






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


UpperCAmelCase__						:Optional[Any]          =			logging.get_logger(__name__)


UpperCAmelCase__						:Union[str, Any]          =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

UpperCAmelCase__						:Any          =			{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
}

UpperCAmelCase__						:Tuple          =			{
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def     __lowercase  ()   ->     Any:


     """simple docstring"""
     __lowerCamelCase						:       Optional[Any]				   =				(
         list(range(ord("""!"""     ),						ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     ),						ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     ),						ord("""ÿ"""     ) + 1     )     )
     )
     __lowerCamelCase						:       Tuple				   =				bs[:]
     __lowerCamelCase						:       str				   =				0
     for b in range(2**8     ):
          if b not in bs:
               bs.append(_lowercase     )
               cs.append(2**8 + n     )
               n += 1
     __lowerCamelCase						:       Tuple				   =				[chr(_lowercase     ) for n in cs]
     return dict(zip(_lowercase,						_lowercase     )     )
def     __lowercase  (_lowercase     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				set()
     __lowerCamelCase						:       Union[str, Any]				   =				word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)     )
          __lowerCamelCase						:       List[str]				   =				char
     return pairs







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     Union[str, Any]   		=			VOCAB_FILES_NAMES
       snake_case__    :     Tuple   		=			PRETRAINED_VOCAB_FILES_MAP
       snake_case__    :     Optional[int]   		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case__    :     Union[str, Any]   		=			['input_ids', 'attention_mask']




       def __init__(					self      : Dict	,    A__      : Dict	,    A__      : Union[str, Any]	,    A__      : Optional[Any]="replace"	,    A__      : Any="<s>"	,    A__      : Tuple="</s>"	,    A__      : Union[str, Any]="</s>"	,    A__      : int="<s>"	,    A__      : Optional[int]="<unk>"	,    A__      : Optional[int]="<pad>"	,    A__      : Dict="<mask>"	,    A__      : Optional[Any]=False	,    **A__      : Dict	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				AddedToken(A__	,    lstrip=A__	,    rstrip=A__					) if isinstance(A__	,    A__					) else bos_token
            __lowerCamelCase						:       List[Any]				   =				AddedToken(A__	,    lstrip=A__	,    rstrip=A__					) if isinstance(A__	,    A__					) else eos_token
            __lowerCamelCase						:       Optional[int]				   =				AddedToken(A__	,    lstrip=A__	,    rstrip=A__					) if isinstance(A__	,    A__					) else sep_token
            __lowerCamelCase						:       Optional[Any]				   =				AddedToken(A__	,    lstrip=A__	,    rstrip=A__					) if isinstance(A__	,    A__					) else cls_token
            __lowerCamelCase						:       List[Any]				   =				AddedToken(A__	,    lstrip=A__	,    rstrip=A__					) if isinstance(A__	,    A__					) else unk_token
            __lowerCamelCase						:       Optional[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
            __lowerCamelCase						:       int				   =				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:
                 __lowerCamelCase						:       int				   =				json.load(A__					)
            __lowerCamelCase						:       str				   =				{v: k for k, v in self.encoder.items()}
            __lowerCamelCase						:       List[Any]				   =				errors  # how to handle errors in decoding
            __lowerCamelCase						:       Optional[int]				   =				bytes_to_unicode()
            __lowerCamelCase						:       List[Any]				   =				{v: k for k, v in self.byte_encoder.items()}
            with open(A__	,    encoding="""utf-8"""					) as merges_handle:
                 __lowerCamelCase						:       Tuple				   =				merges_handle.read().split("""\n"""					)[1:-1]
            __lowerCamelCase						:       List[Any]				   =				[tuple(merge.split()					) for merge in bpe_merges]
            __lowerCamelCase						:       Optional[int]				   =				dict(zip(A__	,    range(len(A__					)					)					)					)
            __lowerCamelCase						:       int				   =				{}
            __lowerCamelCase						:       Optional[Any]				   =				add_prefix_space

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




       @property
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            return len(self.encoder					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            return dict(self.encoder	,    **self.added_tokens_encoder					)




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


            """simple docstring"""







            if token in self.cache:
                 return self.cache[token]
            __lowerCamelCase						:       List[Any]				   =				tuple(A__					)
            __lowerCamelCase						:       Tuple				   =				get_pairs(A__					)

            if not pairs:
                 return token

            while True:
                 __lowerCamelCase						:       Union[str, Any]				   =				min(A__	,    key=lambda A__					: self.bpe_ranks.get(A__	,    float("""inf"""					)					)					)
                 if bigram not in self.bpe_ranks:
                      break
                 __lowerCamelCase						:       Optional[int]				   =				bigram
                 __lowerCamelCase						:       Dict				   =				[]
                 __lowerCamelCase						:       Any				   =				0
                 while i < len(A__					):
                      try:
                           __lowerCamelCase						:       List[Any]				   =				word.index(A__	,    A__					)
                      except ValueError:
                           new_word.extend(word[i:]					)
                           break
                      else:
                           new_word.extend(word[i:j]					)
                           __lowerCamelCase						:       List[Any]				   =				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
                 __lowerCamelCase						:       Dict				   =				tuple(A__					)
                 __lowerCamelCase						:       Union[str, Any]				   =				new_word
                 if len(A__					) == 1:
                      break
                 else:
                      __lowerCamelCase						:       Any				   =				get_pairs(A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				""" """.join(A__					)
            __lowerCamelCase						:       int				   =				word
            return word




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


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				[]
            for token in re.findall(self.pat	,    A__					):
                 __lowerCamelCase						:       Any				   =				"""""".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 					a_					(					self      : List[str]	,    A__      : List[Any]					):


            """simple docstring"""







            return self.encoder.get(A__	,    self.encoder.get(self.unk_token					)					)




       def 					a_					(					self      : Tuple	,    A__      : Optional[int]					):


            """simple docstring"""







            return self.decoder.get(A__					)




       def 					a_					(					self      : Tuple	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				"""""".join(A__					)
            __lowerCamelCase						:       int				   =				bytearray([self.byte_decoder[c] for c in text]					).decode("""utf-8"""	,    errors=self.errors					)
            return text




       def 					a_					(					self      : List[str]	,    A__      : str	,    A__      : Optional[str] = None					):


            """simple docstring"""







            if not os.path.isdir(A__					):
                 logger.error(f"Vocabulary path ({save_directory}) should be a directory"					)
                 return
            __lowerCamelCase						:       List[str]				   =				os.path.join(
                A__	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
            __lowerCamelCase						:       List[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"""					)

            __lowerCamelCase						:       str				   =				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!"""					)
                           __lowerCamelCase						:       int				   =				token_index
                      writer.write(""" """.join(A__					) + """\n"""					)
                      index += 1

            return vocab_file, merge_file




       def 					a_					(					self      : Any	,    A__      : List[int]	,    A__      : Optional[List[int]] = None					):


            """simple docstring"""







            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __lowerCamelCase						:       int				   =				[self.cls_token_id]
            __lowerCamelCase						:       Optional[Any]				   =				[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 					a_					(					self      : Optional[int]	,    A__      : List[int]	,    A__      : Optional[List[int]] = None	,    A__      : bool = False					):


            """simple docstring"""







            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 					a_					(					self      : Any	,    A__      : List[int]	,    A__      : Optional[List[int]] = None					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				[self.sep_token_id]
            __lowerCamelCase						:       Any				   =				[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 					a_					(					self      : Tuple	,    A__      : Any	,    A__      : Any=False	,    **A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				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()):
                 __lowerCamelCase						:       Any				   =				""" """ + text
            return (text, kwargs)











'''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"""	,    )



