


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase					:       Optional[Any]			    =     get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class A__     ( A__				,		unittest.TestCase  ):



  """simple docstring"""



  _lowercase									=		XGLMTokenizer
  _lowercase									=		XGLMTokenizerFast
  _lowercase									=		True
  _lowercase									=		True

  def    _UpperCamelCase( self			:  List[Any]       ):
        super().setUp()

        # We have a SentencePiece fixture for testing
        a__	:  str     =   XGLMTokenizer(lowerCamelCase__				,				keep_accents=lowerCamelCase__       )
        tokenizer.save_pretrained(self.tmpdirname       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  int     =   "<pad>"
        a__	:  Union[str, Any]     =   1

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

  def    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  List[str]     =   list(self.get_tokenizer().get_vocab().keys()       )

        self.assertEqual(vocab_keys[0]				,				"<s>"       )
        self.assertEqual(vocab_keys[1]				,				"<pad>"       )
        self.assertEqual(len(lowerCamelCase__       )				,				1_008       )

  def    _UpperCamelCase( self			:  Dict       ):
        self.assertEqual(self.get_tokenizer().vocab_size				,				1_008       )

  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  str     =   XGLMTokenizer(lowerCamelCase__				,				keep_accents=lowerCamelCase__       )

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

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowerCamelCase__       )				,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,				)

        a__	:  Any     =   tokenizer.tokenize("I was born in 92000, and this is falsé."       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "9",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "é",
                ".",
            ]				,				)
        a__	:  List[str]     =   tokenizer.convert_tokens_to_ids(lowerCamelCase__       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]				,				)

        a__	:  Dict     =   tokenizer.convert_ids_to_tokens(lowerCamelCase__       )
        self.assertListEqual(
            lowerCamelCase__				,				[
                SPIECE_UNDERLINE + "I",
                SPIECE_UNDERLINE + "was",
                SPIECE_UNDERLINE + "b",
                "or",
                "n",
                SPIECE_UNDERLINE + "in",
                SPIECE_UNDERLINE + "",
                "<unk>",
                "2",
                "0",
                "0",
                "0",
                ",",
                SPIECE_UNDERLINE + "and",
                SPIECE_UNDERLINE + "this",
                SPIECE_UNDERLINE + "is",
                SPIECE_UNDERLINE + "f",
                "al",
                "s",
                "<unk>",
                ".",
            ]				,				)

  @cached_property
  def    _UpperCamelCase( self			:  Dict       ):
        return XGLMTokenizer.from_pretrained("facebook/xglm-564M"       )

  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        with tempfile.NamedTemporaryFile() as f:
              shutil.copyfile(lowerCamelCase__				,				f.name       )
              a__	:  Any     =   XGLMTokenizer(f.name				,				keep_accents=lowerCamelCase__       )
              a__	:  List[str]     =   pickle.dumps(lowerCamelCase__       )
        pickle.loads(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[Any]       ):
        if not self.test_rust_tokenizer:
              return

        a__	:  Any     =   self.get_tokenizer()
        a__	:  Optional[Any]     =   self.get_rust_tokenizer()

        a__	:  Tuple     =   "I was born in 92000, and this is falsé."

        a__	:  List[str]     =   tokenizer.tokenize(lowerCamelCase__       )
        a__	:  Union[str, Any]     =   rust_tokenizer.tokenize(lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

        a__	:  Optional[int]     =   tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
        a__	:  Union[str, Any]     =   rust_tokenizer.encode(lowerCamelCase__				,				add_special_tokens=lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

        a__	:  List[str]     =   self.get_rust_tokenizer()
        a__	:  Tuple     =   tokenizer.encode(lowerCamelCase__       )
        a__	:  Optional[Any]     =   rust_tokenizer.encode(lowerCamelCase__       )
        self.assertListEqual(lowerCamelCase__				,				lowerCamelCase__       )

  @slow
  def    _UpperCamelCase( self			:  List[str]       ):
        a__	:  Union[str, Any]     =   "Hello World!"
        a__	:  List[str]     =   [2, 31_227, 4_447, 35]

        self.assertListEqual(lowerCamelCase__				,				self.big_tokenizer.encode(lowerCamelCase__       )       )

  @slow
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  Optional[int]     =   (
            "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
            " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
        )
        # fmt: off
        a__	:  Union[str, Any]     =   [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
        # fmt: on

        self.assertListEqual(lowerCamelCase__				,				self.big_tokenizer.encode(lowerCamelCase__       )       )

  @slow
  def    _UpperCamelCase( self			:  List[Any]       ):
        # fmt: off
        a__	:  Optional[int]     =   {
            "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
            "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, 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, 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, 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]]
        }  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCamelCase__				,				model_name="facebook/xglm-564M"				,				padding=lowerCamelCase__				,				)








'''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      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''Wav2Vec2FeatureExtractor'''
					SCREAMING_SNAKE_CASE__				=	'''AutoTokenizer'''


					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				List[Any]		=			False


					@classmethod
					def lowerCamelCase_			(						cls				:			int      ,						lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Any						):

							'''simple docstring'''



							try:
									return super().from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
							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: """      ,						lowerCamelCase_      ,						)

									SCREAMING_SNAKE_CASE :				Optional[Any]		=			WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Dict		=			WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)

									return cls(feature_extractor=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)


					def __call__(						self				:			List[Any]      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



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

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

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


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						*lowerCamelCase_				:			Tuple      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				str		=			kwargs.pop("""input_features"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""labels"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			args[0]
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			args[1:]

							if input_features is not None:
									SCREAMING_SNAKE_CASE :				Any		=			self.feature_extractor.pad(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							if labels is not None:
									SCREAMING_SNAKE_CASE :				int		=			self.tokenizer.pad(lowerCamelCase_      ,						**lowerCamelCase_						)

							if labels is None:
									return input_features
							elif input_features is None:
									return labels
							else:
									SCREAMING_SNAKE_CASE :				Dict		=			labels["""input_ids"""]
									return input_features


					def lowerCamelCase_			(						self				:			Optional[int]      ,						*lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[str]      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



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



					@contextmanager
					def lowerCamelCase_			(						self				:			Tuple						):

							'''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."""						)
							SCREAMING_SNAKE_CASE :				int		=			True
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer
							yield
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			False



