




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class       a__	( UpperCamelCase__				):
     a		:      Any		 =     (DDIMParallelScheduler,)
     a		:      Dict		 =     (("""eta""", 0.0), ("""num_inference_steps""", 50))







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   List[str]:



          '''simple docstring'''



          a     =				{
              "num_train_timesteps": 1000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**A			)
          return config







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Any:



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config(**A			)
          a     =				scheduler_class(**A			)

          a  ,				a     =				10, 0.0

          a     =				self.dummy_model()
          a     =				self.dummy_sample_deter

          scheduler.set_timesteps(A			)

          for t in scheduler.timesteps:
               a     =				model(A  ,		A			)
               a     =				scheduler.step(A  ,		A  ,		A  ,		A			).prev_sample

          return sample







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



          '''simple docstring'''



          for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=A			)

          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config(steps_offset=1			)
          a     =				scheduler_class(**A			)
          scheduler.set_timesteps(5			)
          assert torch.equal(scheduler.timesteps  ,		torch.LongTensor([801, 601, 401, 201, 1]			)			)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]  ,		[0.0_0_2, 0.0_2, 0.2, 2]			):
               self.check_over_configs(beta_start=A  ,		beta_end=A			)







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



          '''simple docstring'''



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







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



          '''simple docstring'''



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







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



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



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







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



          '''simple docstring'''



          for t in [1, 10, 49]:
               self.check_over_forward(time_step=A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          for t, num_inference_steps in zip([1, 10, 50]  ,		[10, 50, 500]			):
               self.check_over_forward(time_step=A  ,		num_inference_steps=A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          for t, eta in zip([1, 10, 49]  ,		[0.0, 0.5, 1.0]			):
               self.check_over_forward(time_step=A  ,		eta=A			)







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



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config()
          a     =				scheduler_class(**A			)

          assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0			) - 0.0			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420  ,		400			) - 0.1_4_7_7_1			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980  ,		960			) - 0.3_2_4_6_0			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0			) - 0.0			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487  ,		486			) - 0.0_0_9_7_9			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999  ,		998			) - 0.0_2			)			) < 1e-5







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config()
          a     =				scheduler_class(**A			)

          a  ,				a     =				10, 0.0
          scheduler.set_timesteps(A			)

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

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

          a     =				model(samples.flatten(0  ,		1			)  ,		timesteps.flatten(0  ,		1			)			)
          a     =				scheduler.batch_step_no_noise(A  ,		timesteps.flatten(0  ,		1			)  ,		samples.flatten(0  ,		1			)  ,		A			)

          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          assert abs(result_sum.item() - 1147.7904			) < 1e-2
          assert abs(result_mean.item() - 0.4_9_8_2			) < 1e-3







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



          '''simple docstring'''



          a     =				self.full_loop()

          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          assert abs(result_sum.item() - 172.0067			) < 1e-2
          assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1e-3







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



          '''simple docstring'''



          a     =				self.full_loop(prediction_type="v_prediction"			)

          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          assert abs(result_sum.item() - 52.5302			) < 1e-2
          assert abs(result_mean.item() - 0.0_6_8_4			) < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.full_loop(set_alpha_to_one=A  ,		beta_start=0.0_1			)
          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          assert abs(result_sum.item() - 149.8295			) < 1e-2
          assert abs(result_mean.item() - 0.1_9_5_1			) < 1e-3







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



          '''simple docstring'''



          a     =				self.full_loop(set_alpha_to_one=A  ,		beta_start=0.0_1			)
          a     =				torch.sum(torch.abs(A			)			)
          a     =				torch.mean(torch.abs(A			)			)

          assert abs(result_sum.item() - 149.0784			) < 1e-2
          assert abs(result_mean.item() - 0.1_9_4_1			) < 1e-3







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

import sentencepiece as spm

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


lowercase__				:      int	   = logging.get_logger(__name__)


lowercase__				:      Dict	   = "▁"

lowercase__				:      Union[str, Any]	   = {"vocab_file": "spiece.model"}

lowercase__				:      Union[str, Any]	   = {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowercase__				:      Tuple	   = {
    "google/reformer-crime-and-punishment": 524_288,
}




class       a__	( UpperCamelCase__				):
     a		:      List[Any]		 =     VOCAB_FILES_NAMES
     a		:      List[Any]		 =     PRETRAINED_VOCAB_FILES_MAP
     a		:      Any		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a		:      Dict		 =     ["""input_ids""", """attention_mask"""]







     def __init__(     self  ,		A  ,		A="</s>"  ,		A="<unk>"  ,		A=[]  ,		A = None  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          a     =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=A  ,		unk_token=A  ,		additional_special_tokens=A  ,		sp_model_kwargs=self.sp_model_kwargs  ,		**A  ,		)

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







     @property
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          return self.sp_model.get_piece_size()







     def       lowerCAmelCase_    (     self			)  ->   Dict[str, int]:



          '''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			)  ->   Optional[int]:



          '''simple docstring'''



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







     def __setstate__(     self  ,		A			)  ->   Union[str, Any]:



          '''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       lowerCAmelCase_    (     self  ,		A			)  ->   List[str]:



          '''simple docstring'''



          return self.sp_model.encode(A  ,		out_type=A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.sp_model.piece_to_id(A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[int]:



          '''simple docstring'''



          if index < self.sp_model.get_piece_size():
               a     =				self.sp_model.IdToPiece(A			)
          return token







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				[]
          a     =				""
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(A			) + token
                    a     =				[]
               else:
                    current_sub_tokens.append(A			)
          out_string += self.sp_model.decode(A			)
          return out_string.strip()







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   Tuple[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,)

