

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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: int  		=       "laion/clap-htsat-unfused"
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       tempfile.mkdtemp()



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						**lowerCAmelCase__  :     Optional[Any]):
									return RobertaTokenizer.from_pretrained(self.checkpoint   ,						**lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple   ,						**lowerCAmelCase__  :     List[Any]):
									return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,						**lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									shutil.rmtree(self.tmpdirname)



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_tokenizer()
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.get_feature_extractor()

									SCREAMING_SNAKE_CASE_: Tuple  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

									processor.save_pretrained(self.tmpdirname)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       ClapProcessor.from_pretrained(self.tmpdirname)

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

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: int  		=       ClapProcessor(tokenizer=self.get_tokenizer()   ,						feature_extractor=self.get_feature_extractor())
									processor.save_pretrained(self.tmpdirname)

									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_tokenizer(bos_token="(BOS)"   ,						eos_token="(EOS)")
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_feature_extractor(do_normalize=lowerCAmelCase__   ,						padding_value=1.0)

									SCREAMING_SNAKE_CASE_: Tuple  		=       ClapProcessor.from_pretrained(
									    self.tmpdirname   ,						bos_token="(BOS)"   ,						eos_token="(EOS)"   ,						do_normalize=lowerCAmelCase__   ,						padding_value=1.0)

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

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



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_feature_extractor()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_tokenizer()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       floats_list((3, 1000))

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       feature_extractor(lowerCAmelCase__   ,						return_tensors="np")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       processor(audios=lowerCAmelCase__   ,						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        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_feature_extractor()
									SCREAMING_SNAKE_CASE_: int  		=       self.get_tokenizer()

									SCREAMING_SNAKE_CASE_: Tuple  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       "This is a test string"

									SCREAMING_SNAKE_CASE_: int  		=       processor(text=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       tokenizer(lowerCAmelCase__)

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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_feature_extractor()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_tokenizer()

									SCREAMING_SNAKE_CASE_: str  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       processor.batch_decode(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       tokenizer.batch_decode(lowerCAmelCase__)

									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: str  		=       self.get_feature_extractor()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.get_tokenizer()

									SCREAMING_SNAKE_CASE_: Dict  		=       ClapProcessor(tokenizer=lowerCAmelCase__   ,						feature_extractor=lowerCAmelCase__)

									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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase							:  Any   	=						TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  int   	=						[
						    """MT5EncoderModel""",
						    """MT5ForConditionalGeneration""",
						    """MT5ForQuestionAnswering""",
						    """MT5Model""",
						    """MT5PreTrainedModel""",
						    """MT5Stack""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Tuple   	=						["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase							:  Optional[Any]   	=						_LazyModule(
						    __name__,
						    globals()["""__file__"""],
						    _import_structure,
						    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
						    module_spec=__spec__,
						)






