




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase   :			Optional[Any]          = logging.get_logger(__name__)

__UpperCamelCase   :			Optional[Any]          = {'vocab_file': 'sentencepiece.bpe.model'}

__UpperCamelCase   :			Union[str, Any]          = {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

__UpperCamelCase   :			Union[str, Any]          = {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

__UpperCamelCase   :			Union[str, Any]          = '▁'

class 						__lowerCAmelCase      (      _lowerCamelCase					):
							UpperCamelCase__   	=    VOCAB_FILES_NAMES
							UpperCamelCase__   	=    PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__   	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__   	=    ['''input_ids''', '''attention_mask''']
							def __init__(		self		:Any						, __magic_name__		:Optional[Any]						, __magic_name__		:int="<s>"						, __magic_name__		:Any="</s>"						, __magic_name__		:int="</s>"						, __magic_name__		:Any="<s>"						, __magic_name__		:List[Any]="<unk>"						, __magic_name__		:List[Any]="<pad>"						, __magic_name__		:Optional[Any]="<mask>"						, __magic_name__		:Optional[Dict[str, Any]] = None						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				AddedToken(_SCREAMING_SNAKE_CASE						, lstrip=_SCREAMING_SNAKE_CASE						, rstrip=_SCREAMING_SNAKE_CASE							) if isinstance(_SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE							) else mask_token

								a									=				{} if sp_model_kwargs is None else sp_model_kwargs

								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						, mask_token=_SCREAMING_SNAKE_CASE						, sp_model_kwargs=self.sp_model_kwargs						, **_SCREAMING_SNAKE_CASE						, )

								a									=				vocab_file
								a									=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(str(_SCREAMING_SNAKE_CASE							)							)

								a									=				{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

								a									=				len(self.sp_model							) - 1
								a									=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None							):







								'''simple docstring'''







								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								a									=				[self.cls_token_id]
								a									=				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None						, __magic_name__		:bool = False							):







								'''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							)) + [1]
								return [1] + ([0] * len(_SCREAMING_SNAKE_CASE							)) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE							)) + [1]
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None							):







								'''simple docstring'''







								a									=				[self.sep_token_id]
								a									=				[self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep							) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]
							@property
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								return len(self.sp_model							)
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								a									=				{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  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:str							):







								'''simple docstring'''







								return self.sp_model.encode(_SCREAMING_SNAKE_CASE						, out_type=_SCREAMING_SNAKE_CASE							)
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:int							):







								'''simple docstring'''







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

								return spm_id if spm_id else self.unk_token_id
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE							)
							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:Dict							):







								'''simple docstring'''







								a									=				[]
								a									=				''
								a									=				False
								for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
										if not prev_is_special:
											out_string += " "
										out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE							) + token
										a									=				True
										a									=				[]
									else:
										current_sub_tokens.append(_SCREAMING_SNAKE_CASE							)
										a									=				False
								out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE							)
								return out_string.strip()
							def __getstate__(		self		:Tuple							):







								'''simple docstring'''







								a									=				self.__dict__.copy()
								a									=				None
								return state
							def __setstate__(		self		:Any						, __magic_name__		:Any							):







								'''simple docstring'''







								a									=				d

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

								a									=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(self.vocab_file							)





							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:str						, __magic_name__		:Optional[str] = None							):







								'''simple docstring'''







								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							) 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:
										a									=				self.sp_model.serialized_model_proto()
										fi.write(_SCREAMING_SNAKE_CASE							)

								return (out_vocab_file,)






"""simple docstring"""

from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']





	@register_to_config
	def __init__(      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       int = 50_257     ,       _SCREAMING_SNAKE_CASE				:       int = 1_024     ,       _SCREAMING_SNAKE_CASE				:       int = 768     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       str = "gelu_new"     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 1E-5     ,       _SCREAMING_SNAKE_CASE				:       float = 0.0_2     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       )						->	Tuple:





					"""simple docstring"""


					super().__init__()

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
									raise ValueError(
									    f"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
									    f""" `n_embd`: {n_embd} are not equal."""    )

					SCREAMING_SNAKE_CASE						:							int							=     prefix_inner_dim
					SCREAMING_SNAKE_CASE						:							Dict							=     prefix_hidden_dim

					SCREAMING_SNAKE_CASE						:							int							=     (
					    nn.Linear(self.prefix_inner_dim     ,       self.prefix_hidden_dim    )
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     (
					    nn.Linear(self.prefix_hidden_dim     ,       _SCREAMING_SNAKE_CASE    ) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     GPTaConfig(
					    vocab_size=_SCREAMING_SNAKE_CASE     ,       n_positions=_SCREAMING_SNAKE_CASE     ,       n_embd=_SCREAMING_SNAKE_CASE     ,       n_layer=_SCREAMING_SNAKE_CASE     ,       n_head=_SCREAMING_SNAKE_CASE     ,       n_inner=_SCREAMING_SNAKE_CASE     ,       activation_function=_SCREAMING_SNAKE_CASE     ,       resid_pdrop=_SCREAMING_SNAKE_CASE     ,       embd_pdrop=_SCREAMING_SNAKE_CASE     ,       attn_pdrop=_SCREAMING_SNAKE_CASE     ,       layer_norm_epsilon=_SCREAMING_SNAKE_CASE     ,       initializer_range=_SCREAMING_SNAKE_CASE     ,       scale_attn_weights=_SCREAMING_SNAKE_CASE     ,       use_cache=_SCREAMING_SNAKE_CASE     ,       scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE     ,       reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     GPTaLMHeadModel(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     self.encode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     self.decode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.cat((prefix_embeds, embedding_text)     ,       dim=1    )

					if labels is not None:
									SCREAMING_SNAKE_CASE						:							Dict							=     self.get_dummy_token(input_ids.shape[0]     ,       input_ids.device    )
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.cat((dummy_token, input_ids)     ,       dim=1    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       torch.device    )						->	torch.Tensor:





					"""simple docstring"""


					return torch.zeros(_SCREAMING_SNAKE_CASE     ,       self.prefix_length     ,       dtype=torch.intaa     ,       device=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					return self.encode_prefix(_SCREAMING_SNAKE_CASE    )





	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.split(_SCREAMING_SNAKE_CASE     ,       1     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							int							=     []
					SCREAMING_SNAKE_CASE						:							str							=     []
					for feature in features:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.decode_prefix(feature.to(_SCREAMING_SNAKE_CASE    )    )  # back to the clip feature
									# Only support beam search for now
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     self.generate_beam(
									    input_embeds=_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE    )
									generated_tokens.append(output_tokens[0]    )
									generated_seq_lengths.append(seq_lengths[0]    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.stack(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.stack(_SCREAMING_SNAKE_CASE    )
					return generated_tokens, generated_seq_lengths







	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       int = 5     ,       _SCREAMING_SNAKE_CASE				:       int = 67     ,       _SCREAMING_SNAKE_CASE				:       float = 1.0     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     eos_token_id
					SCREAMING_SNAKE_CASE						:							Tuple							=     None
					SCREAMING_SNAKE_CASE						:							int							=     None
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.ones(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.int    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.zeros(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.bool    )

					if input_embeds is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     input_embeds
					else:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )

					for i in range(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							int							=     outputs.logits
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     logits.softmax(-1    ).log()

									if scores is None:
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     logits.topk(_SCREAMING_SNAKE_CASE     ,       -1    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     generated.expand(_SCREAMING_SNAKE_CASE     ,       *generated.shape[1:]    )
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     next_tokens.permute(1     ,       0    ), scores.squeeze(0    )
													if tokens is None:
																	SCREAMING_SNAKE_CASE						:							Optional[Any]							=     next_tokens
													else:
																	SCREAMING_SNAKE_CASE						:							List[Any]							=     tokens.expand(_SCREAMING_SNAKE_CASE     ,       *tokens.shape[1:]    )
																	SCREAMING_SNAKE_CASE						:							List[str]							=     torch.cat((tokens, next_tokens)     ,       dim=1    )
									else:
													SCREAMING_SNAKE_CASE						:							List[str]							=     -float(np.inf    )
													SCREAMING_SNAKE_CASE						:							int							=     0
													SCREAMING_SNAKE_CASE						:							Tuple							=     scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													SCREAMING_SNAKE_CASE						:							Any							=     scores_sum / seq_lengths[:, None]
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     scores_sum_average.view(-1    ).topk(_SCREAMING_SNAKE_CASE     ,       -1    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     next_tokens // scores_sum.shape[1]
													SCREAMING_SNAKE_CASE						:							Dict							=     seq_lengths[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							List[str]							=     next_tokens % scores_sum.shape[1]
													SCREAMING_SNAKE_CASE						:							int							=     next_tokens.unsqueeze(1    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     tokens[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.cat((tokens, next_tokens)     ,       dim=1    )
													SCREAMING_SNAKE_CASE						:							Any							=     generated[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							Any							=     scores_sum_average * seq_lengths
													SCREAMING_SNAKE_CASE						:							Tuple							=     is_stopped[next_tokens_source]

									SCREAMING_SNAKE_CASE						:							str							=     self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]     ,       1     ,       -1    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     torch.cat((generated, next_token_embed)     ,       dim=1    )
									SCREAMING_SNAKE_CASE						:							str							=     is_stopped + next_tokens.eq(_SCREAMING_SNAKE_CASE    ).squeeze()
									if is_stopped.all():
													break

					SCREAMING_SNAKE_CASE						:							Tuple							=     scores / seq_lengths
					SCREAMING_SNAKE_CASE						:							Tuple							=     scores.argsort(descending=_SCREAMING_SNAKE_CASE    )
					# tokens tensors are already padded to max_seq_length
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     [tokens[i] for i in order]
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.stack(_SCREAMING_SNAKE_CASE     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							int							=     torch.tensor([seq_lengths[i] for i in order]     ,       dtype=seq_lengths.dtype    )
					return output_texts, seq_lengths





