





"""simple docstring"""



import os
import unicodedata
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 SPIECE_UNDERLINE, logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE    =	{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

SCREAMING_SNAKE_CASE    =	{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
SCREAMING_SNAKE_CASE    =	0
SCREAMING_SNAKE_CASE    =	1
SCREAMING_SNAKE_CASE    =	2
SCREAMING_SNAKE_CASE    =	3
SCREAMING_SNAKE_CASE    =	4




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				VOCAB_FILES_NAMES
    lowercase__						 =				PRETRAINED_VOCAB_FILES_MAP
    lowercase__						 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__						 =				'''left'''




    def __init__(   self						:	int     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Any=False     ,			snake_case_						:	Optional[Any]=True     ,			snake_case_						:	Optional[int]=False     ,			snake_case_						:	List[str]="<s>"     ,			snake_case_						:	Tuple="</s>"     ,			snake_case_						:	List[Any]="<unk>"     ,			snake_case_						:	Tuple="<sep>"     ,			snake_case_						:	Union[str, Any]="<pad>"     ,			snake_case_						:	int="<cls>"     ,			snake_case_						:	Tuple="<mask>"     ,			snake_case_						:	Any=["<eop>", "<eod>"]     ,			snake_case_						:	Optional[Dict[str, Any]] = None     ,			**snake_case_						:	str     ,			)				->							Optional[int]:




     '''simple docstring'''

     A__	=    AddedToken(_a     ,			lstrip=_a     ,			rstrip=_a				) if isinstance(_a     ,			_a				) else mask_token

     A__	=    {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=_a     ,			remove_space=_a     ,			keep_accents=_a     ,			bos_token=_a     ,			eos_token=_a     ,			unk_token=_a     ,			sep_token=_a     ,			pad_token=_a     ,			cls_token=_a     ,			mask_token=_a     ,			additional_special_tokens=_a     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**_a     ,			)

     A__	=    3

     A__	=    do_lower_case
     A__	=    remove_space
     A__	=    keep_accents
     A__	=    vocab_file

     A__	=    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.Load(_a				)




    @property
    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     return len(self.sp_model				)




    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

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




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




     '''simple docstring'''

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




    def __setstate__(   self						:	Optional[int]     ,			snake_case_						:	Tuple				)				->							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 							__magic_name__							(   self						:	Any     ,			snake_case_						:	List[Any]				)				->							List[Any]:




     '''simple docstring'''

     if self.remove_space:
      A__	=    " ".join(inputs.strip().split()				)
     else:
      A__	=    inputs
     A__	=    outputs.replace("``"     ,			"\""				).replace("\'\'"     ,			"\""				)

     if not self.keep_accents:
      A__	=    unicodedata.normalize("NFKD"     ,			_a				)
      A__	=    "".join([c for c in outputs if not unicodedata.combining(_a				)]				)
     if self.do_lower_case:
      A__	=    outputs.lower()

     return outputs




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	str				)				->							str:




     '''simple docstring'''

     A__	=    self.preprocess_text(_a				)
     A__	=    self.sp_model.encode(_a     ,			out_type=_a				)
     A__	=    []
     for piece in pieces:
      if len(_a				) > 1 and piece[-1] == str(","				) and piece[-2].isdigit():
       A__	=    self.sp_model.EncodeAsPieces(piece[:-1].replace(_a     ,			""				)				)
       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
        if len(cur_pieces[0]				) == 1:
         A__	=    cur_pieces[1:]
        else:
         A__	=    cur_pieces[0][1:]
       cur_pieces.append(piece[-1]				)
       new_pieces.extend(_a				)
      else:
       new_pieces.append(_a				)

     return new_pieces




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     return self.sp_model.PieceToId(_a				)




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




     '''simple docstring'''

     return self.sp_model.IdToPiece(_a				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	Optional[Any]				)				->							str:




     '''simple docstring'''

     A__	=    "".join(_a				).replace(_a     ,			" "				).strip()
     return out_string




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	List[int]     ,			snake_case_						:	bool = False     ,			snake_case_						:	bool = None     ,			snake_case_						:	bool = True     ,			**snake_case_						:	Any     ,			)				->							Tuple:




     '''simple docstring'''

     A__	=    kwargs.pop("use_source_tokenizer"     ,			_a				)

     A__	=    self.convert_ids_to_tokens(_a     ,			skip_special_tokens=_a				)

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     A__	=    []
     A__	=    []
     for token in filtered_tokens:
      if skip_special_tokens and token in self.all_special_ids:
       continue
      if token in self.added_tokens_encoder:
       if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(_a				)				)
        A__	=    []
       sub_texts.append(_a				)
      else:
       current_sub_text.append(_a				)
     if current_sub_text:
      sub_texts.append(self.convert_tokens_to_string(_a				)				)

     # Mimic the behavior of the Rust tokenizer:
     # By default, there are no spaces between special tokens
     A__	=    "".join(_a				)

     A__	=    (
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
      A__	=    self.clean_up_tokenization(_a				)
      return clean_text
     else:
      return text




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[int]     ,			snake_case_						:	Optional[List[int]] = None				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    [self.sep_token_id]
     A__	=    [self.cls_token_id]
     if token_ids_a is None:
      return token_ids_a + sep + cls
     return token_ids_a + sep + token_ids_a + sep + cls




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	List[int]     ,			snake_case_						:	Optional[List[int]] = None     ,			snake_case_						:	bool = False				)				->							Optional[Any]:




     '''simple docstring'''

     if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=_a     ,			token_ids_a=_a     ,			already_has_special_tokens=_a				)

     if token_ids_a is not None:
      return ([0] * len(_a				)) + [1] + ([0] * len(_a				)) + [1, 1]
     return ([0] * len(_a				)) + [1, 1]




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	List[int]     ,			snake_case_						:	Optional[List[int]] = None				)				->							Tuple:




     '''simple docstring'''

     A__	=    [self.sep_token_id]
     A__	=    [2]

     if token_ids_a is None:
      return len(token_ids_a + sep				) * [0] + cls_segment_id
     return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	str     ,			snake_case_						:	Optional[str] = None				)				->							str:




     '''simple docstring'''

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

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

     return (out_vocab_file,)











"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class      UpperCAmelCase_  (						nn.Module							):




    def __init__(   self						:	Optional[int]     ,			snake_case_						:	int = 16     ,			snake_case_						:	int = 88     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	int = 1     ,			snake_case_						:	float = 0.0     ,			snake_case_						:	int = 32     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	bool = False     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	str = "geglu"     ,			snake_case_						:	Optional[int] = None     ,			)				->							str:




     '''simple docstring'''

     super().__init__()
     A__	=    nn.ModuleList(
         [
             TransformeraDModel(
                 num_attention_heads=snake_case_     ,			attention_head_dim=snake_case_     ,			in_channels=snake_case_     ,			num_layers=snake_case_     ,			dropout=snake_case_     ,			norm_num_groups=snake_case_     ,			cross_attention_dim=snake_case_     ,			attention_bias=snake_case_     ,			sample_size=snake_case_     ,			num_vector_embeds=snake_case_     ,			activation_fn=snake_case_     ,			num_embeds_ada_norm=snake_case_     ,			)
             for _ in range(2				)
         ]				)

     # Variables that can be set by a pipeline:

     # The ratio of transformer1 to transformer2's output states to be combined during inference
     A__	=    0.5

     # The shape of `encoder_hidden_states` is expected to be
     # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
     A__	=    [77, 257]

     # Which transformer to use to encode which condition.
     # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
     A__	=    [1, 0]




    def 							__magic_name__							(   self						:	Dict     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Any=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	bool = True     ,			)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    hidden_states

     A__	=    []
     A__	=    0
     # attention_mask is not used yet
     for i in range(2				):
      # for each of the two transformers, pass the corresponding condition tokens
      A__	=    encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
      A__	=    self.transformer_index_for_condition[i]
      A__	=    self.transformers[transformer_index](
          snake_case_     ,			encoder_hidden_states=snake_case_     ,			timestep=snake_case_     ,			cross_attention_kwargs=snake_case_     ,			return_dict=snake_case_     ,			)[0]
      encoded_states.append(encoded_state - input_states				)
      tokens_start += self.condition_lengths[i]

     A__	=    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
     A__	=    output_states + input_states

     if not return_dict:
      return (output_states,)

     return TransformeraDModelOutput(sample=snake_case_				)





