									a_     :      List[Any] 						=     self.tokenizer
									yield
									a_     :      Union[str, Any] 						=     self.feature_extractor
									a_     :      List[Any] 						=     False




"""simple docstring"""
import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")




@require_sentencepiece
@require_tokenizers
class 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					GPTSwaTokenizer
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					True
							__lowerCAmelCase    					=					False






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

									# We have a SentencePiece fixture for testing
									a_     :      Optional[int] 						=     GPTSwaTokenizer(a_			,     eos_token="<unk>"			,     bos_token="<unk>"			,     pad_token="<unk>"      )

									tokenizer.save_pretrained(self.tmpdirname      )






							def snake_case_  (   self			,     a_      ):
									a_     :      Union[str, Any] 						=     "This is a test"
									a_     :      Tuple 						=     "This is a test"
									return input_text, output_text






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     "<s>"
									a_     :      Any 						=     1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_      )			,     a_      )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_      )			,     a_      )






							def snake_case_  (   self      ):
									a_     :      int 						=     list(self.get_tokenizer().get_vocab().keys()      )

									self.assertEqual(vocab_keys[0]			,     "<unk>"      )
									self.assertEqual(vocab_keys[1]			,     "<s>"      )
									self.assertEqual(vocab_keys[-1]			,     "j"      )
									self.assertEqual(len(a_      )			,     2_0_0_0      )






							def snake_case_  (   self      ):
									self.assertEqual(self.get_tokenizer().vocab_size			,     2_0_0_0      )






							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     GPTSwaTokenizer(a_      )

									a_     :      str 						=     tokenizer.tokenize("This is a test"      )
									self.assertListEqual(a_			,     ["▁This", "▁is", "▁a", "▁t", "est"]      )

									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_      )			,     [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]      )

									a_     :      str 						=     tokenizer.tokenize("I was born in 92000, and this is falsé."      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]			,     )
									# fmt: on

									a_     :      List[str] 						=     tokenizer.convert_tokens_to_ids(a_      )
									self.assertListEqual(
									    a_			,     [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]			,     )

									a_     :      Optional[int] 						=     tokenizer.convert_ids_to_tokens(a_      )
									# fmt: off
									self.assertListEqual(
									    a_			,     ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."]      )
									# fmt: on






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     GPTSwaTokenizer(a_      )
									a_     :      List[Any] 						=     ["This is a test", "I was born in 92000, and this is falsé."]
									a_     :      Optional[Any] 						=     [
									    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
									    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(a_			,     a_      ):
											self.assertListEqual(tokenizer.encode_fast(a_      )			,     a_      )

									# Test that decode_fast returns the input text
									for text, token_ids in zip(a_			,     a_      ):
											self.assertEqual(tokenizer.decode_fast(a_      )			,     a_      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Dict 						=     [
									    "<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')",
									    "Hey there, how are you doing this fine day?",
									    "This is a text with a trailing spaces followed by a dot     .",
									    "Häj sväjs lillebrör! =)",
									    "Det är inget fel på Mr. Cool",
									]

									# fmt: off
									a_     :      Union[str, Any] 						=     {"input_ids": [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=a_			,     model_name="AI-Sweden/gpt-sw3-126m"			,     sequences=a_			,     )