



"""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


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

__snake_case				:  int =       '▁'

__snake_case				:  int =       {'vocab_file': 'sentencepiece.bpe.model'}

__snake_case				:  Any =       {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case				:  List[str] =       {
    'facebook/xglm-564M': 2_048,
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE        =      PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE        =      ['input_ids', 'attention_mask']

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

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			{} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				__lowerCAmelCase :    Dict				=			7
				__lowerCAmelCase :    Tuple				=			[F"""<madeupword{i}>""" for i in range(self.num_madeup_words)]

				__lowerCAmelCase :    Optional[int]				=			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=_SCREAMING_SNAKE_CASE			,     eos_token=_SCREAMING_SNAKE_CASE			,     unk_token=_SCREAMING_SNAKE_CASE			,     sep_token=_SCREAMING_SNAKE_CASE			,     cls_token=_SCREAMING_SNAKE_CASE			,     pad_token=_SCREAMING_SNAKE_CASE			,     sp_model_kwargs=self.sp_model_kwargs			,     **_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    List[str]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(str(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase :    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
				__lowerCAmelCase :    int				=			1

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

				__lowerCAmelCase :    str				=			len(self.sp_model)
				__lowerCAmelCase :    int				=			{F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
				self.fairseq_tokens_to_ids.update(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

	def __getstate__(	self:						int)							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			self.__dict__.copy()
				__lowerCAmelCase :    int				=			None
				__lowerCAmelCase :    Any				=			self.sp_model.serialized_model_proto()
				return state

	def __setstate__(	self:						str			,     _SCREAMING_SNAKE_CASE:						List[Any])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			d

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

				__lowerCAmelCase :    Optional[Any]				=			spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

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

				"""simple docstring"""

				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				__lowerCAmelCase :    List[Any]				=			[self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a

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

				"""simple docstring"""

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

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

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

				"""simple docstring"""

				__lowerCAmelCase :    int				=			[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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			str:

				"""simple docstring"""

				return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
				vocab.update(self.added_tokens_encoder)
				return vocab

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						Any)							->			Tuple:

				"""simple docstring"""

				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				__lowerCAmelCase :    Optional[Any]				=			self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE)

				# 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 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Union[str, Any]:

				"""simple docstring"""

				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 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			"".join(_SCREAMING_SNAKE_CASE).replace(_SCREAMING_SNAKE_CASE			,     " ").strip()
				return out_string







	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Optional[str] = None)							->			Tuple[str]:

				"""simple docstring"""

				if not os.path.isdir(_SCREAMING_SNAKE_CASE):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
							return
				__lowerCAmelCase :    Union[str, Any]				=			os.path.join(
				    _SCREAMING_SNAKE_CASE			,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

				return (out_vocab_file,)




"""simple docstring"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@register_to_config
	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = False			,     )							->			Any:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase :    str				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			False

				__lowerCAmelCase :    Optional[Any]				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaConfig(
				    vocab_size=_SCREAMING_SNAKE_CASE			,     d_model=_SCREAMING_SNAKE_CASE			,     num_heads=_SCREAMING_SNAKE_CASE			,     d_kv=_SCREAMING_SNAKE_CASE			,     d_ff=_SCREAMING_SNAKE_CASE			,     dropout_rate=_SCREAMING_SNAKE_CASE			,     feed_forward_proj=_SCREAMING_SNAKE_CASE			,     is_decoder=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    str				=			nn.ModuleList()
				for lyr_num in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			TaBlock(_SCREAMING_SNAKE_CASE)
							self.encoders.append(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.token_embedder(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			encoder_input_tokens.shape[1]
				__lowerCAmelCase :    List[Any]				=			torch.arange(_SCREAMING_SNAKE_CASE			,     device=encoder_input_tokens.device)
				x += self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			self.dropout_pre(_SCREAMING_SNAKE_CASE)

				# inverted the attention mask
				__lowerCAmelCase :    List[Any]				=			encoder_input_tokens.size()
				__lowerCAmelCase :    Any				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				for lyr in self.encoders:
							__lowerCAmelCase :    Union[str, Any]				=			lyr(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    int				=			self.layer_norm(_SCREAMING_SNAKE_CASE)

				return self.dropout_post(_SCREAMING_SNAKE_CASE), encoder_inputs_mask