



"""simple docstring"""






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


_lowercase			:			List[Any]										=						logging.get_logger(__name__)

_lowercase			:			List[str]										=						"▁"

_lowercase			:			Optional[Any]										=						{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

_lowercase			:			Optional[int]										=						{
    "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"
        )
    },
}

_lowercase			:			str										=						{
    "facebook/s2t-small-librispeech-asr": 1_0_2_4,
}

_lowercase			:			List[Any]										=						["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

_lowercase			:			Union[str, Any]										=						{"mustc": MUSTC_LANGS}

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							PRETRAINED_VOCAB_FILES_MAP
					_a             =							MAX_MODEL_INPUT_SIZES
					_a             =							['input_ids', 'attention_mask']

					_a             =							[]



					def __init__(	self							:  Optional[Any],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  int="<s>",  lowerCamelCase							:  List[str]="</s>",  lowerCamelCase							:  Any="<pad>",  lowerCamelCase							:  List[str]="<unk>",  lowerCamelCase							:  Optional[Any]=False,  lowerCamelCase							:  Tuple=False,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Dict,  )-> None:
								lowerCamelCase__		:		Optional[int]		     ={} 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,  )
								lowerCamelCase__		:		Any		     =do_upper_case
								lowerCamelCase__		:		Optional[int]		     =do_lower_case

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

								if lang_codes is not None:
											lowerCamelCase__		:		str		     =lang_codes
											lowerCamelCase__		:		Union[str, Any]		     =LANGUAGES[lang_codes]
											lowerCamelCase__		:		Optional[int]		     =[F'''<lang:{lang}>''' for lang in self.langs]
											lowerCamelCase__		:		Any		     ={lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''					) for lang in self.langs}

											lowerCamelCase__		:		Tuple		     =self.lang_tokens
											lowerCamelCase__		:		str		     =tgt_lang if tgt_lang is not None else self.langs[0]

											self.set_tgt_lang_special_tokens(self._tgt_lang					)
								else:
											lowerCamelCase__		:		Any		     ={}



					@property
					def 		snake_case	(	self							:  int					)-> int:
								return len(self.encoder					)



					@property
					def 		snake_case	(	self							:  List[Any]					)-> str:
								return self._tgt_lang



					@tgt_lang.setter
					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  Union[str, Any]					)-> None:
								lowerCamelCase__		:		Union[str, Any]		     =new_tgt_lang
								self.set_tgt_lang_special_tokens(lowerCamelCase					)



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  str					)-> None:
								lowerCamelCase__		:		Tuple		     =self.lang_code_to_id[tgt_lang]
								lowerCamelCase__		:		Union[str, Any]		     =[lang_code_id]



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  str					)-> List[str]:
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase					)



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  Tuple					)-> str:
								return self.encoder.get(lowerCamelCase,  self.encoder[self.unk_token]					)



					def 		snake_case	(	self							:  Any,  lowerCamelCase							:  int					)-> str:
								return self.decoder.get(lowerCamelCase,  self.unk_token					)



					def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  List[str]					)-> str:
								lowerCamelCase__		:		List[Any]		     =[]
								lowerCamelCase__		:		Tuple		     =''''''
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														lowerCamelCase__		:		Tuple		     =self.sp_model.decode(lowerCamelCase					)
														out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
														lowerCamelCase__		:		Dict		     =[]
											else:
														current_sub_tokens.append(lowerCamelCase					)
								lowerCamelCase__		:		int		     =self.sp_model.decode(lowerCamelCase					)
								out_string += decoded.upper() if self.do_upper_case else decoded
								return out_string.strip()



					def 		snake_case	(	self							:  Tuple,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[Any]=None					)-> List[int]:
								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							:  List[Any],  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None,  lowerCamelCase							:  bool = False					)-> List[int]:

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

								lowerCamelCase__		:		Optional[Any]		     =[1] * len(self.prefix_tokens					)
								lowerCamelCase__		:		Tuple		     =[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							:  Optional[int]					)-> Dict:
								lowerCamelCase__		:		Union[str, Any]		     =self.encoder.copy()
								vocab.update(self.added_tokens_encoder					)
								return vocab



					def __getstate__(	self							:  List[str]					)-> Dict:
								lowerCamelCase__		:		Tuple		     =self.__dict__.copy()
								lowerCamelCase__		:		Any		     =None
								return state



					def __setstate__(	self							:  List[str],  lowerCamelCase							:  Dict					)-> None:
								lowerCamelCase__		:		Union[str, Any]		     =d

								# for backward compatibility
								if not hasattr(self,  '''sp_model_kwargs'''					):
											lowerCamelCase__		:		Optional[Any]		     ={}

								lowerCamelCase__		:		int		     =load_spm(self.spm_file,  self.sp_model_kwargs					)




					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None					)-> Tuple[str]:
								lowerCamelCase__		:		Optional[int]		     =Path(lowerCamelCase					)
								assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
								lowerCamelCase__		:		Tuple		     =save_dir / (
								    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
								)
								lowerCamelCase__		:		List[str]		     =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:
														lowerCamelCase__		:		Union[str, Any]		     =self.sp_model.serialized_model_proto()
														fi.write(lowerCamelCase					)

								return (str(lowerCamelCase					), str(lowerCamelCase					))






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   Dict[str, Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Optional[int]		     =sentencepiece.SentencePieceProcessor(**__lowerCamelCase     )
			spm.Load(str(__lowerCamelCase     )     )
			return spm






def 					snake_case__       (		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''r'''     ) as f:
						return json.load(__lowerCamelCase     )






def 					snake_case__       (		__lowerCamelCase    :   Dict							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			with open(__lowerCamelCase							,		'''w'''     ) as f:
						json.dump(__lowerCamelCase							,		__lowerCamelCase							,		indent=2     )









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowercase			:			Any										=						{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase			:			List[Any]										=						[
		    "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TrOCRForCausalLM",
		    "TrOCRPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
		from .processing_trocr import TrOCRProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
		import sys

		_lowercase			:			Tuple										=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




