

"""simple docstring"""




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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xglm import XGLMTokenizer
else:
	__A     :    Dict    =		None


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

__A     :    Any    =		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

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

__A     :    Tuple    =		{
    'facebook/xglm-564M': 20_48,
}





class 			__UpperCamelCase (   _A						):
		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"]
		SCREAMING_SNAKE_CASE								=    XGLMTokenizer


		def __init__(self :		Dict     ,     __SCREAMING_SNAKE_CASE :		str=None     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=None     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]="<s>"     ,     __SCREAMING_SNAKE_CASE :		List[Any]="</s>"     ,     __SCREAMING_SNAKE_CASE :		int="</s>"     ,     __SCREAMING_SNAKE_CASE :		Any="<s>"     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]="<unk>"     ,     __SCREAMING_SNAKE_CASE :		Dict="<pad>"     ,     **__SCREAMING_SNAKE_CASE :		Optional[int]     ,     ):
							# Compatibility with the original tokenizer
							A			=					7
							A			=					[F"""<madeupword{i}>""" for i in range(self.num_madeup_words)]

							A			=					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__(
							    __SCREAMING_SNAKE_CASE     ,     tokenizer_file=__SCREAMING_SNAKE_CASE     ,     bos_token=__SCREAMING_SNAKE_CASE     ,     eos_token=__SCREAMING_SNAKE_CASE     ,     sep_token=__SCREAMING_SNAKE_CASE     ,     cls_token=__SCREAMING_SNAKE_CASE     ,     unk_token=__SCREAMING_SNAKE_CASE     ,     pad_token=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE     ,     )

							A			=					vocab_file
							A			=					False if not self.vocab_file else True


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		List[int]     ,     __SCREAMING_SNAKE_CASE :		Optional[List[int]] = None):

							if token_ids_a is None:
												return [self.sep_token_id] + token_ids_a
							A			=					[self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     __SCREAMING_SNAKE_CASE :		List[int]     ,     __SCREAMING_SNAKE_CASE :		Optional[List[int]] = None):
							A			=					[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]


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		Optional[str] = None):
							if not self.can_save_slow_tokenizer:
												raise ValueError(
												    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
												    "tokenizer.")

							if not os.path.isdir(__SCREAMING_SNAKE_CASE):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""")
												return
							A			=					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):
												copyfile(self.vocab_file     ,     __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 			__UpperCamelCase (   _A     ,		_A     ,		_A						):


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __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     ,     ):
							super().__init__()

							A			=					nn.Linear(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     bias=__SCREAMING_SNAKE_CASE)

							A			=					nn.Embedding(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					False

							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)

							A			=					TaConfig(
							    d_model=__SCREAMING_SNAKE_CASE     ,     num_heads=__SCREAMING_SNAKE_CASE     ,     d_kv=__SCREAMING_SNAKE_CASE     ,     d_ff=__SCREAMING_SNAKE_CASE     ,     feed_forward_proj=__SCREAMING_SNAKE_CASE     ,     dropout_rate=__SCREAMING_SNAKE_CASE     ,     is_decoder=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     )
							A			=					nn.ModuleList()
							for lyr_num in range(__SCREAMING_SNAKE_CASE):
												A			=					TaBlock(__SCREAMING_SNAKE_CASE)
												self.encoders.append(__SCREAMING_SNAKE_CASE)

							A			=					TaLayerNorm(__SCREAMING_SNAKE_CASE)
							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					self.input_proj(__SCREAMING_SNAKE_CASE)

							# terminal relative positional encodings
							A			=					encoder_inputs.shape[1]
							A			=					torch.arange(__SCREAMING_SNAKE_CASE     ,     device=encoder_inputs.device)

							A			=					encoder_inputs_mask.sum(-1)
							A			=					torch.roll(input_positions.unsqueeze(0)     ,     tuple(seq_lens.tolist())     ,     dims=0)
							x += self.position_encoding(__SCREAMING_SNAKE_CASE)

							A			=					self.dropout_pre(__SCREAMING_SNAKE_CASE)

							# inverted the attention mask
							A			=					encoder_inputs.size()
							A			=					self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							for lyr in self.encoders:
												A			=					lyr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)[0]
							A			=					self.layer_norm(__SCREAMING_SNAKE_CASE)

							return self.dropout_post(__SCREAMING_SNAKE_CASE), encoder_inputs_mask





