






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__				     =			{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__				     =			['''MvpTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__				     =			[
							    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MvpForCausalLM''',
							    '''MvpForConditionalGeneration''',
							    '''MvpForQuestionAnswering''',
							    '''MvpForSequenceClassification''',
							    '''MvpModel''',
							    '''MvpPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
							from .tokenization_mvp import MvpTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mvp_fast import MvpTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mvp import (
														    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MvpForCausalLM,
														    MvpForConditionalGeneration,
														    MvpForQuestionAnswering,
														    MvpForSequenceClassification,
														    MvpModel,
														    MvpPreTrainedModel,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__				     =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










"""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      ):







			"""simple docstring"""



			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    'laion/clap-htsat-unfused'
						_lowerCamelCase					:					Optional[Any]	       =    tempfile.mkdtemp()
			def    _lowerCAmelCase		(	self				,   **A							):
						return RobertaTokenizer.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self				,   **A							):
						return ClapFeatureExtractor.from_pretrained(self.checkpoint				,   **A							)
			def    _lowerCAmelCase		(	self							):
						shutil.rmtree(self.tmpdirname							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()

						_lowerCamelCase					:					List[str]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						processor.save_pretrained(self.tmpdirname							)
						_lowerCamelCase					:					str	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    ClapProcessor(tokenizer=self.get_tokenizer()				,   feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						_lowerCamelCase					:					Tuple	       =    self.get_tokenizer(bos_token='(BOS)'				,   eos_token='(EOS)'							)
						_lowerCamelCase					:					str	       =    self.get_feature_extractor(do_normalize=A				,   padding_value=1.0							)

						_lowerCamelCase					:					List[Any]	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[int]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Any	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					int	       =    floats_list((3, 1000)							)

						_lowerCamelCase					:					str	       =    feature_extractor(A				,   return_tensors='np'							)
						_lowerCamelCase					:					List[str]	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    self.get_feature_extractor()
						_lowerCamelCase					:					str	       =    self.get_tokenizer()

						_lowerCamelCase					:					List[Any]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

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

						_lowerCamelCase					:					Any	       =    processor(text=A							)

						_lowerCamelCase					:					Optional[int]	       =    tokenizer(A							)

						for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key]				,   encoded_processor[key]							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					Optional[int]	       =    ClapProcessor(tokenizer=A				,   feature_extractor=A							)

						_lowerCamelCase					:					Dict	       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						_lowerCamelCase					:					int	       =    processor.batch_decode(A							)
						_lowerCamelCase					:					int	       =    tokenizer.batch_decode(A							)

						self.assertListEqual(A				,   A							)

			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    self.get_feature_extractor()
						_lowerCamelCase					:					Optional[Any]	       =    self.get_tokenizer()

						_lowerCamelCase					:					int	       =    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'				,   )





