




'''simple docstring'''
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{"vocab_file": "vocab.txt"}

_SCREAMING_SNAKE_CASE			=						{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

_SCREAMING_SNAKE_CASE			=						{
    "facebook/esm2_t6_8M_UR50D": 10_24,
    "facebook/esm2_t12_35M_UR50D": 10_24,
}





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Optional[int]:
			with open(SCREAMING_SNAKE_CASE_       ,'r'     ) as f:
						A_									= f.read().splitlines()
						return [l.strip() for l in lines]






class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		VOCAB_FILES_NAMES
					_UpperCamelCase							=		PRETRAINED_VOCAB_FILES_MAP
					_UpperCamelCase							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_UpperCamelCase							=		["""input_ids""", """attention_mask"""]



					def __init__(	self     :						Union[str, Any]      ,       _lowercase     :						List[str]      ,       _lowercase     :						List[Any]="<unk>"      ,       _lowercase     :						str="<cls>"      ,       _lowercase     :						Dict="<pad>"      ,       _lowercase     :						Any="<mask>"      ,       _lowercase     :						Dict="<eos>"      ,       **_lowercase     :						Dict      ,       )    ->							int:
								super().__init__(**_lowercase)
								A_									= load_vocab_file(_lowercase)
								A_									= dict(enumerate(self.all_tokens))
								A_									= {tok: ind for ind, tok in enumerate(self.all_tokens)}
								A_									= unk_token
								A_									= cls_token
								A_									= pad_token
								A_									= mask_token
								A_									= eos_token
								A_									= self.all_tokens
								self._create_trie(self.unique_no_split_tokens)



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						int)    ->							str:
								return self._id_to_token.get(_lowercase      ,       self.unk_token)



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						str)    ->							int:
								return self._token_to_id.get(_lowercase      ,       self._token_to_id.get(self.unk_token))



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						List[str]      ,       **_lowercase     :						int)    ->							Dict:
								return text.split()



					def     __snake_case						(	self     :						int      ,       _lowercase     :						int=False)    ->							List[str]:
								return len(self._id_to_token)



					def     __snake_case						(	self     :						int)    ->							Union[str, Any]:
								return {token: i for i, token in enumerate(self.all_tokens)}



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						str)    ->							int:
								return self._token_to_id.get(_lowercase      ,       self._token_to_id.get(self.unk_token))



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						int)    ->							str:
								return self._id_to_token.get(_lowercase      ,       self.unk_token)



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						List[int]      ,       _lowercase     :						Optional[List[int]] = None)    ->							List[int]:
								A_									= [self.cls_token_id]
								A_									= [self.eos_token_id]  # No sep token in ESM vocabulary
								if token_ids_a is None:
											if self.eos_token_id is None:
														return cls + token_ids_a
											else:
														return cls + token_ids_a + sep
								elif self.eos_token_id is None:
											raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!')
								return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						List      ,       _lowercase     :						Optional[List] = None      ,       _lowercase     :						bool = False)    ->							List[int]:
								if already_has_special_tokens:
											if token_ids_a is not None:
														raise ValueError(
														    'You should not supply a second sequence if the provided sequence of '
														    'ids is already formatted with special tokens for the model.')

											return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
								A_									= [1] + ([0] * len(_lowercase)) + [1]
								if token_ids_a is not None:
											mask += [0] * len(_lowercase) + [1]
								return mask



					def     __snake_case						(	self     :						Dict      ,       _lowercase     :						str      ,       _lowercase     :						Optional[int])    ->							Dict:
								A_									= os.path.join(_lowercase      ,       (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt')
								with open(_lowercase      ,       'w') as f:
											f.write('\n'.join(self.all_tokens))
								return (vocab_file,)



					@property
					def     __snake_case						(	self     :						int)    ->							int:
								return self.get_vocab_size(with_added_tokens=_lowercase)



					def     __snake_case						(	self     :						Any      ,       _lowercase     :						Union[List[str], List[AddedToken]]      ,       _lowercase     :						bool = False)    ->							int:
								return super()._add_tokens(_lowercase      ,       special_tokens=_lowercase)










'''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




