'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					List[str]  ={'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE_:					int  ={
    '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'
        ),
    },
}

SCREAMING_SNAKE_CASE_:					List[Any]  ={
    'moussaKam/mbarthez': 10_24,
    'moussaKam/barthez': 10_24,
    'moussaKam/barthez-orangesum-title': 10_24,
}

SCREAMING_SNAKE_CASE_:					Optional[Any]  ='▁'




class 	__A				(			_lowerCamelCase     ):
    a__	:						List[str]	       =  VOCAB_FILES_NAMES
    a__	:						Optional[int]	       =  PRETRAINED_VOCAB_FILES_MAP
    a__	:						Optional[int]	       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						List[str]	       =  ["""input_ids""", """attention_mask"""]



    def __init__(self   :   Any       ,      __a   :   Any       ,      __a   :   Any="<s>"       ,      __a   :   List[str]="</s>"       ,      __a   :   Union[str, Any]="</s>"       ,      __a   :   Optional[Any]="<s>"       ,      __a   :   List[str]="<unk>"       ,      __a   :   List[str]="<pad>"       ,      __a   :   str="<mask>"       ,      __a   :   Optional[Dict[str, Any]] = None       ,      **__a   :   Tuple       ,      ):
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCAmelCase_	   =       AddedToken(lowercase_       ,      lstrip=lowercase_       ,      rstrip=lowercase_			) if isinstance(lowercase_       ,      lowercase_			) else mask_token

         UpperCAmelCase_	   =       {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=lowercase_       ,      eos_token=lowercase_       ,      unk_token=lowercase_       ,      sep_token=lowercase_       ,      cls_token=lowercase_       ,      pad_token=lowercase_       ,      mask_token=lowercase_       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **lowercase_       ,      )

         UpperCAmelCase_	   =       vocab_file
         UpperCAmelCase_	   =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
         self.sp_model.Load(str(lowercase_			)			)

         UpperCAmelCase_	   =       {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

         UpperCAmelCase_	   =       len(self.sp_model			) - 1
         UpperCAmelCase_	   =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}



    def  _lowercase						(self   :   Any       ,      __a   :   List[int]       ,      __a   :   Optional[List[int]] = None			):

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



    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   List[int]       ,      __a   :   Optional[List[int]] = None       ,      __a   :   bool = False			):
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=lowercase_       ,      token_ids_a=lowercase_       ,      already_has_special_tokens=lowercase_			)

         if token_ids_a is None:
              return [1] + ([0] * len(lowercase_			)) + [1]
         return [1] + ([0] * len(lowercase_			)) + [1, 1] + ([0] * len(lowercase_			)) + [1]



    def  _lowercase						(self   :   Any       ,      __a   :   List[int]       ,      __a   :   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]



    @property
    def  _lowercase						(self   :   Any			):
         return len(self.sp_model			)



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       {self.convert_ids_to_tokens(lowercase_			): i for i in range(self.vocab_size			)}
         vocab.update(self.added_tokens_encoder			)
         return vocab



    def  _lowercase						(self   :   List[Any]       ,      __a   :   str			):
         return self.sp_model.encode(lowercase_       ,      out_type=lowercase_			)



    def  _lowercase						(self   :   List[str]       ,      __a   :   List[str]			):
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase_	   =       self.sp_model.PieceToId(lowercase_			)

         return spm_id if spm_id else self.unk_token_id



    def  _lowercase						(self   :   str       ,      __a   :   Union[str, Any]			):
         if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(lowercase_			)



    def  _lowercase						(self   :   str       ,      __a   :   List[str]			):
         UpperCAmelCase_	   =       []
         UpperCAmelCase_	   =       ""
         UpperCAmelCase_	   =       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(lowercase_			) + token
                   UpperCAmelCase_	   =       True
                   UpperCAmelCase_	   =       []
              else:
                   current_sub_tokens.append(lowercase_			)
                   UpperCAmelCase_	   =       False
         out_string += self.sp_model.decode(lowercase_			)
         return out_string.strip()



    def __getstate__(self   :   Dict			):
         UpperCAmelCase_	   =       self.__dict__.copy()
         UpperCAmelCase_	   =       None
         return state



    def __setstate__(self   :   str       ,      __a   :   Dict			):
         UpperCAmelCase_	   =       d

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

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






    def  _lowercase						(self   :   Optional[int]       ,      __a   :   str       ,      __a   :   Optional[str] = None			):
         if not os.path.isdir(lowercase_			):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
              return
         UpperCAmelCase_	   =       os.path.join(
             lowercase_       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

         return (out_vocab_file,)





'''simple docstring'''





import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class 	__A				:



    @property
    def  _lowercase						(self   :   int			):
         return self.get_dummy_input()



    @property
    def  _lowercase						(self   :   Any			):
         if self.block_type == "down":
              return (4, 32, 16, 16)
         elif self.block_type == "mid":
              return (4, 32, 32, 32)
         elif self.block_type == "up":
              return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   List[str]=True       ,      __a   :   Any=False       ,      __a   :   List[Any]=False       ,      __a   :   Any=False       ,      ):
         UpperCAmelCase_	   =       4
         UpperCAmelCase_	   =       32
         UpperCAmelCase_	   =       (32, 32)

         UpperCAmelCase_	   =       torch.manual_seed(0			)
         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       (batch_size, num_channels) + sizes
         UpperCAmelCase_	   =       randn_tensor(__a       ,      generator=__a       ,      device=__a			)
         UpperCAmelCase_	   =       {"hidden_states": hidden_states}

         if include_temb:
              UpperCAmelCase_	   =       128
              UpperCAmelCase_	   =       randn_tensor((batch_size, temb_channels)       ,      generator=__a       ,      device=__a			)

         if include_res_hidden_states_tuple:
              UpperCAmelCase_	   =       torch.manual_seed(1			)
              UpperCAmelCase_	   =       (randn_tensor(__a       ,      generator=__a       ,      device=__a			),)

         if include_encoder_hidden_states:
              UpperCAmelCase_	   =       floats_tensor((batch_size, 32, 32)			).to(__a			)

         if include_skip_sample:
              UpperCAmelCase_	   =       randn_tensor(((batch_size, 3) + sizes)       ,      generator=__a       ,      device=__a			)

         return dummy_input



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       {
             "in_channels": 32,
             "out_channels": 32,
             "temb_channels": 128,
         }
         if self.block_type == "up":
              UpperCAmelCase_	   =       32

         if self.block_type == "mid":
              init_dict.pop("out_channels"			)

         UpperCAmelCase_	   =       self.dummy_input
         return init_dict, inputs_dict



    def  _lowercase						(self   :   Tuple       ,      __a   :   Any			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         unet_block.to(__a			)
         unet_block.eval()

         with torch.no_grad():
              UpperCAmelCase_	   =       unet_block(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         self.assertEqual(output.shape       ,      self.output_shape			)

         UpperCAmelCase_	   =       output[0, -1, -3:, -3:]
         UpperCAmelCase_	   =       torch.tensor(__a			).to(__a			)
         assert torch_all_close(output_slice.flatten()       ,      __a       ,      atol=5E-3			)






    @unittest.skipIf(torch_device == "mps"       ,      "Training is not supported in mps"			)
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.prepare_init_args_and_inputs_for_common()
         UpperCAmelCase_	   =       self.block_class(**__a			)
         model.to(__a			)
         model.train()
         UpperCAmelCase_	   =       model(**__a			)

         if isinstance(__a       ,      __a			):
              UpperCAmelCase_	   =       output[0]

         UpperCAmelCase_	   =       torch.device(__a			)
         UpperCAmelCase_	   =       randn_tensor(output.shape       ,      device=__a			)
         UpperCAmelCase_	   =       torch.nn.functional.mse_loss(__a       ,      __a			)
         loss.backward()




