"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


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


lowerCAmelCase__						:						int		  =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase__						:						Any		  =			{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__						:						List[str]		  =			{
    'facebook/bart-base': 1_024,
    'facebook/bart-large': 1_024,
    'facebook/bart-large-mnli': 1_024,
    'facebook/bart-large-cnn': 1_024,
    'facebook/bart-large-xsum': 1_024,
    'yjernite/bart_eli5': 1_024,
}
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				VOCAB_FILES_NAMES
					snake_case__       					=				PRETRAINED_VOCAB_FILES_MAP
					snake_case__       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__       					=				["input_ids", "attention_mask"]
					snake_case__       					=				BartTokenizer







					def __init__(       self     :			Any					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Any=None					,lowerCamelCase__     :			Dict="replace"					,lowerCamelCase__     :			Tuple="<s>"					,lowerCamelCase__     :			List[str]="</s>"					,lowerCamelCase__     :			Union[str, Any]="</s>"					,lowerCamelCase__     :			Union[str, Any]="<s>"					,lowerCamelCase__     :			Optional[Any]="<unk>"					,lowerCamelCase__     :			str="<pad>"					,lowerCamelCase__     :			List[str]="<mask>"					,lowerCamelCase__     :			List[str]=False					,lowerCamelCase__     :			List[Any]=True					,**lowerCamelCase__     :			List[str]					,):
									super().__init__(
									    lowerCamelCase__					,lowerCamelCase__					,tokenizer_file=lowerCamelCase__					,errors=lowerCamelCase__					,bos_token=lowerCamelCase__					,eos_token=lowerCamelCase__					,sep_token=lowerCamelCase__					,cls_token=lowerCamelCase__					,unk_token=lowerCamelCase__					,pad_token=lowerCamelCase__					,mask_token=lowerCamelCase__					,add_prefix_space=lowerCamelCase__					,trim_offsets=lowerCamelCase__					,**lowerCamelCase__					,)

									UpperCAmelCase__    =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
									if pre_tok_state.get('add_prefix_space'					,lowerCamelCase__    ) != add_prefix_space:
													UpperCAmelCase__    =							getattr(lowerCamelCase__					,pre_tok_state.pop('type'    )    )
													UpperCAmelCase__    =							add_prefix_space
													UpperCAmelCase__    =							pre_tok_class(**lowerCamelCase__    )

									UpperCAmelCase__    =							add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									UpperCAmelCase__    =							'post_processor'
									UpperCAmelCase__    =							getattr(self.backend_tokenizer					,lowerCamelCase__					,lowerCamelCase__    )
									if tokenizer_component_instance:
													UpperCAmelCase__    =							json.loads(tokenizer_component_instance.__getstate__()    )

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																	UpperCAmelCase__    =							tuple(state['sep']    )
													if "cls" in state:
																	UpperCAmelCase__    =							tuple(state['cls']    )

													UpperCAmelCase__    =							False

													if state.get('add_prefix_space'					,lowerCamelCase__    ) != add_prefix_space:
																	UpperCAmelCase__    =							add_prefix_space
																	UpperCAmelCase__    =							True

													if state.get('trim_offsets'					,lowerCamelCase__    ) != trim_offsets:
																	UpperCAmelCase__    =							trim_offsets
																	UpperCAmelCase__    =							True

													if changes_to_apply:
																	UpperCAmelCase__    =							getattr(lowerCamelCase__					,state.pop('type'    )    )
																	UpperCAmelCase__    =							component_class(**lowerCamelCase__    )
																	setattr(self.backend_tokenizer					,lowerCamelCase__					,lowerCamelCase__    )







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									if self._mask_token is None:
													if self.verbose:
																	logger.error('Using mask_token, but it is not set yet.'    )
													return None
									return str(self._mask_token    )







					@mask_token.setter
					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							AddedToken(lowerCamelCase__					,lstrip=lowerCamelCase__					,rstrip=lowerCamelCase__    ) if isinstance(lowerCamelCase__					,lowerCamelCase__    ) else value
									UpperCAmelCase__    =							value







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,*lowerCamelCase__     :			Any					,**lowerCamelCase__     :			Tuple    ):
									UpperCAmelCase__    =							kwargs.get('is_split_into_words'					,lowerCamelCase__    )

									if is_split_into_words and not self.add_prefix_space:
													raise ValueError(
													    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
													    'to use it with pretokenized inputs.'    )

									return super()._batch_encode_plus(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any					,*lowerCamelCase__     :			Optional[Any]					,**lowerCamelCase__     :			List[Any]    ):
									UpperCAmelCase__    =							kwargs.get('is_split_into_words'					,lowerCamelCase__    )

									if is_split_into_words and not self.add_prefix_space:
													raise ValueError(
													    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
													    'to use it with pretokenized inputs.'    )

									return super()._encode_plus(*lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[str] = None    ):
									UpperCAmelCase__    =							self._tokenizer.model.save(lowerCamelCase__					,name=lowerCamelCase__    )
									return tuple(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Union[str, Any]=None    ):
									UpperCAmelCase__    =							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
													return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			List[int]					,lowerCamelCase__     :			Optional[List[int]] = None    ):
									UpperCAmelCase__    =							[self.sep_token_id]
									UpperCAmelCase__    =							[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]




"""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        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







					@register_to_config
					def __init__(       self     :			str					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			int = 50_257					,lowerCamelCase__     :			int = 1_024					,lowerCamelCase__     :			int = 768					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			str = "gelu_new"					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 1e-5					,lowerCamelCase__     :			float = 0.0_2					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			bool = False					,):
									super().__init__()

									UpperCAmelCase__    =							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.'''    )

									UpperCAmelCase__    =							prefix_inner_dim
									UpperCAmelCase__    =							prefix_hidden_dim

									UpperCAmelCase__    =							(
									    nn.Linear(self.prefix_inner_dim					,self.prefix_hidden_dim    )
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									UpperCAmelCase__    =							(
									    nn.Linear(self.prefix_hidden_dim					,lowerCamelCase__    ) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									UpperCAmelCase__    =							GPTaConfig(
									    vocab_size=lowerCamelCase__					,n_positions=lowerCamelCase__					,n_embd=lowerCamelCase__					,n_layer=lowerCamelCase__					,n_head=lowerCamelCase__					,n_inner=lowerCamelCase__					,activation_function=lowerCamelCase__					,resid_pdrop=lowerCamelCase__					,embd_pdrop=lowerCamelCase__					,attn_pdrop=lowerCamelCase__					,layer_norm_epsilon=lowerCamelCase__					,initializer_range=lowerCamelCase__					,scale_attn_weights=lowerCamelCase__					,use_cache=lowerCamelCase__					,scale_attn_by_inverse_layer_idx=lowerCamelCase__					,reorder_and_upcast_attn=lowerCamelCase__					,)
									UpperCAmelCase__    =							GPTaLMHeadModel(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,):
									UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )
									UpperCAmelCase__    =							self.encode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							self.decode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.cat((prefix_embeds, embedding_text)					,dim=1    )

									if labels is not None:
													UpperCAmelCase__    =							self.get_dummy_token(input_ids.shape[0]					,input_ids.device    )
													UpperCAmelCase__    =							torch.cat((dummy_token, input_ids)					,dim=1    )
									UpperCAmelCase__    =							self.transformer(inputs_embeds=lowerCamelCase__					,labels=lowerCamelCase__					,attention_mask=lowerCamelCase__    )
									if self.prefix_hidden_dim is not None:
													return out, hidden
									else:
													return out







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			torch.device    ):
									return torch.zeros(lowerCamelCase__					,self.prefix_length					,dtype=torch.intaa					,device=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]    ):
									return self.encode_prefix(lowerCamelCase__    )







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







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			int = 5					,lowerCamelCase__     :			int = 67					,lowerCamelCase__     :			float = 1.0					,lowerCamelCase__     :			Optional[int] = None					,):
									UpperCAmelCase__    =							eos_token_id
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							torch.ones(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.int    )
									UpperCAmelCase__    =							torch.zeros(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.bool    )

									if input_embeds is not None:
													UpperCAmelCase__    =							input_embeds
									else:
													UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )

									for i in range(lowerCamelCase__    ):
													UpperCAmelCase__    =							self.transformer(inputs_embeds=lowerCamelCase__    )
													UpperCAmelCase__    =							outputs.logits
													UpperCAmelCase__    =							logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
													UpperCAmelCase__    =							logits.softmax(-1    ).log()

													if scores is None:
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							logits.topk(lowerCamelCase__					,-1    )
																	UpperCAmelCase__    =							generated.expand(lowerCamelCase__					,*generated.shape[1:]    )
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							next_tokens.permute(1					,0    ), scores.squeeze(0    )
																	if tokens is None:
																					UpperCAmelCase__    =							next_tokens
																	else:
																					UpperCAmelCase__    =							tokens.expand(lowerCamelCase__					,*tokens.shape[1:]    )
																					UpperCAmelCase__    =							torch.cat((tokens, next_tokens)					,dim=1    )
													else:
																	UpperCAmelCase__    =							-float(np.inf    )
																	UpperCAmelCase__    =							0
																	UpperCAmelCase__    =							scores[:, None] + logits
																	seq_lengths[~is_stopped] += 1
																	UpperCAmelCase__    =							scores_sum / seq_lengths[:, None]
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							scores_sum_average.view(-1    ).topk(lowerCamelCase__					,-1    )
																	UpperCAmelCase__    =							next_tokens // scores_sum.shape[1]
																	UpperCAmelCase__    =							seq_lengths[next_tokens_source]
																	UpperCAmelCase__    =							next_tokens % scores_sum.shape[1]
																	UpperCAmelCase__    =							next_tokens.unsqueeze(1    )
																	UpperCAmelCase__    =							tokens[next_tokens_source]
																	UpperCAmelCase__    =							torch.cat((tokens, next_tokens)					,dim=1    )
																	UpperCAmelCase__    =							generated[next_tokens_source]
																	UpperCAmelCase__    =							scores_sum_average * seq_lengths
																	UpperCAmelCase__    =							is_stopped[next_tokens_source]

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

									UpperCAmelCase__    =							scores / seq_lengths
									UpperCAmelCase__    =							scores.argsort(descending=lowerCamelCase__    )
									# tokens tensors are already padded to max_seq_length
									UpperCAmelCase__    =							[tokens[i] for i in order]
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__					,dim=0    )
									UpperCAmelCase__    =							torch.tensor([seq_lengths[i] for i in order]					,dtype=seq_lengths.dtype    )
									return output_texts, seq_lengths



