






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        __UpperCamelCase    (     unittest.TestCase   ):







	def  _a   (  self      :							List[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  """laion/clap-htsat-unfused"""
		__lowercase             =  tempfile.mkdtemp()







	def  _a   (  self      :							Any       ,				**_lowerCAmelCase      :							Any			)		-> Any:
		"""simple docstring"""







		return RobertaTokenizer.from_pretrained(self.checkpoint       ,				**_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]       ,				**_lowerCAmelCase      :							Union[str, Any]			)		-> int:
		"""simple docstring"""







		return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,				**_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







		shutil.rmtree(self.tmpdirname			)







	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  self.get_tokenizer()
		__lowercase             =  self.get_feature_extractor()

		__lowercase             =  ClapProcessor(tokenizer=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase			)

		processor.save_pretrained(self.tmpdirname			)
		__lowercase             =  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  _a   (  self      :							int			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  ClapProcessor(tokenizer=self.get_tokenizer()       ,				feature_extractor=self.get_feature_extractor()			)
		processor.save_pretrained(self.tmpdirname			)

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

		__lowercase             =  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  _a   (  self      :							Optional[int]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.get_feature_extractor()
		__lowercase             =  self.get_tokenizer()

		__lowercase             =  ClapProcessor(tokenizer=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase			)

		__lowercase             =  floats_list((3, 1000)			)

		__lowercase             =  feature_extractor(_lowerCAmelCase       ,				return_tensors="""np"""			)
		__lowercase             =  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  _a   (  self      :							str			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.get_feature_extractor()
		__lowercase             =  self.get_tokenizer()

		__lowercase             =  ClapProcessor(tokenizer=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase			)

		__lowercase             =  """This is a test string"""

		__lowercase             =  processor(text=_lowerCAmelCase			)

		__lowercase             =  tokenizer(_lowerCAmelCase			)

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







	def  _a   (  self      :							Any			)		-> int:
		"""simple docstring"""







		__lowercase             =  self.get_feature_extractor()
		__lowercase             =  self.get_tokenizer()

		__lowercase             =  ClapProcessor(tokenizer=_lowerCAmelCase       ,				feature_extractor=_lowerCAmelCase			)

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

		__lowercase             =  processor.batch_decode(_lowerCAmelCase			)
		__lowercase             =  tokenizer.batch_decode(_lowerCAmelCase			)

		self.assertListEqual(_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  self.get_feature_extractor()
		__lowercase             =  self.get_tokenizer()

		__lowercase             =  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"""       ,				)









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCamelCase      :      Any     						=						{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Dict     						=						[
						    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MraForMaskedLM""",
						    """MraForMultipleChoice""",
						    """MraForQuestionAnswering""",
						    """MraForSequenceClassification""",
						    """MraForTokenClassification""",
						    """MraLayer""",
						    """MraModel""",
						    """MraPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mra import (
												    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MraForMaskedLM,
												    MraForMultipleChoice,
												    MraForQuestionAnswering,
												    MraForSequenceClassification,
												    MraForTokenClassification,
												    MraLayer,
												    MraModel,
												    MraPreTrainedModel,
												)
else:
						import sys

						__UpperCamelCase      :      List[str]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

