






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

import sentencepiece as spm

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


__SCREAMING_SNAKE_CASE						:   Any         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   Dict         =			'▁'

__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			{'vocab_file': 'sentencepiece.bpe.model'}

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

__SCREAMING_SNAKE_CASE						:   Optional[int]         =			{
    'facebook/mbart-large-50-one-to-many-mmt': 1_0_2_4,
}

# fmt: off
__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']



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

							_lowerCamelCase						=		[]
							_lowerCamelCase						=		[]



							def __init__(						self				,    lowercase_				,    lowercase_=None				,    lowercase_=None				,    lowercase_="</s>"				,    lowercase_="</s>"				,    lowercase_="<s>"				,    lowercase_="<unk>"				,    lowercase_="<pad>"				,    lowercase_="<mask>"				,    lowercase_ = None				,    **lowercase_				,    ):
													_snake_case						:					Dict    =   AddedToken(lowercase_				,    lstrip=lowercase_				,    rstrip=lowercase_    ) if isinstance(lowercase_				,    lowercase_    ) else mask_token

													_snake_case						:					Optional[int]    =   {} if sp_model_kwargs is None else sp_model_kwargs

													_snake_case						:					Optional[Any]    =   kwargs.get("additional_special_tokens"				,    []    )
													kwargs["additional_special_tokens"] += [
													    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
													]

													super().__init__(
													    src_lang=lowercase_				,    tgt_lang=lowercase_				,    eos_token=lowercase_				,    unk_token=lowercase_				,    sep_token=lowercase_				,    cls_token=lowercase_				,    pad_token=lowercase_				,    mask_token=lowercase_				,    sp_model_kwargs=self.sp_model_kwargs				,    **lowercase_				,    )

													_snake_case						:					List[str]    =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
													self.sp_model.Load(str(lowercase_    )    )
													_snake_case						:					str    =   vocab_file

													# Original fairseq vocab and spm vocab must be "aligned":
													# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
													# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
													# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
													# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

													# Mimic fairseq token-to-id alignment for the first 4 token
													_snake_case						:					Optional[int]    =   {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

													# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
													_snake_case						:					Dict    =   1

													_snake_case						:					str    =   len(self.sp_model    )
													_snake_case						:					Union[str, Any]    =   {
													    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase_    )
													}
													_snake_case						:					str    =   {v: k for k, v in self.lang_code_to_id.items()}
													_snake_case						:					Optional[int]    =   len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset

													self.fairseq_tokens_to_ids.update(self.lang_code_to_id    )
													_snake_case						:					Tuple    =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}

													_snake_case						:					List[str]    =   src_lang if src_lang is not None else "en_XX"
													_snake_case						:					Any    =   self.lang_code_to_id[self._src_lang]
													_snake_case						:					List[Any]    =   tgt_lang
													self.set_src_lang_special_tokens(self._src_lang    )



							@property
							def 	UpperCamelCase	(						self    ):
													return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token



							@property
							def 	UpperCamelCase	(						self    ):
													return self._src_lang



							@src_lang.setter
							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					str    =   new_src_lang
													self.set_src_lang_special_tokens(self._src_lang    )



							def __getstate__(						self    ):
													_snake_case						:					Tuple    =   self.__dict__.copy()
													_snake_case						:					str    =   None
													return state



							def __setstate__(						self				,    lowercase_    ):
													_snake_case						:					Tuple    =   d

													# for backward compatibility
													if not hasattr(self				,    "sp_model_kwargs"    ):
																			_snake_case						:					Union[str, Any]    =   {}

													_snake_case						:					Dict    =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
													self.sp_model.Load(self.vocab_file    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   {self.convert_ids_to_tokens(lowercase_    ): i for i in range(self.vocab_size    )}
													vocab.update(self.added_tokens_encoder    )
													return vocab



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													return self.sp_model.encode(lowercase_				,    out_type=lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):







													if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
													_snake_case						:					Union[str, Any]    =   self.sp_model.PieceToId(lowercase_    )

													# Need to return unknown token if the SP model returned 0
													return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



							def 	UpperCamelCase	(						self				,    lowercase_    ):







													if index in self.fairseq_ids_to_tokens:
																			return self.fairseq_ids_to_tokens[index]
													return self.sp_model.IdToPiece(index - self.fairseq_offset    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					Dict    =   []
													_snake_case						:					Union[str, Any]    =   ""
													_snake_case						:					Tuple    =   False
													for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																									if not prev_is_special:
																															out_string += " "
																									out_string += self.sp_model.decode(lowercase_    ) + token
																									_snake_case						:					Any    =   True
																									_snake_case						:					Optional[Any]    =   []
																			else:
																									current_sub_tokens.append(lowercase_    )
																									_snake_case						:					Union[str, Any]    =   False
													out_string += self.sp_model.decode(lowercase_    )
													return out_string.strip()



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):







													if not os.path.isdir(lowercase_    ):
																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
																			return
													_snake_case						:					Dict    =   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_    ) and os.path.isfile(self.vocab_file    ):
																			copyfile(self.vocab_file				,    lowercase_    )
													elif not os.path.isfile(self.vocab_file    ):
																			with open(lowercase_				,    "wb"    ) as fi:
																									_snake_case						:					List[Any]    =   self.sp_model.serialized_model_proto()
																									fi.write(lowercase_    )

													return (out_vocab_file,)



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = False    ):







													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=lowercase_				,    token_ids_a=lowercase_				,    already_has_special_tokens=lowercase_    )

													_snake_case						:					Tuple    =   [1] * len(self.prefix_tokens    )
													_snake_case						:					int    =   [1] * len(self.suffix_tokens    )
													if token_ids_a is None:
																			return prefix_ones + ([0] * len(lowercase_    )) + suffix_ones
													return prefix_ones + ([0] * len(lowercase_    )) + ([0] * len(lowercase_    )) + suffix_ones



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):







													if token_ids_a is None:
																			return self.prefix_tokens + token_ids_a + self.suffix_tokens
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_				,    **lowercase_    ):







													if src_lang is None or tgt_lang is None:
																			raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"    )
													_snake_case						:					Optional[Any]    =   src_lang
													_snake_case						:					str    =   self(lowercase_				,    add_special_tokens=lowercase_				,    return_tensors=lowercase_				,    **lowercase_    )
													_snake_case						:					Optional[int]    =   self.convert_tokens_to_ids(lowercase_    )
													_snake_case						:					str    =   tgt_lang_id
													return inputs



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = "en_XX"				,    lowercase_ = None				,    lowercase_ = "ro_RO"				,    **lowercase_				,    ):
													_snake_case						:					Optional[Any]    =   src_lang
													_snake_case						:					List[Any]    =   tgt_lang
													return super().prepare_seqaseq_batch(lowercase_				,    lowercase_				,    **lowercase_    )



							def 	UpperCamelCase	(						self    ):
													return self.set_src_lang_special_tokens(self.src_lang    )



							def 	UpperCamelCase	(						self    ):
													return self.set_tgt_lang_special_tokens(self.tgt_lang    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					int    =   self.lang_code_to_id[src_lang]
													_snake_case						:					Any    =   [self.cur_lang_code_id]
													_snake_case						:					Tuple    =   [self.eos_token_id]



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					int    =   self.lang_code_to_id[tgt_lang]
													_snake_case						:					List[Any]    =   [self.cur_lang_code_id]
													_snake_case						:					Optional[Any]    =   [self.eos_token_id]
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"				,    )