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 PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase					:   Tuple							=		logging.get_logger(__name__)

_lowerCamelCase					:   List[str]							=		'''▁'''

_lowerCamelCase					:   Optional[int]							=		{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

_lowerCamelCase					:   Optional[Any]							=		{
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

_lowerCamelCase					:   Tuple							=		{
    '''facebook/s2t-small-librispeech-asr''': 10_24,
}

_lowerCamelCase					:   Optional[int]							=		['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

_lowerCamelCase					:   str							=		{'''mustc''': MUSTC_LANGS}


class 						lowercase    (     a__     ):
							lowercase__						:       Optional[Any]								=							VOCAB_FILES_NAMES
							lowercase__						:       List[Any]								=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__						:       List[Any]								=							MAX_MODEL_INPUT_SIZES
							lowercase__						:       List[str]								=							["""input_ids""", """attention_mask"""]

							lowercase__						:       List[int]								=							[]




							def __init__(						self   :					Optional[Any]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[str]="<s>"	,							_UpperCamelCase   :					Union[str, Any]="</s>"	,							_UpperCamelCase   :					Optional[int]="<pad>"	,							_UpperCamelCase   :					Dict="<unk>"	,							_UpperCamelCase   :					Optional[int]=False	,							_UpperCamelCase   :					Optional[int]=False	,							_UpperCamelCase   :					Tuple=None	,							_UpperCamelCase   :					int=None	,							_UpperCamelCase   :					Optional[Any] = None	,							**_UpperCamelCase   :					List[Any]	,							)					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=lowerCAmelCase__	,							eos_token=lowerCAmelCase__	,							unk_token=lowerCAmelCase__	,							pad_token=lowerCAmelCase__	,							do_upper_case=lowerCAmelCase__	,							do_lower_case=lowerCAmelCase__	,							tgt_lang=lowerCAmelCase__	,							lang_codes=lowerCAmelCase__	,							sp_model_kwargs=self.sp_model_kwargs	,							**lowerCAmelCase__	,							)
												SCREAMING_SNAKE_CASE    			=       do_upper_case
												SCREAMING_SNAKE_CASE    			=       do_lower_case

												SCREAMING_SNAKE_CASE    			=       load_json(lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE    			=       {v: k for k, v in self.encoder.items()}
												SCREAMING_SNAKE_CASE    			=       spm_file
												SCREAMING_SNAKE_CASE    			=       load_spm(lowerCAmelCase__	,							self.sp_model_kwargs      )

												if lang_codes is not None:
																	SCREAMING_SNAKE_CASE    			=       lang_codes
																	SCREAMING_SNAKE_CASE    			=       LANGUAGES[lang_codes]
																	SCREAMING_SNAKE_CASE    			=       [F"<lang:{lang}>" for lang in self.langs]
																	SCREAMING_SNAKE_CASE    			=       {lang: self.sp_model.PieceToId(F"<lang:{lang}>"      ) for lang in self.langs}

																	SCREAMING_SNAKE_CASE    			=       self.lang_tokens
																	SCREAMING_SNAKE_CASE    			=       tgt_lang if tgt_lang is not None else self.langs[0]

																	self.set_tgt_lang_special_tokens(self._tgt_lang      )
												else:
																	SCREAMING_SNAKE_CASE    			=       {}




							@property
							def 	__snake_case(						self   :					int      )					->				int:



												'''simple docstring'''

												return len(self.encoder      )




							@property
							def 	__snake_case(						self   :					Optional[Any]      )					->				str:



												'''simple docstring'''

												return self._tgt_lang




							@tgt_lang.setter
							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					List[Any]      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       new_tgt_lang
												self.set_tgt_lang_special_tokens(lowerCAmelCase__      )




							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					Dict      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.lang_code_to_id[tgt_lang]
												SCREAMING_SNAKE_CASE    			=       [lang_code_id]




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					int      )					->				List[str]:



												'''simple docstring'''

												return self.sp_model.encode(lowerCAmelCase__	,							out_type=lowerCAmelCase__      )




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Any      )					->				Any:



												'''simple docstring'''

												return self.encoder.get(lowerCAmelCase__	,							self.encoder[self.unk_token]      )




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					Optional[int]      )					->				str:



												'''simple docstring'''

												return self.decoder.get(lowerCAmelCase__	,							self.unk_token      )




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					Any      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       []
												SCREAMING_SNAKE_CASE    			=       ""
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						SCREAMING_SNAKE_CASE    			=       self.sp_model.decode(lowerCAmelCase__      )
																						out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
																						SCREAMING_SNAKE_CASE    			=       []
																	else:
																						current_sub_tokens.append(lowerCAmelCase__      )
												SCREAMING_SNAKE_CASE    			=       self.sp_model.decode(lowerCAmelCase__      )
												out_string += decoded.upper() if self.do_upper_case else decoded
												return out_string.strip()




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					Optional[int]=None      )					->				List[int]:



												'''simple docstring'''

												if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + [self.eos_token_id]
												# 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.eos_token_id]




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					Tuple	,							_UpperCamelCase   :					Any = None	,							_UpperCamelCase   :					Any = False      )					->				List[int]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       [1] * len(self.prefix_tokens      )
												SCREAMING_SNAKE_CASE    			=       [1]
												if token_ids_a is None:
																	return prefix_ones + ([0] * len(lowerCAmelCase__      )) + suffix_ones
												return prefix_ones + ([0] * len(lowerCAmelCase__      )) + ([0] * len(lowerCAmelCase__      )) + suffix_ones




							def 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.encoder.copy()
												vocab.update(self.added_tokens_encoder      )
												return vocab




							def __getstate__(						self   :					Tuple      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.__dict__.copy()
												SCREAMING_SNAKE_CASE    			=       None
												return state




							def __setstate__(						self   :					Dict	,							_UpperCamelCase   :					str      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       d

												# for backward compatibility
												if not hasattr(self	,							"sp_model_kwargs"      ):
																	SCREAMING_SNAKE_CASE    			=       {}

												SCREAMING_SNAKE_CASE    			=       load_spm(self.spm_file	,							self.sp_model_kwargs      )





							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Union[str, Any] = None      )					->				Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       Path(lowerCAmelCase__      )
												assert save_dir.is_dir(), F"{save_directory} should be a directory"
												SCREAMING_SNAKE_CASE    			=       save_dir / (
												    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
												)
												SCREAMING_SNAKE_CASE    			=       save_dir / (
												    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
												)

												save_json(self.encoder	,							lowerCAmelCase__      )

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

												return (str(lowerCAmelCase__      ), str(lowerCAmelCase__      ))





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Dict[str, Any]  ):
					SCREAMING_SNAKE_CASE    			=       sentencepiece.SentencePieceProcessor(**_lowercase  )
					spm.Load(str(_lowercase  )  )
					return spm





def 	__lowerCamelCase							(UpperCAmelCase__     :							str  ):






					with open(_lowercase      , "r"  ) as f:
										return json.load(_lowercase  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							str  ):






					with open(_lowercase      , "w"  ) as f:
										json.dump(_lowercase      , _lowercase      , indent=2  )





# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


_lowerCamelCase					:   List[Any]							=		'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
							import torch

							print('''Torch version:''', torch.__version__)
							print('''Cuda available:''', torch.cuda.is_available())
							print('''Cuda version:''', torch.version.cuda)
							print('''CuDNN version:''', torch.backends.cudnn.version())
							print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
							print('''Torch version:''', None)

try:
							import transformers

							print('''transformers version:''', transformers.__version__)
except ImportError:
							print('''transformers version:''', None)




