
import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {'vocab_file': 'sentencepiece.model'}

__lowerCAmelCase					   =       {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

__lowerCAmelCase					   =       {
    'google/rembert': 2_5_6,
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Optional[Any]    		=	VOCAB_FILES_NAMES
	UpperCamelCase_  :					int    		=	PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase_  :					Optional[Any]    		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







	def __init__(     self      :Optional[Any]   ,     lowercase      :Tuple   ,     lowercase      :int=False   ,     lowercase      :Optional[Any]=True   ,     lowercase      :Optional[int]=True   ,     lowercase      :Union[str, Any]="[CLS]"   ,     lowercase      :List[Any]="[SEP]"   ,     lowercase      :List[Any]="[UNK]"   ,     lowercase      :List[str]="[SEP]"   ,     lowercase      :str="[PAD]"   ,     lowercase      :str="[CLS]"   ,     lowercase      :str="[MASK]"   ,     **lowercase      :Dict   ,     )		->      Tuple:







				"""simple docstring"""






				super().__init__(
				    do_lower_case=lowercase   ,     remove_space=lowercase   ,     keep_accents=lowercase   ,     bos_token=lowercase   ,     eos_token=lowercase   ,     unk_token=lowercase   ,     sep_token=lowercase   ,     pad_token=lowercase   ,     cls_token=lowercase   ,     mask_token=lowercase   ,     **lowercase   ,     )

				SCREAMING_SNAKE_CASE     						= do_lower_case
				SCREAMING_SNAKE_CASE     						= remove_space
				SCREAMING_SNAKE_CASE     						= keep_accents
				SCREAMING_SNAKE_CASE     						= vocab_file

				SCREAMING_SNAKE_CASE     						= spm.SentencePieceProcessor()
				self.sp_model.Load(lowercase			)







	@property
	def  snake_case__     (     self      :Dict			)		->      List[str]:







				"""simple docstring"""






				return len(self.sp_model			)







	def  snake_case__     (     self      :Any			)		->      Any:







				"""simple docstring"""






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







	def __getstate__(     self      :str			)		->      List[str]:







				"""simple docstring"""






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







	def __setstate__(     self      :Optional[Any]   ,     lowercase      :Dict			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= d
				SCREAMING_SNAKE_CASE     						= spm.SentencePieceProcessor()
				self.sp_model.Load(self.vocab_file			)







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :Optional[int]   ,     lowercase      :List[str]=False			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.sp_model.EncodeAsPieces(lowercase			)
				return pieces







	def  snake_case__     (     self      :List[Any]   ,     lowercase      :int			)		->      str:







				"""simple docstring"""






				return self.sp_model.PieceToId(lowercase			)







	def  snake_case__     (     self      :str   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				return self.sp_model.IdToPiece(lowercase			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :Any			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.sp_model.decode_pieces(lowercase			)
				return out_string







	def  snake_case__     (     self      :int   ,     lowercase      :List[int]   ,     lowercase      :Optional[List[int]] = None			)		->      List[int]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :List[Any]   ,     lowercase      :List[int]   ,     lowercase      :Optional[List[int]] = None   ,     lowercase      :bool = False			)		->      List[int]:







				"""simple docstring"""






				if already_has_special_tokens:
							if token_ids_a is not None:
										raise ValueError(
										    '''You should not supply a second sequence if the provided sequence of '''
										    '''ids is already formatted with special tokens for the model.'''			)
							return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :List[int]   ,     lowercase      :Optional[List[int]] = None			)		->      List[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [self.sep_token_id]
				SCREAMING_SNAKE_CASE     						= [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			) * [0] + len(token_ids_a + sep			) * [1]







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :str   ,     lowercase      :Optional[str] = None			)		->      Tuple[str]:







				"""simple docstring"""






				if not os.path.isdir(lowercase			):
							logger.error('''Vocabulary path ({}) should be a directory'''.format(lowercase			)			)
							return
				SCREAMING_SNAKE_CASE     						= 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			):
							copyfile(self.vocab_file   ,     lowercase			)

				return (out_vocab_file,)

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					int    		=	(DDPMParallelScheduler,)







	def  snake_case__     (     self      :Any   ,     **lowercase      :str			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {
				    '''num_train_timesteps''': 1_0_0_0,
				    '''beta_start''': 0.00_01,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**lowercase			)
				return config







	def  snake_case__     (     self      :Dict			)		->      List[Any]:







				"""simple docstring"""






				for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]   ,     [0.0_02, 0.02, 0.2, 2]			):
							self.check_over_configs(beta_start=lowercase   ,     beta_end=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[str]:







				"""simple docstring"""






				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=lowercase			)







	def  snake_case__     (     self      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=lowercase			)







	def  snake_case__     (     self      :Any			)		->      List[Any]:







				"""simple docstring"""






				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=lowercase			)







	def  snake_case__     (     self      :str			)		->      str:







				"""simple docstring"""






				self.check_over_configs(thresholding=lowercase			)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=lowercase   ,     prediction_type=lowercase   ,     sample_max_value=lowercase   ,     )







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







				"""simple docstring"""






				for prediction_type in ["epsilon", "sample", "v_prediction"]:
							self.check_over_configs(prediction_type=lowercase			)







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				for t in [0, 5_0_0, 9_9_9]:
							self.check_over_forward(time_step=lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(4_8_7			) - 0.0_09_79			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(9_9_9			) - 0.02			)			) < 1e-5







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter + 0.1
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter - 0.1

				SCREAMING_SNAKE_CASE     						= samplea.shape[0]
				SCREAMING_SNAKE_CASE     						= torch.stack([samplea, samplea, samplea]   ,     dim=0			)
				SCREAMING_SNAKE_CASE     						= torch.arange(lowercase			)[0:3, None].repeat(1   ,     lowercase			)

				SCREAMING_SNAKE_CASE     						= model(samples.flatten(0   ,     1			)   ,     timesteps.flatten(0   ,     1			)			)
				SCREAMING_SNAKE_CASE     						= scheduler.batch_step_no_noise(lowercase   ,     timesteps.flatten(0   ,     1			)   ,     samples.flatten(0   ,     1			)			)

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 11_53.18_33			) < 1e-2
				assert abs(result_mean.item() - 0.50_05			) < 1e-3







	def  snake_case__     (     self      :str			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= torch.manual_seed(0			)

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

							# 2. predict previous mean of sample x_t-1
							SCREAMING_SNAKE_CASE     						= scheduler.step(lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase			).prev_sample

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_58.96_06			) < 1e-2
				assert abs(result_mean.item() - 0.33_72			) < 1e-3







	def  snake_case__     (     self      :int			)		->      str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config(prediction_type='''v_prediction'''			)
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.dummy_model()
				SCREAMING_SNAKE_CASE     						= self.dummy_sample_deter
				SCREAMING_SNAKE_CASE     						= torch.manual_seed(0			)

				for t in reversed(range(lowercase			)			):
							# 1. predict noise residual
							SCREAMING_SNAKE_CASE     						= model(lowercase   ,     lowercase			)

							# 2. predict previous mean of sample x_t-1
							SCREAMING_SNAKE_CASE     						= scheduler.step(lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase			).prev_sample

							SCREAMING_SNAKE_CASE     						= pred_prev_sample

				SCREAMING_SNAKE_CASE     						= torch.sum(torch.abs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= torch.mean(torch.abs(lowercase			)			)

				assert abs(result_sum.item() - 2_02.02_96			) < 1e-2
				assert abs(result_mean.item() - 0.26_31			) < 1e-3







	def  snake_case__     (     self      :Optional[int]			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]

				scheduler.set_timesteps(timesteps=lowercase			)

				SCREAMING_SNAKE_CASE     						= scheduler.timesteps

				for i, timestep in enumerate(lowercase			):
							if i == len(lowercase			) - 1:
										SCREAMING_SNAKE_CASE     						= -1
							else:
										SCREAMING_SNAKE_CASE     						= timesteps[i + 1]

							SCREAMING_SNAKE_CASE     						= scheduler.previous_timestep(lowercase			)
							SCREAMING_SNAKE_CASE     						= prev_t.item()

							self.assertEqual(lowercase   ,     lowercase			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 5_1, 0]

				with self.assertRaises(lowercase   ,     msg='''`custom_timesteps` must be in descending order.'''			):
							scheduler.set_timesteps(timesteps=lowercase			)







	def  snake_case__     (     self      :Optional[int]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [1_0_0, 8_7, 5_0, 1, 0]
				SCREAMING_SNAKE_CASE     						= len(lowercase			)

				with self.assertRaises(lowercase   ,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''			):
							scheduler.set_timesteps(num_inference_steps=lowercase   ,     timesteps=lowercase			)







	def  snake_case__     (     self      :str			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE     						= self.get_scheduler_config()
				SCREAMING_SNAKE_CASE     						= scheduler_class(**lowercase			)

				SCREAMING_SNAKE_CASE     						= [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    lowercase   ,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''   ,     ):
							scheduler.set_timesteps(timesteps=lowercase			)