'''simple docstring'''





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list




@require_torchaudio
@require_sentencepiece
class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   "laion/clap-htsat-unfused"
         UpperCAmelCase_           =   tempfile.mkdtemp()



    def 					_lowercase						(self    :    Dict   ,			**__a    :    List[Any] ):
         return RobertaTokenizer.from_pretrained(self.checkpoint   ,			**__a )



    def 					_lowercase						(self    :    str   ,			**__a    :    Union[str, Any] ):
         return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,			**__a )



    def 					_lowercase						(self    :    str ):
         shutil.rmtree(self.tmpdirname )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.get_tokenizer()
         UpperCAmelCase_           =   self.get_feature_extractor()

         UpperCAmelCase_           =   ClapProcessor(tokenizer=__a   ,			feature_extractor=__a )

         processor.save_pretrained(self.tmpdirname )
         UpperCAmelCase_           =   ClapProcessor.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 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   ClapProcessor(tokenizer=self.get_tokenizer()   ,			feature_extractor=self.get_feature_extractor() )
         processor.save_pretrained(self.tmpdirname )

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

         UpperCAmelCase_           =   ClapProcessor.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 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.get_feature_extractor()
         UpperCAmelCase_           =   self.get_tokenizer()

         UpperCAmelCase_           =   ClapProcessor(tokenizer=__a   ,			feature_extractor=__a )

         UpperCAmelCase_           =   floats_list((3, 1000) )

         UpperCAmelCase_           =   feature_extractor(__a   ,			return_tensors="np" )
         UpperCAmelCase_           =   processor(audios=__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 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.get_feature_extractor()
         UpperCAmelCase_           =   self.get_tokenizer()

         UpperCAmelCase_           =   ClapProcessor(tokenizer=__a   ,			feature_extractor=__a )

         UpperCAmelCase_           =   "This is a test string"

         UpperCAmelCase_           =   processor(text=__a )

         UpperCAmelCase_           =   tokenizer(__a )

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



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.get_feature_extractor()
         UpperCAmelCase_           =   self.get_tokenizer()

         UpperCAmelCase_           =   ClapProcessor(tokenizer=__a   ,			feature_extractor=__a )

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

         UpperCAmelCase_           =   processor.batch_decode(__a )
         UpperCAmelCase_           =   tokenizer.batch_decode(__a )

         self.assertListEqual(__a   ,			__a )






    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.get_feature_extractor()
         UpperCAmelCase_           =   self.get_tokenizer()

         UpperCAmelCase_           =   ClapProcessor(tokenizer=__a   ,			feature_extractor=__a )

         self.assertListEqual(
             processor.model_input_names[2:]   ,			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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	str    = {
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = ['BlenderbotTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = [
					    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BlenderbotForCausalLM',
					    'BlenderbotForConditionalGeneration',
					    'BlenderbotModel',
					    'BlenderbotPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'TFBlenderbotForConditionalGeneration',
					    'TFBlenderbotModel',
					    'TFBlenderbotPreTrainedModel',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'FlaxBlenderbotForConditionalGeneration',
					    'FlaxBlenderbotModel',
					    'FlaxBlenderbotPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_blenderbot import (
					    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BlenderbotConfig,
					    BlenderbotOnnxConfig,
					)
					from .tokenization_blenderbot import BlenderbotTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blenderbot import (
										    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlenderbotForCausalLM,
										    BlenderbotForConditionalGeneration,
										    BlenderbotModel,
										    BlenderbotPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blenderbot import (
										    TFBlenderbotForConditionalGeneration,
										    TFBlenderbotModel,
										    TFBlenderbotPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_blenderbot import (
										    FlaxBlenderbotForConditionalGeneration,
										    FlaxBlenderbotModel,
										    FlaxBlenderbotPreTrainedModel,
										)

else:
					import sys

					A     :	List[Any]    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)