



"""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_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE_				= {
    """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_				= {
    """moussaKam/mbarthez""": 10_24,
    """moussaKam/barthez""": 10_24,
    """moussaKam/barthez-orangesum-title""": 10_24,
}

SCREAMING_SNAKE_CASE_				= """▁"""




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					VOCAB_FILES_NAMES
							__lowerCAmelCase    					=					PRETRAINED_VOCAB_FILES_MAP
							__lowerCAmelCase    					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCAmelCase    					=					["input_ids", "attention_mask"]






							def __init__(   self			,     a_			,     a_="<s>"			,     a_="</s>"			,     a_="</s>"			,     a_="<s>"			,     a_="<unk>"			,     a_="<pad>"			,     a_="<mask>"			,     a_ = None			,     **a_			,     ):
									# Mask token behave like a normal word, i.e. include the space before it
									a_     :      Tuple 						=     AddedToken(a_			,     lstrip=a_			,     rstrip=a_      ) if isinstance(a_			,     a_      ) else mask_token

									a_     :      Dict 						=     {} if sp_model_kwargs is None else sp_model_kwargs

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

									a_     :      Any 						=     vocab_file
									a_     :      str 						=     spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(str(a_      )      )

									a_     :      Union[str, Any] 						=     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

									a_     :      str 						=     len(self.sp_model      ) - 1
									a_     :      List[Any] 						=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}






							def snake_case_  (   self			,     a_			,     a_ = None      ):

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






							def snake_case_  (   self			,     a_			,     a_ = None			,     a_ = False      ):
									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 None:
											return [1] + ([0] * len(a_      )) + [1]
									return [1] + ([0] * len(a_      )) + [1, 1] + ([0] * len(a_      )) + [1]






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									a_     :      Dict 						=     [self.sep_token_id]
									a_     :      Optional[int] 						=     [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 snake_case_  (   self      ):
									return len(self.sp_model      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     {self.convert_ids_to_tokens(a_      ): i for i in range(self.vocab_size      )}
									vocab.update(self.added_tokens_encoder      )
									return vocab






							def snake_case_  (   self			,     a_      ):
									return self.sp_model.encode(a_			,     out_type=a_      )






							def snake_case_  (   self			,     a_      ):
									if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
									a_     :      Dict 						=     self.sp_model.PieceToId(a_      )

									return spm_id if spm_id else self.unk_token_id






							def snake_case_  (   self			,     a_      ):
									if index in self.fairseq_ids_to_tokens:
											return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(a_      )






							def snake_case_  (   self			,     a_      ):
									a_     :      List[Any] 						=     []
									a_     :      List[Any] 						=     ""
									a_     :      Optional[Any] 						=     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(a_      ) + token
													a_     :      List[str] 						=     True
													a_     :      Tuple 						=     []
											else:
													current_sub_tokens.append(a_      )
													a_     :      str 						=     False
									out_string += self.sp_model.decode(a_      )
									return out_string.strip()






							def __getstate__(   self      ):
									a_     :      Optional[int] 						=     self.__dict__.copy()
									a_     :      Optional[int] 						=     None
									return state






							def __setstate__(   self			,     a_      ):
									a_     :      List[Any] 						=     d

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

									a_     :      List[str] 						=     spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )






							def snake_case_  (   self			,     a_			,     a_ = None      ):
									if not os.path.isdir(a_      ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
											return
									a_     :      Any 						=     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_     :      Union[str, Any] 						=     self.sp_model.serialized_model_proto()
													fi.write(a_      )

									return (out_vocab_file,)




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case_						(						unittest.TestCase				):






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      Any 						=     UNetaDModel(
									    block_out_channels=(3_2, 6_4)			,     layers_per_block=2			,     sample_size=3_2			,     in_channels=3			,     out_channels=3			,     down_block_types=("DownBlock2D", "AttnDownBlock2D")			,     up_block_types=("AttnUpBlock2D", "UpBlock2D")			,     )
									return model






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.dummy_uncond_unet
									a_     :      Any 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )

									a_     :      Tuple 						=     torch.manual_seed(0      )
									a_     :      Union[str, Any] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"      ).images

									a_     :      int 						=     torch.manual_seed(0      )
									a_     :      Optional[int] 						=     pndm(generator=a_			,     num_inference_steps=2_0			,     output_type="numpy"			,     return_dict=a_      )[0]

									a_     :      List[Any] 						=     image[0, -3:, -3:, -1]
									a_     :      Union[str, Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      List[Any] 						=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




@slow
@require_torch
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "google/ddpm-cifar10-32"

									a_     :      Optional[int] 						=     UNetaDModel.from_pretrained(a_      )
									a_     :      Union[str, Any] 						=     PNDMScheduler()

									a_     :      List[Any] 						=     PNDMPipeline(unet=a_			,     scheduler=a_      )
									pndm.to(a_      )
									pndm.set_progress_bar_config(disable=a_      )
									a_     :      Optional[int] 						=     torch.manual_seed(0      )
									a_     :      List[Any] 						=     pndm(generator=a_			,     output_type="numpy"      ).images

									a_     :      str 						=     image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)
									a_     :      Optional[Any] 						=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2