




from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ""
 lowercase_							       =       "hf-legacy"  # "hf://"" is reserved for hffs



 def __init__(				self    :						List[str]							,							_lowerCAmelCase    :						Optional[DatasetInfo] = None							,							_lowerCAmelCase    :						Optional[str] = None							,							**_lowerCAmelCase    :						Optional[int]							,							):
       super().__init__(self							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     repo_info
       SCREAMING_SNAKE_CASE_      		=     token
       SCREAMING_SNAKE_CASE_      		=     None



 def  lowerCAmelCase_    (				self    :						Tuple		):
       if self.dir_cache is None:
             SCREAMING_SNAKE_CASE_      		=     {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   SCREAMING_SNAKE_CASE_      		=     {
                       'name': hf_file.rfilename,
                       'size': None,
                       'type': 'file',
                   }
                   self.dir_cache.update(
                       {
                           str(_lowerCAmelCase		): {'name': str(_lowerCAmelCase		), 'size': None, 'type': 'directory'}
                           for d in list(PurePosixPath(hf_file.rfilename		).parents		)[:-1]
                       }		)



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						str = "rb"							,							**_lowerCAmelCase    :						Optional[Any]							,							):
       if not isinstance(self.repo_info							,							_lowerCAmelCase		):
             raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"		)
       SCREAMING_SNAKE_CASE_      		=     hf_hub_url(self.repo_info.id							,							_lowerCAmelCase							,							revision=self.repo_info.sha		)
       return fsspec.open(
           _lowerCAmelCase							,							mode=_lowerCAmelCase							,							headers=get_authentication_headers_for_url(_lowerCAmelCase							,							use_auth_token=self.token		)							,							client_kwargs={'trust_env': True}							,							).open()



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Optional[Any]							,							**_lowerCAmelCase    :						Any		):
       self._get_dirs()
       SCREAMING_SNAKE_CASE_      		=     self._strip_protocol(_lowerCAmelCase		)
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						Union[str, Any]=False							,							**_lowerCAmelCase    :						List[str]		):
       self._get_dirs()
       SCREAMING_SNAKE_CASE_      		=     PurePosixPath(path.strip('/'		)		)
       SCREAMING_SNAKE_CASE_      		=     {}
       for p, f in self.dir_cache.items():
             SCREAMING_SNAKE_CASE_      		=     PurePosixPath(p.strip('/'		)		)
             SCREAMING_SNAKE_CASE_      		=     p.parent
             if root == path:
                   SCREAMING_SNAKE_CASE_      		=     f
       SCREAMING_SNAKE_CASE_      		=     list(paths.values()		)
       if detail:
             return out
       else:
             return sorted(f['name'] for f in out		)





'''simple docstring'''
import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					@require_torch
					def     __snake_case						(	self     :						str)    ->							Optional[int]:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='hf-internal-testing/tiny-clap-htsat-unfused')
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= audio_classifier(_lowercase      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [{'score': 0.5_01, 'label': 'Sound of a dog'}, {'score': 0.4_99, 'label': 'Sound of vaccum cleaner'}]      ,       )



					@unittest.skip('No models are available in TF')
					def     __snake_case						(	self     :						Tuple)    ->							str:
								pass



					@slow
					@require_torch
					def     __snake_case						(	self     :						Optional[Any])    ->							Tuple:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='laion/clap-htsat-unfused'      ,       )
								# This is an audio of a dog
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= audio_classifier(_lowercase      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])

								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        {'score': 0.9_99, 'label': 'Sound of a dog'},
								        {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								    ]      ,       )

								A_									= audio_classifier([audio] * 5      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner'])
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5      ,       )
								A_									= audio_classifier(
								    [audio] * 5      ,       candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']      ,       batch_size=5)
								self.assertEqual(
								    nested_simplify(_lowercase)      ,       [
								        [
								            {'score': 0.9_99, 'label': 'Sound of a dog'},
								            {'score': 0.0_01, 'label': 'Sound of vaccum cleaner'},
								        ],
								    ]
								    * 5      ,       )



					@unittest.skip('No models are available in TF')
					def     __snake_case						(	self     :						List[str])    ->							str:
								pass




