
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


lowerCamelCase_						: Union[str, Any]         =  logging.get_logger(__name__)

lowerCamelCase_						: Optional[Any]         =  """▁"""

lowerCamelCase_						: List[str]         =  {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

lowerCamelCase_						: Optional[Any]         =  {
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

lowerCamelCase_						: Union[str, Any]         =  {
    """facebook/m2m100_418M""": 10_24,
}

# fmt: off
lowerCamelCase_						: str         =  {
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Union[str, Any]					      = VOCAB_FILES_NAMES
			__UpperCamelCase      :						Optional[int]					      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase      :						Dict					      = PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase      :						Any					      = ["""input_ids""", """attention_mask"""]

			__UpperCamelCase      :						List[int]					      = []
			__UpperCamelCase      :						List[int]					      = []

			def __init__(       self :       List[Any]						,	snake_case_ :       List[str]						,	snake_case_ :       Any						,	snake_case_ :       List[Any]=None						,	snake_case_ :       Union[str, Any]=None						,	snake_case_ :       Optional[Any]="<s>"						,	snake_case_ :       int="</s>"						,	snake_case_ :       Tuple="</s>"						,	snake_case_ :       Optional[int]="<pad>"						,	snake_case_ :       Tuple="<unk>"						,	snake_case_ :       Dict="m2m100"						,	snake_case_ :       Optional[Dict[str, Any]] = None						,	snake_case_ :       Tuple=8						,	**snake_case_ :       Optional[int]						,	):
							UpperCamelCase_:							Tuple	=						{} if sp_model_kwargs is None else sp_model_kwargs

							UpperCamelCase_:							str	=						language_codes
							UpperCamelCase_:							int	=						FAIRSEQ_LANGUAGE_CODES[language_codes]
							UpperCamelCase_:							Optional[int]	=						{lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

							UpperCamelCase_:							Tuple	=						kwargs.get("""additional_special_tokens"""						,	[]      )
							kwargs["additional_special_tokens"] += [
							    self.get_lang_token(snake_case_      )
							    for lang_code in fairseq_language_code
							    if self.get_lang_token(snake_case_      ) not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=snake_case_						,	tgt_lang=snake_case_						,	bos_token=snake_case_						,	eos_token=snake_case_						,	sep_token=snake_case_						,	unk_token=snake_case_						,	pad_token=snake_case_						,	language_codes=snake_case_						,	sp_model_kwargs=self.sp_model_kwargs						,	num_madeup_words=snake_case_						,	**snake_case_						,	)

							UpperCamelCase_:							Any	=						vocab_file
							UpperCamelCase_:							Optional[Any]	=						load_json(snake_case_      )
							UpperCamelCase_:							List[str]	=						{v: k for k, v in self.encoder.items()}
							UpperCamelCase_:							Optional[Any]	=						spm_file
							UpperCamelCase_:							str	=						load_spm(snake_case_						,	self.sp_model_kwargs      )

							UpperCamelCase_:							Union[str, Any]	=						len(self.encoder      )

							UpperCamelCase_:							Optional[int]	=						{
							    self.get_lang_token(snake_case_      ): self.encoder_size + i for i, lang_code in enumerate(snake_case_      )
							}
							UpperCamelCase_:							Tuple	=						{lang_code: self.encoder_size + i for i, lang_code in enumerate(snake_case_      )}
							UpperCamelCase_:							Tuple	=						{v: k for k, v in self.lang_token_to_id.items()}

							UpperCamelCase_:							int	=						src_lang if src_lang is not None else """en"""
							UpperCamelCase_:							Optional[Any]	=						tgt_lang
							UpperCamelCase_:							str	=						self.get_lang_id(self._src_lang      )
							self.set_src_lang_special_tokens(self._src_lang      )

							UpperCamelCase_:							Optional[Any]	=						num_madeup_words

			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							return len(self.encoder      ) + len(self.lang_token_to_id      )

			@property
			def       lowerCAmelCase__	(       self :       int      ):
							return self._src_lang

			@src_lang.setter
			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       str      ):
							UpperCamelCase_:							List[Any]	=						new_src_lang
							self.set_src_lang_special_tokens(self._src_lang      )

			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       str      ):
							return self.sp_model.encode(snake_case_						,	out_type=snake_case_      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       Tuple      ):
							if token in self.lang_token_to_id:
											return self.lang_token_to_id[token]
							return self.encoder.get(snake_case_						,	self.encoder[self.unk_token]      )

			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       int      ):
							if index in self.id_to_lang_token:
											return self.id_to_lang_token[index]
							return self.decoder.get(snake_case_						,	self.unk_token      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       Any      ):
							UpperCamelCase_:							Any	=						[]
							UpperCamelCase_:							Union[str, Any]	=						""""""
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += self.sp_model.decode(snake_case_      ) + token
															UpperCamelCase_:							Optional[int]	=						[]
											else:
															current_sub_tokens.append(snake_case_      )
							out_string += self.sp_model.decode(snake_case_      )
							return out_string.strip()

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       List[int]						,	snake_case_ :       Optional[List[int]] = None						,	snake_case_ :       bool = False      ):

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

							UpperCamelCase_:							Union[str, Any]	=						[1] * len(self.prefix_tokens      )
							UpperCamelCase_:							str	=						[1] * len(self.suffix_tokens      )
							if token_ids_a is None:
											return prefix_ones + ([0] * len(snake_case_      )) + suffix_ones
							return prefix_ones + ([0] * len(snake_case_      )) + ([0] * len(snake_case_      )) + suffix_ones

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       List[int]						,	snake_case_ :       Optional[List[int]] = 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       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						{self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
							vocab.update(self.added_tokens_encoder      )
							return vocab

			def __getstate__(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Any	=						self.__dict__.copy()
							UpperCamelCase_:							Any	=						None
							return state

			def __setstate__(       self :       Union[str, Any]						,	snake_case_ :       Dict      ):
							UpperCamelCase_:							List[Any]	=						d

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

							UpperCamelCase_:							Any	=						load_spm(self.spm_file						,	self.sp_model_kwargs      )

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       str						,	snake_case_ :       Optional[str] = None      ):
							UpperCamelCase_:							Tuple	=						Path(snake_case_      )
							if not save_dir.is_dir():
											raise OSError(f'''{save_directory} should be a directory'''      )
							UpperCamelCase_:							str	=						save_dir / (
							    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
							)
							UpperCamelCase_:							Dict	=						save_dir / (
							    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
							)

							save_json(self.encoder						,	snake_case_      )

							if os.path.abspath(self.spm_file      ) != os.path.abspath(snake_case_      ) and os.path.isfile(self.spm_file      ):
											copyfile(self.spm_file						,	snake_case_      )
							elif not os.path.isfile(self.spm_file      ):
											with open(snake_case_						,	"""wb"""      ) as fi:
															UpperCamelCase_:							Any	=						self.sp_model.serialized_model_proto()
															fi.write(snake_case_      )

							return (str(snake_case_      ), str(snake_case_      ))

			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       List[str]						,	snake_case_ :       str = "en"						,	snake_case_ :       Optional[List[str]] = None						,	snake_case_ :       str = "ro"						,	**snake_case_ :       Dict						,	):
							UpperCamelCase_:							Tuple	=						src_lang
							UpperCamelCase_:							str	=						tgt_lang
							self.set_src_lang_special_tokens(self.src_lang      )
							return super().prepare_seqaseq_batch(snake_case_						,	snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       Dict						,	snake_case_ :       Optional[str]						,	snake_case_ :       Optional[str]						,	**snake_case_ :       Union[str, Any]      ):
							if src_lang is None or tgt_lang is None:
											raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""      )
							UpperCamelCase_:							str	=						src_lang
							UpperCamelCase_:							Optional[Any]	=						self(snake_case_						,	add_special_tokens=snake_case_						,	**snake_case_      )
							UpperCamelCase_:							Tuple	=						self.get_lang_id(snake_case_      )
							UpperCamelCase_:							str	=						tgt_lang_id
							return inputs

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							self.set_src_lang_special_tokens(self.src_lang      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							self.set_tgt_lang_special_tokens(self.tgt_lang      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       str      ):
							UpperCamelCase_:							int	=						self.get_lang_token(snake_case_      )
							UpperCamelCase_:							Dict	=						self.lang_token_to_id[lang_token]
							UpperCamelCase_:							List[str]	=						[self.cur_lang_id]
							UpperCamelCase_:							Dict	=						[self.eos_token_id]

			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       str      ):
							UpperCamelCase_:							List[str]	=						self.get_lang_token(snake_case_      )
							UpperCamelCase_:							Any	=						self.lang_token_to_id[lang_token]
							UpperCamelCase_:							Tuple	=						[self.cur_lang_id]
							UpperCamelCase_:							int	=						[self.eos_token_id]

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       str      ):
							return self.lang_code_to_token[lang]




			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       str      ):
							UpperCamelCase_:							Optional[int]	=						self.get_lang_token(snake_case_      )
							return self.lang_token_to_id[lang_token]







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	sentencepiece.SentencePieceProcessor:
				UpperCamelCase_:							Dict	=						sentencepiece.SentencePieceProcessor(**lowerCamelCase						)
				spm.Load(str(lowerCamelCase						)						)
				return spm







def        A__					(						lowerCamelCase						)  ->	Union[Dict, List]:
				with open(lowerCamelCase     ,    """r"""						) as f:
								return json.load(lowerCamelCase						)







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	None:
				with open(lowerCamelCase     ,    """w"""						) as f:
								json.dump(lowerCamelCase     ,    lowerCamelCase     ,    indent=2						)








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        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""laion/clap-htsat-unfused"""
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						ClapProcessor.from_pretrained(self.tmpdirname      )

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

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

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						ClapProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

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

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

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						floats_list((3, 1000)      )

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						processor(audios=snake_case_						,	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       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							str	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

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






