

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  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											a__				      =		"""laion/clap-htsat-unfused"""
											a__				      =		tempfile.mkdtemp()


							def _a      (							self  ,  **a_		):
											return RobertaTokenizer.from_pretrained(self.checkpoint  ,  **a_		)


							def _a      (							self  ,  **a_		):
											return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,  **a_		)


							def _a      (							self		):
											shutil.rmtree(self.tmpdirname		)


							def _a      (							self		):
											a__				      =		self.get_tokenizer()
											a__				      =		self.get_feature_extractor()

											a__				      =		ClapProcessor(tokenizer=a_  ,  feature_extractor=a_		)

											processor.save_pretrained(self.tmpdirname		)
											a__				      =		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 _a      (							self		):
											a__				      =		ClapProcessor(tokenizer=self.get_tokenizer()  ,  feature_extractor=self.get_feature_extractor()		)
											processor.save_pretrained(self.tmpdirname		)

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

											a__				      =		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 _a      (							self		):
											a__				      =		self.get_feature_extractor()
											a__				      =		self.get_tokenizer()

											a__				      =		ClapProcessor(tokenizer=a_  ,  feature_extractor=a_		)

											a__				      =		floats_list((3, 1_000)		)

											a__				      =		feature_extractor(a_  ,  return_tensors="""np"""		)
											a__				      =		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 _a      (							self		):
											a__				      =		self.get_feature_extractor()
											a__				      =		self.get_tokenizer()

											a__				      =		ClapProcessor(tokenizer=a_  ,  feature_extractor=a_		)

											a__				      =		"""This is a test string"""

											a__				      =		processor(text=a_		)

											a__				      =		tokenizer(a_		)

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


							def _a      (							self		):
											a__				      =		self.get_feature_extractor()
											a__				      =		self.get_tokenizer()

											a__				      =		ClapProcessor(tokenizer=a_  ,  feature_extractor=a_		)

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

											a__				      =		processor.batch_decode(a_		)
											a__				      =		tokenizer.batch_decode(a_		)

											self.assertListEqual(a_  ,  a_		)







							def _a      (							self		):
											a__				      =		self.get_feature_extractor()
											a__				      =		self.get_tokenizer()

											a__				      =		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_torch_available,
)


UpperCAmelCase							 =					{
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FalconForCausalLM""",
	    """FalconModel""",
	    """FalconPreTrainedModel""",
	    """FalconForSequenceClassification""",
	    """FalconForTokenClassification""",
	    """FalconForQuestionAnswering""",
	]


if TYPE_CHECKING:
	from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_falcon import (
		    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FalconForCausalLM,
		    FalconForQuestionAnswering,
		    FalconForSequenceClassification,
		    FalconForTokenClassification,
		    FalconModel,
		    FalconPreTrainedModel,
		)


else:
	import sys

	UpperCAmelCase							 =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

