

from typing import TYPE_CHECKING

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


_lowerCamelCase       :     Any           =							{"configuration_bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig", "BitOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase       :     str           =							[
         "BIT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BitForImageClassification",
         "BitModel",
         "BitPreTrainedModel",
         "BitBackbone",
     ]


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase       :     List[Any]           =							["BitImageProcessor"]


if TYPE_CHECKING:
     from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_bit import (
              BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BitBackbone,
              BitForImageClassification,
              BitModel,
              BitPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_bit import BitImageProcessor

else:
     import sys

     _lowerCamelCase       :     List[str]           =							_LazyModule(__name__, globals()["__file__"], _import_structure)








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  __magic_name__					(							self							:  Dict		):
     UpperCAmelCase :							Tuple								=				'''<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(''' '''		)
     UpperCAmelCase :							List[str]								=				dict(zip(__A,	range(len(__A		)		)		)		)

     UpperCAmelCase :							int								=				{
         '''pad_token''': '''<pad>''',
         '''unk_token''': '''<unk>''',
         '''bos_token''': '''<s>''',
         '''eos_token''': '''</s>''',
     }
     UpperCAmelCase :							str								=				{
         '''feature_size''': 1,
         '''padding_value''': 0.0,
         '''sampling_rate''': 1_6_0_0_0,
         '''return_attention_mask''': False,
         '''do_normalize''': True,
     }

     UpperCAmelCase :							List[Any]								=				tempfile.mkdtemp()
     UpperCAmelCase :							str								=				os.path.join(self.tmpdirname,	VOCAB_FILES_NAMES['''vocab_file''']		)
     UpperCAmelCase :							Tuple								=				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  __magic_name__					(							self							:  List[Any],	**__A							:  List[Any]		):
     UpperCAmelCase :							str								=				self.add_kwargs_tokens_map.copy()
     kwargs.update(__A		)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,	**__A		)



   def  __magic_name__					(							self							:  List[Any],	**__A							:  Union[str, Any]		):
     return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname,	**__A		)



   def  __magic_name__					(							self							:  Any		):
     shutil.rmtree(self.tmpdirname		)



   def  __magic_name__					(							self							:  str		):
     UpperCAmelCase :							Any								=				self.get_tokenizer()
     UpperCAmelCase :							Optional[int]								=				self.get_feature_extractor()

     UpperCAmelCase :							Tuple								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     processor.save_pretrained(self.tmpdirname		)
     UpperCAmelCase :							Union[str, Any]								=				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  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							int								=				WavaVecaProcessor(tokenizer=self.get_tokenizer(),	feature_extractor=self.get_feature_extractor()		)
     processor.save_pretrained(self.tmpdirname		)

     UpperCAmelCase :							Dict								=				self.get_tokenizer(bos_token='''(BOS)''',	eos_token='''(EOS)'''		)
     UpperCAmelCase :							int								=				self.get_feature_extractor(do_normalize=__A,	padding_value=1.0		)

     UpperCAmelCase :							int								=				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  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Optional[Any]								=				self.get_feature_extractor()
     UpperCAmelCase :							Optional[int]								=				self.get_tokenizer()

     UpperCAmelCase :							Any								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							Tuple								=				floats_list((3, 1_0_0_0)		)

     UpperCAmelCase :							Any								=				feature_extractor(__A,	return_tensors='''np'''		)
     UpperCAmelCase :							int								=				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  __magic_name__					(							self							:  Union[str, Any]		):
     UpperCAmelCase :							Optional[Any]								=				self.get_feature_extractor()
     UpperCAmelCase :							Union[str, Any]								=				self.get_tokenizer()

     UpperCAmelCase :							List[Any]								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							List[str]								=				'''This is a test string'''

     UpperCAmelCase :							Tuple								=				processor(text=__A		)

     UpperCAmelCase :							Tuple								=				tokenizer(__A		)

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



   def  __magic_name__					(							self							:  Optional[int]		):
     UpperCAmelCase :							Tuple								=				self.get_feature_extractor()
     UpperCAmelCase :							int								=				self.get_tokenizer()

     UpperCAmelCase :							List[str]								=				WavaVecaProcessor(tokenizer=__A,	feature_extractor=__A		)

     UpperCAmelCase :							Any								=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     UpperCAmelCase :							str								=				processor.batch_decode(__A		)
     UpperCAmelCase :							List[str]								=				tokenizer.batch_decode(__A		)

     self.assertListEqual(__A,	__A		)





   def  __magic_name__					(							self							:  int		):
     UpperCAmelCase :							Optional[int]								=				self.get_feature_extractor()
     UpperCAmelCase :							List[str]								=				self.get_tokenizer()

     UpperCAmelCase :							Any								=				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''',	)





