






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       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      Dict )							->				List[str]:
        __UpperCAmelCase							:     Union[str, Any]									=  """<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							:     Any									=  dict(zip(__lowercase		,				range(len(__lowercase ) ) ) )

        __UpperCAmelCase							:     int									=  {
            """pad_token""": """<pad>""",
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        __UpperCAmelCase							:     Tuple									=  {
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 16000,
            """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							:     Optional[Any]									=  os.path.join(self.tmpdirname		,				__lowercase )
        with open(self.vocab_file		,				"""w"""		,				encoding="""utf-8""" ) as fp:
          fp.write(json.dumps(__lowercase ) + """\n""" )

        with open(self.feature_extraction_file		,				"""w"""		,				encoding="""utf-8""" ) as fp:
          fp.write(json.dumps(__lowercase ) + """\n""" )
      def      UpperCAmelCase				(				self :      Tuple		,				**__lowercase :      List[Any] )							->				List[Any]:
        __UpperCAmelCase							:     Optional[int]									=  self.add_kwargs_tokens_map.copy()
        kwargs.update(__lowercase )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,				**__lowercase )
      def      UpperCAmelCase				(				self :      List[str]		,				**__lowercase :      Tuple )							->				Union[str, Any]:
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,				**__lowercase )
      def      UpperCAmelCase				(				self :      Optional[int] )							->				Optional[Any]:
        shutil.rmtree(self.tmpdirname )
      def      UpperCAmelCase				(				self :      List[str] )							->				Any:
        __UpperCAmelCase							:     int									=  self.get_tokenizer()
        __UpperCAmelCase							:     str									=  self.get_feature_extractor()

        __UpperCAmelCase							:     Union[str, Any]									=  WavaVecaProcessor(tokenizer=__lowercase		,				feature_extractor=__lowercase )

        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		,				__lowercase )

        self.assertEqual(processor.feature_extractor.to_json_string()		,				feature_extractor.to_json_string() )
        self.assertIsInstance(processor.feature_extractor		,				__lowercase )
      def      UpperCAmelCase				(				self :      int )							->				List[Any]:
        __UpperCAmelCase							:     List[str]									=  WavaVecaProcessor(tokenizer=self.get_tokenizer()		,				feature_extractor=self.get_feature_extractor() )
        processor.save_pretrained(self.tmpdirname )

        __UpperCAmelCase							:     Any									=  self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)""" )
        __UpperCAmelCase							:     Tuple									=  self.get_feature_extractor(do_normalize=__lowercase		,				padding_value=1.0 )

        __UpperCAmelCase							:     str									=  WavaVecaProcessor.from_pretrained(
            self.tmpdirname		,				bos_token="""(BOS)"""		,				eos_token="""(EOS)"""		,				do_normalize=__lowercase		,				padding_value=1.0 )

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

        self.assertEqual(processor.feature_extractor.to_json_string()		,				feature_extractor_add_kwargs.to_json_string() )
        self.assertIsInstance(processor.feature_extractor		,				__lowercase )
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				List[str]:
        __UpperCAmelCase							:     Tuple									=  self.get_feature_extractor()
        __UpperCAmelCase							:     List[Any]									=  self.get_tokenizer()

        __UpperCAmelCase							:     Optional[Any]									=  WavaVecaProcessor(tokenizer=__lowercase		,				feature_extractor=__lowercase )

        __UpperCAmelCase							:     List[str]									=  floats_list((3, 1000) )

        __UpperCAmelCase							:     int									=  feature_extractor(__lowercase		,				return_tensors="""np""" )
        __UpperCAmelCase							:     str									=  processor(__lowercase		,				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 :      Any )							->				Dict:
        __UpperCAmelCase							:     Optional[Any]									=  self.get_feature_extractor()
        __UpperCAmelCase							:     int									=  self.get_tokenizer()

        __UpperCAmelCase							:     List[Any]									=  WavaVecaProcessor(tokenizer=__lowercase		,				feature_extractor=__lowercase )

        __UpperCAmelCase							:     Tuple									=  """This is a test string"""

        __UpperCAmelCase							:     List[str]									=  processor(text=__lowercase )

        __UpperCAmelCase							:     int									=  tokenizer(__lowercase )

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,				encoded_processor[key] )
      def      UpperCAmelCase				(				self :      Dict )							->				Optional[int]:
        __UpperCAmelCase							:     List[str]									=  self.get_feature_extractor()
        __UpperCAmelCase							:     Tuple									=  self.get_tokenizer()

        __UpperCAmelCase							:     Optional[int]									=  WavaVecaProcessor(tokenizer=__lowercase		,				feature_extractor=__lowercase )

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

        __UpperCAmelCase							:     Dict									=  processor.batch_decode(__lowercase )
        __UpperCAmelCase							:     Any									=  tokenizer.batch_decode(__lowercase )

        self.assertListEqual(__lowercase		,				__lowercase )




      def      UpperCAmelCase				(				self :      Optional[Any] )							->				str:
        __UpperCAmelCase							:     List[Any]									=  self.get_feature_extractor()
        __UpperCAmelCase							:     Optional[Any]									=  self.get_tokenizer()

        __UpperCAmelCase							:     Optional[Any]									=  WavaVecaProcessor(tokenizer=__lowercase		,				feature_extractor=__lowercase )

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










from typing import TYPE_CHECKING

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


a		:	Any      					=			{
    "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:	Optional[int]      					=			["DetaImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:	Tuple      					=			[
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_deta import DetaImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )

else:
       import sys

       a		:	Any      					=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


