
'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_xglm import XGLMTokenizer
else:
				__A       =      None


__A       =      logging.get_logger(__name__)

__A       =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A       =      {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

__A       =      {
    '''facebook/xglm-564M''': 2_048,
}





class      A							(    __lowerCAmelCase			):
			lowerCamelCase							:							int    						=					VOCAB_FILES_NAMES
			lowerCamelCase							:							Optional[int]    						=					PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase							:							Optional[int]    						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase							:							Dict    						=					['''input_ids''', '''attention_mask''']
			lowerCamelCase							:							Any    						=					XGLMTokenizer



			def __init__(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=None    ,       lowerCamelCase__="<s>"    ,       lowerCamelCase__="</s>"    ,       lowerCamelCase__="</s>"    ,       lowerCamelCase__="<s>"    ,       lowerCamelCase__="<unk>"    ,       lowerCamelCase__="<pad>"    ,       **lowerCamelCase__    ,       )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    7
									lowercase__								=    [F'''<madeupword{i}>''' for i in range(self.num_madeup_words    )]

									lowercase__								=    kwargs.get("""additional_special_tokens"""    ,       []    )
									kwargs["additional_special_tokens"] += [
									    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    __SCREAMING_SNAKE_CASE    ,       tokenizer_file=__SCREAMING_SNAKE_CASE    ,       bos_token=__SCREAMING_SNAKE_CASE    ,       eos_token=__SCREAMING_SNAKE_CASE    ,       sep_token=__SCREAMING_SNAKE_CASE    ,       cls_token=__SCREAMING_SNAKE_CASE    ,       unk_token=__SCREAMING_SNAKE_CASE    ,       pad_token=__SCREAMING_SNAKE_CASE    ,       **__SCREAMING_SNAKE_CASE    ,       )

									lowercase__								=    vocab_file
									lowercase__								=    False if not self.vocab_file else True



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__ = None    )     ->						List[int]:




									'''simple docstring'''



									if token_ids_a is None:
															return [self.sep_token_id] + token_ids_a
									lowercase__								=    [self.sep_token_id]
									return sep + token_ids_a + sep + sep + token_ids_a



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__ = None    )     ->						List[int]:




									'''simple docstring'''



									lowercase__								=    [self.sep_token_id]

									if token_ids_a is None:
															return len(sep + token_ids_a    ) * [0]
									return len(sep + token_ids_a + sep + sep + token_ids_a    ) * [0]



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__ = None    )     ->						Tuple[str]:




									'''simple docstring'''



									if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""    )

									if not os.path.isdir(__SCREAMING_SNAKE_CASE    ):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''    )
															return
									lowercase__								=    os.path.join(
									    __SCREAMING_SNAKE_CASE    ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

									if os.path.abspath(self.vocab_file    ) != os.path.abspath(__SCREAMING_SNAKE_CASE    ):
															copyfile(self.vocab_file    ,       __SCREAMING_SNAKE_CASE    )

									return (out_vocab_file,)





'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class      A							(    unittest.TestCase			):



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    """<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """    )
									lowercase__								=    dict(zip(lowerCamelCase__    ,       range(len(lowerCamelCase__    )    )    )    )

									lowercase__								=    {
									    """pad_token""": """<pad>""",
									    """unk_token""": """<unk>""",
									    """bos_token""": """<s>""",
									    """eos_token""": """</s>""",
									}
									lowercase__								=    {
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16_000,
									    """return_attention_mask""": False,
									    """do_normalize""": True,
									}

									lowercase__								=    tempfile.mkdtemp()
									lowercase__								=    os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
									lowercase__								=    os.path.join(self.tmpdirname    ,       lowerCamelCase__    )
									with open(self.vocab_file    ,       """w"""    ,       encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(lowerCamelCase__    ) + """\n"""    )

									with open(self.feature_extraction_file    ,       """w"""    ,       encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(lowerCamelCase__    ) + """\n"""    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						int:




									'''simple docstring'''



									lowercase__								=    self.add_kwargs_tokens_map.copy()
									kwargs.update(lowerCamelCase__    )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						Dict:




									'''simple docstring'''



									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									shutil.rmtree(self.tmpdirname    )



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__								=    self.get_tokenizer()
									lowercase__								=    self.get_feature_extractor()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									processor.save_pretrained(self.tmpdirname    )
									lowercase__								=    WavaVecaProcessor.from_pretrained(self.tmpdirname    )

									self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer.get_vocab()    )
									self.assertIsInstance(processor.tokenizer    ,       lowerCamelCase__    )

									self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor.to_json_string()    )
									self.assertIsInstance(processor.feature_extractor    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    WavaVecaProcessor(tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor()    )
									processor.save_pretrained(self.tmpdirname    )

									lowercase__								=    self.get_tokenizer(bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    )
									lowercase__								=    self.get_feature_extractor(do_normalize=lowerCamelCase__    ,       padding_value=1.0    )

									lowercase__								=    WavaVecaProcessor.from_pretrained(
									    self.tmpdirname    ,       bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    ,       do_normalize=lowerCamelCase__    ,       padding_value=1.0    )

									self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer_add_kwargs.get_vocab()    )
									self.assertIsInstance(processor.tokenizer    ,       lowerCamelCase__    )

									self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.feature_extractor    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    floats_list((3, 1_000)    )

									lowercase__								=    feature_extractor(lowerCamelCase__    ,       return_tensors="""np"""    )
									lowercase__								=    processor(lowerCamelCase__    ,       return_tensors="""np"""    )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()    ,       input_processor[key].sum()    ,       delta=1e-2    )



			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    """This is a test string"""

									lowercase__								=    processor(text=lowerCamelCase__    )

									lowercase__								=    tokenizer(lowerCamelCase__    )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key]    )



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									lowercase__								=    processor.batch_decode(lowerCamelCase__    )
									lowercase__								=    tokenizer.batch_decode(lowerCamelCase__    )

									self.assertListEqual(lowerCamelCase__    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									self.assertListEqual(
									    processor.model_input_names    ,       feature_extractor.model_input_names    ,       msg="""`processor` and `feature_extractor` model input names do not match"""    ,       )






