






'''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 		UpperCamelCase__    :




		'''simple docstring'''





		@property
		def 			UpperCamelCase_					(      self       ):
								return self.get_dummy_input()






		@property
		def 			UpperCamelCase_					(      self       ):
								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 			UpperCamelCase_					(      self   ,_lowerCAmelCase=True   ,_lowerCAmelCase=False   ,_lowerCAmelCase=False   ,_lowerCAmelCase=False   ,):
								lowerCamelCase__         =  4
								lowerCamelCase__         =  32
								lowerCamelCase__         =  (32, 32)

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

								if include_temb:
														lowerCamelCase__         =  1_28
														lowerCamelCase__         =  randn_tensor((batch_size, temb_channels)   ,generator=_lowerCAmelCase   ,device=_lowerCAmelCase       )

								if include_res_hidden_states_tuple:
														lowerCamelCase__         =  torch.manual_seed(1       )
														lowerCamelCase__         =  (randn_tensor(_lowerCAmelCase   ,generator=_lowerCAmelCase   ,device=_lowerCAmelCase       ),)

								if include_encoder_hidden_states:
														lowerCamelCase__         =  floats_tensor((batch_size, 32, 32)       ).to(_lowerCAmelCase       )

								if include_skip_sample:
														lowerCamelCase__         =  randn_tensor(((batch_size, 3) + sizes)   ,generator=_lowerCAmelCase   ,device=_lowerCAmelCase       )

								return dummy_input






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  {
								    """in_channels""": 32,
								    """out_channels""": 32,
								    """temb_channels""": 1_28,
								}
								if self.block_type == "up":
														lowerCamelCase__         =  32

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

								lowerCamelCase__         =  self.dummy_input
								return init_dict, inputs_dict






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.prepare_init_args_and_inputs_for_common()
								lowerCamelCase__         =  self.block_class(**_lowerCAmelCase       )
								unet_block.to(_lowerCAmelCase       )
								unet_block.eval()

								with torch.no_grad():
														lowerCamelCase__         =  unet_block(**_lowerCAmelCase       )

								if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       ):
														lowerCamelCase__         =  output[0]

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

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






		@unittest.skipIf(torch_device == """mps"""   ,"""Training is not supported in mps"""       )
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__       ,			lowerCamelCase__         =  self.prepare_init_args_and_inputs_for_common()
								lowerCamelCase__         =  self.block_class(**_lowerCAmelCase       )
								model.to(_lowerCAmelCase       )
								model.train()
								lowerCamelCase__         =  model(**_lowerCAmelCase       )

								if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       ):
														lowerCamelCase__         =  output[0]

								lowerCamelCase__         =  torch.device(_lowerCAmelCase       )
								lowerCamelCase__         =  randn_tensor(output.shape   ,device=_lowerCAmelCase       )
								lowerCamelCase__         =  torch.nn.functional.mse_loss(_lowerCAmelCase   ,_lowerCAmelCase       )
								loss.backward()









'''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


UpperCamelCase    :      int  			=		logging.get_logger(__name__)

UpperCamelCase    :      Optional[int]  			=		{'vocab_file': 'sentencepiece.bpe.model'}

UpperCamelCase    :      Dict  			=		{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

UpperCamelCase    :      List[Any]  			=		{
    'camembert-base': 5_12,
}

UpperCamelCase    :      List[str]  			=		'▁'




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





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






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

								lowerCamelCase__         =  {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_lowerCAmelCase   ,eos_token=_lowerCAmelCase   ,unk_token=_lowerCAmelCase   ,sep_token=_lowerCAmelCase   ,cls_token=_lowerCAmelCase   ,pad_token=_lowerCAmelCase   ,mask_token=_lowerCAmelCase   ,additional_special_tokens=_lowerCAmelCase   ,sp_model_kwargs=self.sp_model_kwargs   ,**_lowerCAmelCase   ,)
								lowerCamelCase__         =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
								self.sp_model.Load(str(_lowerCAmelCase       )       )
								lowerCamelCase__         =  vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								lowerCamelCase__         =  {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
								lowerCamelCase__         =  len(self.fairseq_tokens_to_ids       )
								lowerCamelCase__         =  len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
								lowerCamelCase__         =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):

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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,_lowerCAmelCase = False       ):
								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_lowerCAmelCase   ,token_ids_a=_lowerCAmelCase   ,already_has_special_tokens=_lowerCAmelCase       )

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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								lowerCamelCase__         =  [self.sep_token_id]
								lowerCamelCase__         =  [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 			UpperCamelCase_					(      self       ):
								return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  {self.convert_ids_to_tokens(_lowerCAmelCase       ): i for i in range(self.vocab_size       )}
								vocab.update(self.added_tokens_encoder       )
								return vocab






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self.sp_model.encode(_lowerCAmelCase   ,out_type=_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(_lowerCAmelCase       ) == 0:
														# Convert sentence piece unk token to fairseq unk token index
														return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								if index in self.fairseq_ids_to_tokens:
														return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  []
								lowerCamelCase__         =  """"""
								lowerCamelCase__         =  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(_lowerCAmelCase       ) + token
																				lowerCamelCase__         =  True
																				lowerCamelCase__         =  []
														else:
																				current_sub_tokens.append(_lowerCAmelCase       )
																				lowerCamelCase__         =  False
								out_string += self.sp_model.decode(_lowerCAmelCase       )
								return out_string.strip()






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






		def __setstate__(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  d

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

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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None       ):
								if not os.path.isdir(_lowerCAmelCase       ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
														return
								lowerCamelCase__         =  os.path.join(
								    _lowerCAmelCase   ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

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

								return (out_vocab_file,)

