"""simple docstring"""







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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

_a  :  List[Any]=				"▁"

_a  :  List[str]=				{"vocab_file": "sentencepiece.bpe.model"}

_a  :  Dict=				{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

_a  :  Union[str, Any]=				{
    "facebook/xglm-564M": 2_048,
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   int					=	VOCAB_FILES_NAMES
				UpperCAmelCase       :   Dict					=	PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase       :   Optional[Any]					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase       :   str					=	["""input_ids""", """attention_mask"""]



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

					# Compatibility with the original tokenizer
					__snake_case    :							int   	=				7
					__snake_case    :							List[Any]   	=				[f"<madeupword{i}>" for i in range(self.num_madeup_words)]

					__snake_case    :							Optional[Any]   	=				kwargs.get('additional_special_tokens'			,						[])
					kwargs["additional_special_tokens"] += [
					    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    bos_token=_A			,						eos_token=_A			,						unk_token=_A			,						sep_token=_A			,						cls_token=_A			,						pad_token=_A			,						sp_model_kwargs=self.sp_model_kwargs			,						**_A			,						)

					__snake_case    :							Any   	=				spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(str(_A))
					__snake_case    :							Optional[int]   	=				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'

					# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
					__snake_case    :							Union[str, Any]   	=				1

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

					__snake_case    :							Union[str, Any]   	=				len(self.sp_model)
					__snake_case    :							List[Any]   	=				{f"<madeupword{i}>": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
					self.fairseq_tokens_to_ids.update(_A)

					__snake_case    :							Dict   	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



				def __getstate__(self       :   str)					->			List[str]:
					__snake_case    :							List[Any]   	=				self.__dict__.copy()
					__snake_case    :							Optional[Any]   	=				None
					__snake_case    :							List[Any]   	=				self.sp_model.serialized_model_proto()
					return state



				def __setstate__(self       :   str			,						_A       :   Tuple)					->			Optional[int]:
					__snake_case    :							Optional[Any]   	=				d

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

					__snake_case    :							Tuple   	=				spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto)



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

					if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
					__snake_case    :							Optional[Any]   	=				[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a



				def       _lowercase  (self       :   int			,						_A       :   List[int]			,						_A       :   Optional[List[int]] = None			,						_A       :   bool = False)					->			List[int]:

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

					if token_ids_a is None:
						return [1] + ([0] * len(_A))
					return [1] + ([0] * len(_A)) + [1, 1] + ([0] * len(_A))



				def       _lowercase  (self       :   Tuple			,						_A       :   List[int]			,						_A       :   Optional[List[int]] = None)					->			List[int]:
					__snake_case    :							Union[str, Any]   	=				[self.sep_token_id]

					if token_ids_a is None:
						return len(sep + token_ids_a) * [0]
					return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]



				@property
				def       _lowercase  (self       :   Union[str, Any])					->			Tuple:
					return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words



				def       _lowercase  (self       :   Union[str, Any])					->			List[str]:
					__snake_case    :							int   	=				{self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab



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



				def       _lowercase  (self       :   List[Any]			,						_A       :   Dict)					->			int:
					if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
					__snake_case    :							List[Any]   	=				self.sp_model.PieceToId(_A)

					# 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       _lowercase  (self       :   Tuple			,						_A       :   Tuple)					->			Dict:
					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       _lowercase  (self       :   List[Any]			,						_A       :   Union[str, Any])					->			Union[str, Any]:
					__snake_case    :							int   	=				''.join(_A).replace(_A			,						' ').strip()
					return out_string






				def       _lowercase  (self       :   List[Any]			,						_A       :   str			,						_A       :   Optional[str] = None)					->			Tuple[str]:
					if not os.path.isdir(_A):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
					__snake_case    :							Tuple   	=				os.path.join(
					    _A			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

					if os.path.abspath(self.vocab_file) != os.path.abspath(_A) and os.path.isfile(self.vocab_file):
						copyfile(self.vocab_file			,						_A)
					elif not os.path.isfile(self.vocab_file):
						with open(_A			,						'wb') as fi:
							__snake_case    :							Union[str, Any]   	=				self.sp_model.serialized_model_proto()
							fi.write(_A)

					return (out_vocab_file,)






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a  :  Any=				{"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Union[str, Any]=				["SpeechEncoderDecoderModel"]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	_a  :  str=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





