





import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		def __init__(							self,   snake_case__ = "▁",   snake_case__ = True,   snake_case__ = "<unk>",   snake_case__ = "</s>",   snake_case__ = "<pad>",   )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     {
									    """pad""": {"""id""": 0, """token""": pad_token},
									    """eos""": {"""id""": 1, """token""": eos_token},
									    """unk""": {"""id""": 2, """token""": unk_token},
									}

									lowercase_	:   List[str]  				=     [None] * len(self.special_tokens  )
									for token_dict in self.special_tokens.values():
																lowercase_	:   Union[str, Any]  				=     token_dict["""token"""]

									lowercase_	:   Optional[int]  				=     Tokenizer(Unigram()  )

									lowercase_	:   List[str]  				=     normalizers.Sequence(
									    [
									        normalizers.Nmt(),
									        normalizers.NFKC(),
									        normalizers.Replace(Regex(""" {2,}"""  ),   """ """  ),
									        normalizers.Lowercase(),
									    ]  )
									lowercase_	:   Any  				=     pre_tokenizers.Sequence(
									    [
									        pre_tokenizers.Metaspace(replacement=snake_case__,   add_prefix_space=snake_case__  ),
									        pre_tokenizers.Digits(individual_digits=snake_case__  ),
									        pre_tokenizers.Punctuation(),
									    ]  )
									lowercase_	:   str  				=     decoders.Metaspace(replacement=snake_case__,   add_prefix_space=snake_case__  )

									lowercase_	:   Tuple  				=     TemplateProcessing(
									    single=f"""$A {self.special_tokens["eos"]["token"]}""",   special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])],   )

									lowercase_	:   Dict  				=     {
									    """model""": """SentencePieceUnigram""",
									    """replacement""": replacement,
									    """add_prefix_space""": add_prefix_space,
									}

									super().__init__(snake_case__,   snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = 80_00,   snake_case__ = True,   )       -> Any:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     trainers.UnigramTrainer(
									    vocab_size=snake_case__,   special_tokens=self.special_tokens_list,   show_progress=snake_case__,   )

									if isinstance(snake_case__,   snake_case__  ):
																lowercase_	:   Any  				=     [files]
									self._tokenizer.train(snake_case__,   trainer=snake_case__  )

									self.add_unk_id()






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = 80_00,   snake_case__ = True,   )       -> int:






									"""simple docstring"""
									lowercase_	:   str  				=     trainers.UnigramTrainer(
									    vocab_size=snake_case__,   special_tokens=self.special_tokens_list,   show_progress=snake_case__,   )

									self._tokenizer.train_from_iterator(snake_case__,   trainer=snake_case__  )

									self.add_unk_id()






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   str  				=     json.loads(self._tokenizer.to_str()  )

									lowercase_	:   Union[str, Any]  				=     self.special_tokens["""unk"""]["""id"""]

									lowercase_	:   Dict  				=     Tokenizer.from_str(json.dumps(snake_case__  )  )






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__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = False,   )       -> Any:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Embedding(snake_case__,   snake_case__  )

									lowercase_	:   Dict  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   List[str]  				=     False

									lowercase_	:   Optional[int]  				=     nn.Dropout(p=snake_case__  )

									lowercase_	:   Any  				=     TaConfig(
									    vocab_size=snake_case__,   d_model=snake_case__,   num_heads=snake_case__,   d_kv=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__,   feed_forward_proj=snake_case__,   is_decoder=snake_case__,   is_encoder_decoder=snake_case__,   )

									lowercase_	:   List[Any]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																lowercase_	:   List[str]  				=     TaBlock(snake_case__  )
																self.encoders.append(snake_case__  )

									lowercase_	:   Tuple  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(p=snake_case__  )






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






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.token_embedder(snake_case__  )

									lowercase_	:   Tuple  				=     encoder_input_tokens.shape[1]
									lowercase_	:   List[str]  				=     torch.arange(snake_case__,   device=encoder_input_tokens.device  )
									x += self.position_encoding(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     self.dropout_pre(snake_case__  )

									# inverted the attention mask
									lowercase_	:   List[str]  				=     encoder_input_tokens.size()
									lowercase_	:   Union[str, Any]  				=     self.get_extended_attention_mask(snake_case__,   snake_case__  )

									for lyr in self.encoders:
																lowercase_	:   Optional[Any]  				=     lyr(snake_case__,   snake_case__  )[0]
									lowercase_	:   Dict  				=     self.layer_norm(snake_case__  )

									return self.dropout_post(snake_case__  ), encoder_inputs_mask