





'''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 				UpperCAmelCase  (    unittest.TestCase     ):




     def        lowercase__					(    self    :				List[Any]						)       ->   List[str]:
           _lowerCAmelCase							  =							"""<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(""" """						)
           _lowerCAmelCase							  =							dict(zip(__snake_case    ,    range(len(__snake_case						)						)						)						)

           _lowerCAmelCase							  =							{
               """pad_token""": """<pad>""",
               """unk_token""": """<unk>""",
               """bos_token""": """<s>""",
               """eos_token""": """</s>""",
           }
           _lowerCAmelCase							  =							{
               """feature_size""": 1,
               """padding_value""": 0.0,
               """sampling_rate""": 1_60_00,
               """return_attention_mask""": False,
               """do_normalize""": True,
           }

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

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




     def        lowercase__					(    self    :				Optional[Any]    ,    **__snake_case    :				Dict						)       ->   Dict:
           _lowerCAmelCase							  =							self.add_kwargs_tokens_map.copy()
           kwargs.update(__snake_case						)
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,    **__snake_case						)




     def        lowercase__					(    self    :				int    ,    **__snake_case    :				int						)       ->   Tuple:
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,    **__snake_case						)




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




     def        lowercase__					(    self    :				int						)       ->   Dict:
           _lowerCAmelCase							  =							self.get_tokenizer()
           _lowerCAmelCase							  =							self.get_feature_extractor()

           _lowerCAmelCase							  =							WavaVecaProcessor(tokenizer=__snake_case    ,    feature_extractor=__snake_case						)

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

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

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




     def        lowercase__					(    self    :				Any						)       ->   str:
           _lowerCAmelCase							  =							WavaVecaProcessor(tokenizer=self.get_tokenizer()    ,    feature_extractor=self.get_feature_extractor()						)
           processor.save_pretrained(self.tmpdirname						)

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

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

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

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




     def        lowercase__					(    self    :				Union[str, Any]						)       ->   int:
           _lowerCAmelCase							  =							self.get_feature_extractor()
           _lowerCAmelCase							  =							self.get_tokenizer()

           _lowerCAmelCase							  =							WavaVecaProcessor(tokenizer=__snake_case    ,    feature_extractor=__snake_case						)

           _lowerCAmelCase							  =							floats_list((3, 10_00)						)

           _lowerCAmelCase							  =							feature_extractor(__snake_case    ,    return_tensors="""np"""						)
           _lowerCAmelCase							  =							processor(__snake_case    ,    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        lowercase__					(    self    :				List[str]						)       ->   Optional[int]:
           _lowerCAmelCase							  =							self.get_feature_extractor()
           _lowerCAmelCase							  =							self.get_tokenizer()

           _lowerCAmelCase							  =							WavaVecaProcessor(tokenizer=__snake_case    ,    feature_extractor=__snake_case						)

           _lowerCAmelCase							  =							"""This is a test string"""

           _lowerCAmelCase							  =							processor(text=__snake_case						)

           _lowerCAmelCase							  =							tokenizer(__snake_case						)

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




     def        lowercase__					(    self    :				Optional[Any]						)       ->   Tuple:
           _lowerCAmelCase							  =							self.get_feature_extractor()
           _lowerCAmelCase							  =							self.get_tokenizer()

           _lowerCAmelCase							  =							WavaVecaProcessor(tokenizer=__snake_case    ,    feature_extractor=__snake_case						)

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

           _lowerCAmelCase							  =							processor.batch_decode(__snake_case						)
           _lowerCAmelCase							  =							tokenizer.batch_decode(__snake_case						)

           self.assertListEqual(__snake_case    ,    __snake_case						)




     def        lowercase__					(    self    :				List[str]						)       ->   List[str]:
           _lowerCAmelCase							  =							self.get_feature_extractor()
           _lowerCAmelCase							  =							self.get_tokenizer()

           _lowerCAmelCase							  =							WavaVecaProcessor(tokenizer=__snake_case    ,    feature_extractor=__snake_case						)

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













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__       :     Tuple          ={
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[str]          =['''OneFormerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Dict          =[
          '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''OneFormerForUniversalSegmentation''',
          '''OneFormerModel''',
          '''OneFormerPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      A__       :     Optional[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure)






