
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 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       def     _UpperCamelCase	(							self	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	=      '''<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(''' '''	)
          SCREAMING_SNAKE_CASE_	=      dict(zip(_A ,     range(len(_A	)	)	)	)

          SCREAMING_SNAKE_CASE_	=      {
              '''pad_token''': '''<pad>''',
              '''unk_token''': '''<unk>''',
              '''bos_token''': '''<s>''',
              '''eos_token''': '''</s>''',
          }
          SCREAMING_SNAKE_CASE_	=      {
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 16000,
              '''return_attention_mask''': False,
              '''do_normalize''': True,
          }

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

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



       def     _UpperCamelCase	(							self ,     **_A	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.add_kwargs_tokens_map.copy()
          kwargs.update(_A	)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,     **_A	)



       def     _UpperCamelCase	(							self ,     **_A	)				->				Optional[int]:
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,     **_A	)



       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          shutil.rmtree(self.tmpdirname	)



       def     _UpperCamelCase	(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()
          SCREAMING_SNAKE_CASE_	=      self.get_feature_extractor()

          SCREAMING_SNAKE_CASE_	=      WavaVecaProcessor(tokenizer=_A ,     feature_extractor=_A	)

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

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

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



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          SCREAMING_SNAKE_CASE_	=      WavaVecaProcessor(tokenizer=self.get_tokenizer() ,     feature_extractor=self.get_feature_extractor()	)
          processor.save_pretrained(self.tmpdirname	)

          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer(bos_token='''(BOS)''' ,     eos_token='''(EOS)'''	)
          SCREAMING_SNAKE_CASE_	=      self.get_feature_extractor(do_normalize=_A ,     padding_value=1.0	)

          SCREAMING_SNAKE_CASE_	=      WavaVecaProcessor.from_pretrained(
              self.tmpdirname ,     bos_token='''(BOS)''' ,     eos_token='''(EOS)''' ,     do_normalize=_A ,     padding_value=1.0	)

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

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



       def     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.get_feature_extractor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      WavaVecaProcessor(tokenizer=_A ,     feature_extractor=_A	)

          SCREAMING_SNAKE_CASE_	=      floats_list((3, 1000)	)

          SCREAMING_SNAKE_CASE_	=      feature_extractor(_A ,     return_tensors='''np'''	)
          SCREAMING_SNAKE_CASE_	=      processor(_A ,     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     _UpperCamelCase	(							self	)				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      self.get_feature_extractor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      WavaVecaProcessor(tokenizer=_A ,     feature_extractor=_A	)

          SCREAMING_SNAKE_CASE_	=      '''This is a test string'''

          SCREAMING_SNAKE_CASE_	=      processor(text=_A	)

          SCREAMING_SNAKE_CASE_	=      tokenizer(_A	)

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



       def     _UpperCamelCase	(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.get_feature_extractor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      WavaVecaProcessor(tokenizer=_A ,     feature_extractor=_A	)

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

          SCREAMING_SNAKE_CASE_	=      processor.batch_decode(_A	)
          SCREAMING_SNAKE_CASE_	=      tokenizer.batch_decode(_A	)

          self.assertListEqual(_A ,     _A	)




       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      self.get_feature_extractor()
          SCREAMING_SNAKE_CASE_	=      self.get_tokenizer()

          SCREAMING_SNAKE_CASE_	=      WavaVecaProcessor(tokenizer=_A ,     feature_extractor=_A	)

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





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

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


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       UpperCamelCase__	:   Optional[int]								=     None


UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)


UpperCamelCase__	:   Any								=     "▁"

UpperCamelCase__	:   Any								=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__	:   List[Any]								=     {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

UpperCamelCase__	:   Optional[Any]								=     {
    "google/reformer-crime-and-punishment": 524_288,
}



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						int    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						int    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						Tuple    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						List[str]    			=	['input_ids', 'attention_mask']
  __lowerCAmelCase  :						Dict    			=	ReformerTokenizer

  def __init__(       self    ,		_A=None    ,		_A=None    ,		_A="</s>"    ,		_A="<unk>"    ,		_A=[]    ,		**_A    ,		):
      super().__init__(
          _A    ,		tokenizer_file=_A    ,		eos_token=_A    ,		unk_token=_A    ,		additional_special_tokens=_A    ,		**_A    ,		)

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


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None):
      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(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_						=    os.path.join(
          _A    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

      return (out_vocab_file,)


