


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     UpperCamelCase_   (			a_		, a_		, a_					):

    '''simple docstring'''






    UpperCAmelCase__							  =				[r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]


    @register_to_config
    def __init__(      self				:     Tuple ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     str = None ,			UpperCAmelCase__				:     Any = 50_257 ,			UpperCAmelCase__				:     Optional[Any] = 1_024 ,			UpperCAmelCase__				:     Any = 768 ,			UpperCAmelCase__				:     Tuple = 12 ,			UpperCAmelCase__				:     Optional[Any] = 12 ,			UpperCAmelCase__				:     int = None ,			UpperCAmelCase__				:     List[str] = "gelu_new" ,			UpperCAmelCase__				:     int = 0.1 ,			UpperCAmelCase__				:     List[str] = 0.1 ,			UpperCAmelCase__				:     Tuple = 0.1 ,			UpperCAmelCase__				:     Union[str, Any] = 1e-5 ,			UpperCAmelCase__				:     Tuple = 0.02 ,			UpperCAmelCase__				:     Optional[Any] = True ,			UpperCAmelCase__				:     Any = True ,			UpperCAmelCase__				:     Dict = False ,			UpperCAmelCase__				:     Dict = False ,			)						->int:





           '''simple docstring'''

           super().__init__()

           A__							=   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.""")

           A__							=   prefix_inner_dim
           A__							=   prefix_hidden_dim

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

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple = None ,			UpperCAmelCase__				:     Union[str, Any] = None ,			)						->Any:





           '''simple docstring'''

           A__							=   self.transformer.transformer.wte(UpperCAmelCase__)
           A__							=   self.encode_prefix(UpperCAmelCase__)
           A__							=   self.decode_prefix(UpperCAmelCase__)
           A__							=   torch.cat((prefix_embeds, embedding_text) ,			dim=1)

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Union[str, Any])						->torch.Tensor:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           return self.encode_prefix(UpperCAmelCase__)


    @torch.no_grad()
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Optional[int])						->Union[str, Any]:





           '''simple docstring'''

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





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





           '''simple docstring'''

           A__							=   eos_token_id
           A__							=   None
           A__							=   None
           A__							=   torch.ones(UpperCAmelCase__ ,			device=UpperCAmelCase__ ,			dtype=torch.int)
           A__							=   torch.zeros(UpperCAmelCase__ ,			device=UpperCAmelCase__ ,			dtype=torch.bool)

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

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

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

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

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








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


_lowerCamelCase     :		Union[str, Any]								=       """▁"""

_lowerCamelCase     :		Optional[Any]								=       {"""vocab_file""": """spiece.model"""}

_lowerCamelCase     :		str								=       {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

_lowerCamelCase     :		List[str]								=       {
    """google/pegasus-xsum""": 512,
}


_lowerCamelCase     :		int								=       logging.get_logger(__name__)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				VOCAB_FILES_NAMES

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


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     str="<pad>" ,			UpperCAmelCase__				:     Optional[Any]="</s>" ,			UpperCAmelCase__				:     Any="<unk>" ,			UpperCAmelCase__				:     Union[str, Any]="<mask_2>" ,			UpperCAmelCase__				:     List[str]="<mask_1>" ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Optional[Any]=103 ,			UpperCAmelCase__				:     Optional[Dict[str, Any]] = None ,			**UpperCAmelCase__				:     Dict ,			)						->None:





           '''simple docstring'''

           A__							=   offset
           if additional_special_tokens is not None:
                  if not isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         raise TypeError(
                             f"""additional_special_tokens should be of type {type(UpperCAmelCase__)}, but is"""
                             f""" {type(UpperCAmelCase__)}""")

                  A__							=   (
                      ([mask_token_sent] + additional_special_tokens)
                      if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                      else additional_special_tokens
                  )
                  # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                  additional_special_tokens_extended += [
                      f"""<unk_{i}>""" for i in range(len(UpperCAmelCase__) ,			self.offset - 1)
                  ]

                  if len(set(UpperCAmelCase__)) != len(UpperCAmelCase__):
                         raise ValueError(
                             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                             f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""")
                  A__							=   additional_special_tokens_extended
           else:
                  A__							=   [mask_token_sent] if mask_token_sent is not None else []
                  additional_special_tokens += [f"""<unk_{i}>""" for i in range(2 ,			self.offset)]

           A__							=   {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCAmelCase__ ,			unk_token=UpperCAmelCase__ ,			mask_token=UpperCAmelCase__ ,			pad_token=UpperCAmelCase__ ,			mask_token_sent=UpperCAmelCase__ ,			offset=UpperCAmelCase__ ,			additional_special_tokens=UpperCAmelCase__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**UpperCAmelCase__ ,			)
           A__							=   mask_token_sent
           A__							=   vocab_file
           A__							=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCAmelCase__)

           # add special tokens to encoder dict
           A__							=   {
               0: self.pad_token,
               1: self.eos_token,
           }

           if self.mask_token_sent is not None:
                  self.encoder.update(
                      {
                          2: self.mask_token_sent,
                          3: self.mask_token,
                      })

           if self.offset > 0:
                  # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                  # mask_token_sent is already added to list -> so start at 1
                  self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 ,			self.offset - 1)})

           A__							=   {v: k for k, v in self.encoder.items()}


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->int:





           '''simple docstring'''

           return len(self.sp_model) + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict[str, int]:





           '''simple docstring'''

           A__							=   {self.convert_ids_to_tokens(UpperCAmelCase__): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab


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





           '''simple docstring'''

           A__							=   self.__dict__.copy()
           A__							=   None
           return state


    def __setstate__(      self				:     int ,			UpperCAmelCase__				:     Optional[int])						->Optional[int]:





           '''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 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str)						->List[str]:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           if token in self.decoder:
                  return self.decoder[token]
           elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
           A__							=   self.sp_model.piece_to_id(UpperCAmelCase__)
           return sp_id + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     int)						->str:





           '''simple docstring'''

           if index in self.encoder:
                  return self.encoder[index]
           elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
           else:
                  A__							=   self.sp_model.IdToPiece(index - self.offset)
           return token


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     int)						->Optional[int]:





           '''simple docstring'''

           A__							=   []
           A__							=   ''''''
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(UpperCAmelCase__) + token
                         A__							=   []
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[int]=False)						->Union[str, Any]:





           '''simple docstring'''

           return 1


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           A__							=   set(self.all_special_ids)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

           return [1 if x in all_special_ids else 0 for x in seq]


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





           '''simple docstring'''

           if already_has_special_tokens:
                  return self._special_token_mask(UpperCAmelCase__)
           elif token_ids_a is None:
                  return self._special_token_mask(UpperCAmelCase__) + [1]
           else:
                  return self._special_token_mask(token_ids_a + token_ids_a) + [1]


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     List[str]=None)						->List[int]:





           '''simple docstring'''

           if token_ids_a is None:
                  return token_ids_a + [self.eos_token_id]
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return token_ids_a + token_ids_a + [self.eos_token_id]





    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__							=   os.path.join(
               UpperCAmelCase__ ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

           if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase__) and os.path.isfile(self.vocab_file):
                  copyfile(self.vocab_file ,			UpperCAmelCase__)
           elif not os.path.isfile(self.vocab_file):
                  with open(UpperCAmelCase__ ,			'''wb''') as fi:
                         A__							=   self.sp_model.serialized_model_proto()
                         fi.write(UpperCAmelCase__)

           return (out_vocab_file,)


