






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_pegasus import PegasusTokenizer
else:
				__SCREAMING_SNAKE_CASE						:   List[str]         =			None


__SCREAMING_SNAKE_CASE						:   int         =			logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE						:   List[Any]         =			'▁'

__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__SCREAMING_SNAKE_CASE						:   List[Any]         =			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

__SCREAMING_SNAKE_CASE						:   Any         =			{
    'google/pegasus-xsum': 5_1_2,
}



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		VOCAB_FILES_NAMES
							_lowerCamelCase						=		PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase						=		PegasusTokenizer
							_lowerCamelCase						=		['input_ids', 'attention_mask']



							def __init__(						self				,    lowercase_=None				,    lowercase_=None				,    lowercase_="<pad>"				,    lowercase_="</s>"				,    lowercase_="<unk>"				,    lowercase_="<mask_2>"				,    lowercase_="<mask_1>"				,    lowercase_=None				,    lowercase_=103				,    **lowercase_				,    ):
													_snake_case						:					str    =   offset

													if additional_special_tokens is not None:
																			if not isinstance(lowercase_				,    lowercase_    ):
																									raise TypeError(
																									    f"""additional_special_tokens should be of type {type(lowercase_    )}, but is"""
																									    f""" {type(lowercase_    )}"""    )

																			_snake_case						:					Dict    =   (
																			    ([mask_token_sent] + additional_special_tokens)
																			    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																			    else additional_special_tokens
																			)
																			# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																			additional_special_tokens_extended += [
																			    f"""<unk_{i}>""" for i in range(len(lowercase_    )				,    self.offset - 1    )
																			]

																			if len(set(lowercase_    )    ) != len(lowercase_    ):
																									raise ValueError(
																									    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
																									    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""    )
																			_snake_case						:					Optional[Any]    =   additional_special_tokens_extended
													else:
																			_snake_case						:					Optional[Any]    =   [mask_token_sent] if mask_token_sent is not None else []
																			additional_special_tokens += [f"""<unk_{i}>""" for i in range(2				,    self.offset    )]

													super().__init__(
													    lowercase_				,    tokenizer_file=lowercase_				,    pad_token=lowercase_				,    eos_token=lowercase_				,    unk_token=lowercase_				,    mask_token=lowercase_				,    mask_token_sent=lowercase_				,    offset=lowercase_				,    additional_special_tokens=lowercase_				,    **lowercase_				,    )
													_snake_case						:					Dict    =   vocab_file
													_snake_case						:					Optional[Any]    =   False if not self.vocab_file else True



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Tuple    =   set(self.all_special_ids    )  # call it once instead of inside list comp
													all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

													if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
																			raise ValueError(
																			    "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
																			    f""" {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}"""    )

													return [1 if x in all_special_ids else 0 for x in seq]



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = False    ):
													if already_has_special_tokens:
																			return self._special_token_mask(lowercase_    )
													elif token_ids_a is None:
																			return self._special_token_mask(lowercase_    ) + [1]
													else:
																			return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_=None    ):
													if token_ids_a is None:
																			return token_ids_a + [self.eos_token_id]
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return token_ids_a + token_ids_a + [self.eos_token_id]



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."    )

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

													if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase_    ):
																			copyfile(self.vocab_file				,    lowercase_    )

													return (out_vocab_file,)
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        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   "laion/clap-htsat-unfused"
													_snake_case						:					Dict    =   tempfile.mkdtemp()



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self    ):
													shutil.rmtree(self.tmpdirname    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.get_tokenizer()
													_snake_case						:					List[Any]    =   self.get_feature_extractor()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													processor.save_pretrained(self.tmpdirname    )
													_snake_case						:					Tuple    =   ClapProcessor.from_pretrained(self.tmpdirname    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()    )
													processor.save_pretrained(self.tmpdirname    )

													_snake_case						:					Any    =   self.get_tokenizer(bos_token="(BOS)"				,    eos_token="(EOS)"    )
													_snake_case						:					List[Any]    =   self.get_feature_extractor(do_normalize=lowercase_				,    padding_value=1.0    )

													_snake_case						:					List[Any]    =   ClapProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token="(BOS)"				,    eos_token="(EOS)"				,    do_normalize=lowercase_				,    padding_value=1.0    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.get_feature_extractor()
													_snake_case						:					Union[str, Any]    =   self.get_tokenizer()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					List[str]    =   floats_list((3, 1_000)    )

													_snake_case						:					Union[str, Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    )
													_snake_case						:					Any    =   processor(audios=lowercase_				,    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 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.get_feature_extractor()
													_snake_case						:					Optional[Any]    =   self.get_tokenizer()

													_snake_case						:					Dict    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Any    =   "This is a test string"

													_snake_case						:					Optional[Any]    =   processor(text=lowercase_    )

													_snake_case						:					Optional[Any]    =   tokenizer(lowercase_    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.get_feature_extractor()
													_snake_case						:					Dict    =   self.get_tokenizer()

													_snake_case						:					List[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Dict    =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													_snake_case						:					List[Any]    =   processor.batch_decode(lowercase_    )
													_snake_case						:					Optional[int]    =   tokenizer.batch_decode(lowercase_    )

													self.assertListEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.get_feature_extractor()
													_snake_case						:					str    =   self.get_tokenizer()

													_snake_case						:					Optional[int]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													self.assertListEqual(
													    processor.model_input_names[2:]				,    feature_extractor.model_input_names				,    msg="`processor` and `feature_extractor` model input names do not match"				,    )