






'''simple docstring'''

import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     int   		=			''
       snake_case__    :     str   		=			(
           None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
       )
       snake_case__    :     str   		=			None  # compression type in fsspec. ex: "gzip"
       snake_case__    :     str   		=			None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




       def __init__(					self      : Dict	,    A__      : str = ""	,    A__      : Optional[str] = None	,    A__      : Optional[dict] = None	,    **A__      : Dict					):


            """simple docstring"""







            super().__init__(self	,    **A__					)
            # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
            __lowerCamelCase						:       str				   =				fsspec.open(
                A__	,    mode="""rb"""	,    protocol=A__	,    compression=self.compression	,    client_kwargs={
                    """requote_redirect_url""": False,  # see https://github.com/huggingface/datasets/pull/5459
                    """trust_env""": True,  # Enable reading proxy env variables.
                    **(target_options or {}).pop("""client_kwargs"""	,    {}					),  # To avoid issues if it was already passed.
                }	,    **(target_options or {})	,    )
            __lowerCamelCase						:       Optional[int]				   =				os.path.basename(self.file.path.split("""::"""					)[0]					)
            __lowerCamelCase						:       int				   =				(
                self.compressed_name[: self.compressed_name.rindex("""."""					)]
                if """.""" in self.compressed_name
                else self.compressed_name
            )
            __lowerCamelCase						:       Tuple				   =				None




       @classmethod
       def 					a_					(					cls      : List[Any]	,    A__      : int					):


            """simple docstring"""







            return super()._strip_protocol(A__					).lstrip("""/"""					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            if self.dir_cache is None:
                 __lowerCamelCase						:       List[str]				   =				{**self.file.fs.info(self.file.path					), """name""": self.uncompressed_name}
                 __lowerCamelCase						:       Dict				   =				{f["""name"""]: f}




       def 					a_					(					self      : List[str]	,    A__      : str					):


            """simple docstring"""







            return self.file.open().read()




       def 					a_					(					self      : Union[str, Any]	,    A__      : str	,    A__      : str = "rb"	,    A__      : List[str]=None	,    A__      : List[Any]=True	,    A__      : Any=None	,    **A__      : Tuple	,    ):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self._strip_protocol(A__					)
            if mode != "rb":
                 raise ValueError(f"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"					)
            return self.file.open()







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     List[str]   		=			'bz2'
       snake_case__    :     int   		=			'bz2'
       snake_case__    :     str   		=			'.bz2'







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     int   		=			'gzip'
       snake_case__    :     Optional[Any]   		=			'gzip'
       snake_case__    :     Tuple   		=			'.gz'







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Optional[Any]   		=			'lz4'
       snake_case__    :     Union[str, Any]   		=			'lz4'
       snake_case__    :     Optional[Any]   		=			'.lz4'







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Dict   		=			'xz'
       snake_case__    :     Any   		=			'xz'
       snake_case__    :     Optional[int]   		=			'.xz'







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Optional[Any]   		=			'zstd'
       snake_case__    :     Optional[int]   		=			'zstd'
       snake_case__    :     Union[str, Any]   		=			'.zst'




       def __init__(					self      : List[str]	,    A__      : str	,    A__      : str = "rb"	,    A__      : Optional[str] = None	,    A__      : Optional[dict] = None	,    A__      : int = DEFAULT_BLOCK_SIZE	,    **A__      : Any	,    ):


            """simple docstring"""







            super().__init__(
                fo=A__	,    mode=A__	,    target_protocol=A__	,    target_options=A__	,    block_size=A__	,    **A__	,    )
            # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
            #
            # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
            #     out.close = close
            # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
            #
            # see https://github.com/intake/filesystem_spec/issues/725
            __lowerCamelCase						:       Any				   =				self.file.__enter__







            class 	SCREAMING_SNAKE_CASE	:




                   def __init__(					self      : Dict	,    A__      : Optional[Any]					):


                        """simple docstring"""







                        __lowerCamelCase						:       int				   =				file_




                   def __enter__(					self      : Optional[Any]					):


                        """simple docstring"""







                        self._file.__enter__()
                        return self




                   def __exit__(					self      : List[Any]	,    *A__      : Tuple	,    **A__      : Union[str, Any]					):


                        """simple docstring"""







                        self._file.__exit__(*A__	,    **A__					)




                   def __iter__(					self      : str					):


                        """simple docstring"""







                        return iter(self._file					)




                   def 					a_					(					self      : Union[str, Any]					):


                        """simple docstring"""







                        return next(self._file					)




                   def __getattr__(					self      : Tuple	,    A__      : List[str]					):


                        """simple docstring"""







                        return getattr(self._file	,    A__					)

            def fixed_enter(*A__      : List[str]	,    **A__      : List[str]					):
                 return WrappedFile(_enter(*A__	,    **A__					)					)

            __lowerCamelCase						:       int				   =				fixed_enter











'''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 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				"""laion/clap-htsat-unfused"""
            __lowerCamelCase						:       List[Any]				   =				tempfile.mkdtemp()




       def 					a_					(					self      : Optional[int]	,    **A__      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : str	,    **A__      : Any					):


            """simple docstring"""







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




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            shutil.rmtree(self.tmpdirname					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.get_tokenizer()
            __lowerCamelCase						:       Union[str, Any]				   =				self.get_feature_extractor()

            __lowerCamelCase						:       Dict				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            processor.save_pretrained(self.tmpdirname					)
            __lowerCamelCase						:       Optional[Any]				   =				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      : str					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				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						:       Any				   =				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 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.get_feature_extractor()
            __lowerCamelCase						:       Tuple				   =				self.get_tokenizer()

            __lowerCamelCase						:       Optional[int]				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       Dict				   =				floats_list((3, 1000)					)

            __lowerCamelCase						:       str				   =				feature_extractor(A__	,    return_tensors="""np"""					)
            __lowerCamelCase						:       Any				   =				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      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.get_feature_extractor()
            __lowerCamelCase						:       int				   =				self.get_tokenizer()

            __lowerCamelCase						:       str				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       int				   =				"""This is a test string"""

            __lowerCamelCase						:       Any				   =				processor(text=A__					)

            __lowerCamelCase						:       Any				   =				tokenizer(A__					)

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




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.get_feature_extractor()
            __lowerCamelCase						:       List[str]				   =				self.get_tokenizer()

            __lowerCamelCase						:       int				   =				ClapProcessor(tokenizer=A__	,    feature_extractor=A__					)

            __lowerCamelCase						:       Union[str, Any]				   =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            __lowerCamelCase						:       int				   =				processor.batch_decode(A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.batch_decode(A__					)

            self.assertListEqual(A__	,    A__					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.get_feature_extractor()
            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer()

            __lowerCamelCase						:       Any				   =				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"""	,    )



