



"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_lowerCAmelCase    :Any     =					logging.get_logger(__name__)


_lowerCAmelCase    :List[Any]     =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase    :Any     =					{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

_lowerCAmelCase    :List[str]     =					{
    'facebook/bart-base': 1_024,
    'facebook/bart-large': 1_024,
    'facebook/bart-large-mnli': 1_024,
    'facebook/bart-large-cnn': 1_024,
    'facebook/bart-large-xsum': 1_024,
    'yjernite/bart_eli5': 1_024,
}





@lru_cache()
def  lowerCamelCase_						():
    _UpperCAmelCase							:  Any											=    (
        list(range(ord('''!'''							)     ,       ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)     ,       ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)     ,       ord('''ÿ'''							) + 1							)							)
    )
    _UpperCAmelCase							:  Optional[Any]											=    bs[:]
    _UpperCAmelCase							:  List[str]											=    0
    for b in range(2**8							):
        if b not in bs:
            bs.append(UpperCamelCase__							)
            cs.append(2**8 + n							)
            n += 1
    _UpperCAmelCase							:  Optional[Any]											=    [chr(UpperCamelCase__							) for n in cs]
    return dict(zip(UpperCamelCase__     ,       UpperCamelCase__							)							)





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							:  Union[str, Any]											=    set()
    _UpperCAmelCase							:  Dict											=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)							)
        _UpperCAmelCase							:  str											=    char
    return pairs


class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=['''input_ids''', '''attention_mask''']





     def __init__(					self       ,	A       ,	A       ,	A="replace"       ,	A="<s>"       ,	A="</s>"       ,	A="</s>"       ,	A="<s>"       ,	A="<unk>"       ,	A="<pad>"       ,	A="<mask>"       ,	A=False       ,	**A       ,	)							->					Union[str, Any]:
         _UpperCAmelCase							:  str											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else bos_token
         _UpperCAmelCase							:  List[str]											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else eos_token
         _UpperCAmelCase							:  Tuple											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else sep_token
         _UpperCAmelCase							:  Dict											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else cls_token
         _UpperCAmelCase							:  Tuple											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else unk_token
         _UpperCAmelCase							:  int											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         _UpperCAmelCase							:  Optional[Any]											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else mask_token

         super().__init__(
             errors=A       ,	bos_token=A       ,	eos_token=A       ,	unk_token=A       ,	sep_token=A       ,	cls_token=A       ,	pad_token=A       ,	mask_token=A       ,	add_prefix_space=A       ,	**A       ,	)

         with open(A       ,	encoding='''utf-8'''		) as vocab_handle:
             _UpperCAmelCase							:  List[Any]											=    json.load(A		)
         _UpperCAmelCase							:  Dict											=    {v: k for k, v in self.encoder.items()}
         _UpperCAmelCase							:  Tuple											=    errors  # how to handle errors in decoding
         _UpperCAmelCase							:  List[str]											=    bytes_to_unicode()
         _UpperCAmelCase							:  Tuple											=    {v: k for k, v in self.byte_encoder.items()}
         with open(A       ,	encoding='''utf-8'''		) as merges_handle:
             _UpperCAmelCase							:  Any											=    merges_handle.read().split('''\n'''		)[1:-1]
         _UpperCAmelCase							:  List[Any]											=    [tuple(merge.split()		) for merge in bpe_merges]
         _UpperCAmelCase							:  Union[str, Any]											=    dict(zip(A       ,	range(len(A		)		)		)		)
         _UpperCAmelCase							:  List[Any]											=    {}
         _UpperCAmelCase							:  Optional[int]											=    add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         _UpperCAmelCase							:  Optional[Any]											=    re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''		)





     @property
     def  __lowerCAmelCase	(					self		)							->					str:
         return len(self.encoder		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         return dict(self.encoder       ,	**self.added_tokens_encoder		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Tuple:
         if token in self.cache:
             return self.cache[token]
         _UpperCAmelCase							:  Optional[int]											=    tuple(A		)
         _UpperCAmelCase							:  List[str]											=    get_pairs(A		)

         if not pairs:
             return token

         while True:
             _UpperCAmelCase							:  Optional[int]											=    min(A       ,	key=lambda A		: self.bpe_ranks.get(A       ,	float('''inf'''		)		)		)
             if bigram not in self.bpe_ranks:
                 break
             _UpperCAmelCase							,				_UpperCAmelCase							:  Tuple											=    bigram
             _UpperCAmelCase							:  Optional[Any]											=    []
             _UpperCAmelCase							:  Optional[int]											=    0
             while i < len(A		):
                 try:
                     _UpperCAmelCase							:  Optional[Any]											=    word.index(A       ,	A		)
                 except ValueError:
                     new_word.extend(word[i:]		)
                     break
                 else:
                     new_word.extend(word[i:j]		)
                     _UpperCAmelCase							:  Optional[int]											=    j

                 if word[i] == first and i < len(A		) - 1 and word[i + 1] == second:
                     new_word.append(first + second		)
                     i += 2
                 else:
                     new_word.append(word[i]		)
                     i += 1
             _UpperCAmelCase							:  Optional[Any]											=    tuple(A		)
             _UpperCAmelCase							:  int											=    new_word
             if len(A		) == 1:
                 break
             else:
                 _UpperCAmelCase							:  Any											=    get_pairs(A		)
         _UpperCAmelCase							:  List[Any]											=    ''' '''.join(A		)
         _UpperCAmelCase							:  int											=    word
         return word





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  int											=    []
         for token in re.findall(self.pat       ,	A		):
             _UpperCAmelCase							:  Dict											=    ''''''.join(
                 self.byte_encoder[b] for b in token.encode('''utf-8'''		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A		).split(''' '''		)		)
         return bpe_tokens





     def  __lowerCAmelCase	(					self       ,	A		)							->					str:
         return self.encoder.get(A       ,	self.encoder.get(self.unk_token		)		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					str:
         return self.decoder.get(A		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[Any]:
         _UpperCAmelCase							:  int											=    ''''''.join(A		)
         _UpperCAmelCase							:  str											=    bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''       ,	errors=self.errors		)
         return text





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					Tuple[str]:
         if not os.path.isdir(A		):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
             return
         _UpperCAmelCase							:  List[Any]											=    os.path.join(
             A       ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
         _UpperCAmelCase							:  Optional[Any]											=    os.path.join(
             A       ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

         with open(A       ,	'''w'''       ,	encoding='''utf-8'''		) as f:
             f.write(json.dumps(self.encoder       ,	indent=2       ,	sort_keys=A       ,	ensure_ascii=A		) + '''\n'''		)

         _UpperCAmelCase							:  Dict											=    0
         with open(A       ,	'''w'''       ,	encoding='''utf-8'''		) as writer:
             writer.write('''#version: 0.2\n'''		)
             for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,	key=lambda A		: kv[1]		):
                 if index != token_index:
                     logger.warning(
                         f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                         ''' Please check that the tokenizer is not corrupted!'''		)
                     _UpperCAmelCase							:  Dict											=    token_index
                 writer.write(''' '''.join(A		) + '''\n'''		)
                 index += 1

         return vocab_file, merge_file





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:
         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _UpperCAmelCase							:  str											=    [self.cls_token_id]
         _UpperCAmelCase							:  Union[str, Any]											=    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = False		)							->					List[int]:
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=A       ,	token_ids_a=A       ,	already_has_special_tokens=A		)

         if token_ids_a is None:
             return [1] + ([0] * len(A		)) + [1]
         return [1] + ([0] * len(A		)) + [1, 1] + ([0] * len(A		)) + [1]





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:
         _UpperCAmelCase							:  Optional[int]											=    [self.sep_token_id]
         _UpperCAmelCase							:  str											=    [self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep		) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





     def  __lowerCAmelCase	(					self       ,	A       ,	A=False       ,	**A		)							->					int:
         _UpperCAmelCase							:  List[Any]											=    kwargs.pop('''add_prefix_space'''       ,	self.add_prefix_space		)
         if (is_split_into_words or add_prefix_space) and (len(A		) > 0 and not text[0].isspace()):
             _UpperCAmelCase							:  str											=    ''' ''' + text
         return (text, kwargs)







"""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  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  str											=    pipeline(
             task='''zero-shot-audio-classification'''       ,	model='''hf-internal-testing/tiny-clap-htsat-unfused'''		)
         _UpperCAmelCase							:  List[Any]											=    load_dataset('''ashraq/esc50'''		)
         _UpperCAmelCase							:  Optional[int]											=    dataset['''train''']['''audio'''][-1]['''array''']
         _UpperCAmelCase							:  str											=    audio_classifier(A       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}]       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         pass





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

         self.assertEqual(
             nested_simplify(A		)       ,	[
                 {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                 {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
             ]       ,	)

         _UpperCAmelCase							:  List[Any]											=    audio_classifier([audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)
         _UpperCAmelCase							:  Tuple											=    audio_classifier(
             [audio] * 5       ,	candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']       ,	batch_size=5		)
         self.assertEqual(
             nested_simplify(A		)       ,	[
                 [
                     {'''score''': 0.999, '''label''': '''Sound of a dog'''},
                     {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
                 ],
             ]
             * 5       ,	)





     @unittest.skip('''No models are available in TF'''		)
     def  __lowerCAmelCase	(					self		)							->					int:
         pass


