




'''simple docstring'''





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  (DPMSolverMultistepScheduler,)
      __lowercase 		=  (('''num_inference_steps''', 25),)






      def    A__(self ,  **a__					)       ->   Optional[Any]:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= {
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
             '''sample_max_value''': 1.0,
             '''algorithm_type''': '''dpmsolver++''',
             '''solver_type''': '''midpoint''',
             '''lower_order_final''': False,
             '''lambda_min_clipped''': -float('''inf'''					),
             '''variance_type''': None,
         }

         config.update(**a__					)
         return config






      def    A__(self ,  a__=0 ,  **a__					)       ->   Dict:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= dict(self.forward_default_kwargs					)
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= kwargs.pop('''num_inference_steps''' ,  a__					)
         SCREAMING_SNAKE_CASE__			:				Dict       						= self.dummy_sample
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= 0.1 * sample
         SCREAMING_SNAKE_CASE__			:				str       						= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__			:				int       						= self.get_scheduler_config(**a__					)
            SCREAMING_SNAKE_CASE__			:				List[str]       						= scheduler_class(**a__					)
            scheduler.set_timesteps(a__					)
            # copy over dummy past residuals
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__					)
               SCREAMING_SNAKE_CASE__			:				str       						= scheduler_class.from_pretrained(a__					)
               new_scheduler.set_timesteps(a__					)
               # copy over dummy past residuals
               SCREAMING_SNAKE_CASE__			:				int       						= dummy_past_residuals[: new_scheduler.config.solver_order]

            SCREAMING_SNAKE_CASE__						,				SCREAMING_SNAKE_CASE__			:				str       						= sample, sample
            for t in range(a__ ,  time_step + scheduler.config.solver_order + 1					):
               SCREAMING_SNAKE_CASE__			:				List[str]       						= scheduler.step(a__ ,  a__ ,  a__ ,  **a__					).prev_sample
               SCREAMING_SNAKE_CASE__			:				List[str]       						= new_scheduler.step(a__ ,  a__ ,  a__ ,  **a__					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"






      def    A__(self					)       ->   Optional[Any]:
         pass






      def    A__(self ,  a__=0 ,  **a__					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				Tuple       						= dict(self.forward_default_kwargs					)
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= kwargs.pop('''num_inference_steps''' ,  a__					)
         SCREAMING_SNAKE_CASE__			:				Tuple       						= self.dummy_sample
         SCREAMING_SNAKE_CASE__			:				Any       						= 0.1 * sample
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__			:				Dict       						= self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__			:				str       						= scheduler_class(**a__					)
            scheduler.set_timesteps(a__					)

            # copy over dummy past residuals (must be after setting timesteps)
            SCREAMING_SNAKE_CASE__			:				Tuple       						= dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__					)
               SCREAMING_SNAKE_CASE__			:				List[Any]       						= scheduler_class.from_pretrained(a__					)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(a__					)

               # copy over dummy past residual (must be after setting timesteps)
               SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= dummy_past_residuals[: new_scheduler.config.solver_order]

            SCREAMING_SNAKE_CASE__			:				str       						= scheduler.step(a__ ,  a__ ,  a__ ,  **a__					).prev_sample
            SCREAMING_SNAKE_CASE__			:				Dict       						= new_scheduler.step(a__ ,  a__ ,  a__ ,  **a__					).prev_sample

            assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"






      def    A__(self ,  a__=None ,  **a__					)       ->   int:
         if scheduler is None:
            SCREAMING_SNAKE_CASE__			:				str       						= self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.get_scheduler_config(**a__					)
            SCREAMING_SNAKE_CASE__			:				Any       						= scheduler_class(**a__					)

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= 1_0
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.dummy_model()
         SCREAMING_SNAKE_CASE__			:				str       						= self.dummy_sample_deter
         scheduler.set_timesteps(a__					)

         for i, t in enumerate(scheduler.timesteps					):
            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= model(a__ ,  a__					)
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= scheduler.step(a__ ,  a__ ,  a__					).prev_sample

         return sample






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= dict(self.forward_default_kwargs					)

         SCREAMING_SNAKE_CASE__			:				int       						= kwargs.pop('''num_inference_steps''' ,  a__					)

         for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__			:				Dict       						= self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__			:				Dict       						= scheduler_class(**a__					)

            SCREAMING_SNAKE_CASE__			:				Dict       						= self.dummy_sample
            SCREAMING_SNAKE_CASE__			:				Dict       						= 0.1 * sample

            if num_inference_steps is not None and hasattr(a__ ,  '''set_timesteps'''					):
               scheduler.set_timesteps(a__					)
            elif num_inference_steps is not None and not hasattr(a__ ,  '''set_timesteps'''					):
               SCREAMING_SNAKE_CASE__			:				Tuple       						= num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            SCREAMING_SNAKE_CASE__			:				List[str]       						= [residual + 0.2, residual + 0.15, residual + 0.10]
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= dummy_past_residuals[: scheduler.config.solver_order]

            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= scheduler.timesteps[5]
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= scheduler.timesteps[6]

            SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= scheduler.step(a__ ,  a__ ,  a__ ,  **a__					).prev_sample
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= scheduler.step(a__ ,  a__ ,  a__ ,  **a__					).prev_sample

            self.assertEqual(output_a.shape ,  sample.shape					)
            self.assertEqual(output_a.shape ,  output_a.shape					)






      def    A__(self					)       ->   List[Any]:
         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=a__					)






      def    A__(self					)       ->   int:
         self.check_over_configs(thresholding=a__					)
         for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
               for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=a__ ,  prediction_type=a__ ,  sample_max_value=a__ ,  algorithm_type='''dpmsolver++''' ,  solver_order=a__ ,  solver_type=a__ ,  )






      def    A__(self					)       ->   List[str]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=a__					)






      def    A__(self					)       ->   int:
         for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                        if order == 3:
                           continue
                     else:
                        self.check_over_configs(
                            solver_order=a__ ,  solver_type=a__ ,  prediction_type=a__ ,  algorithm_type=a__ ,  )
                     SCREAMING_SNAKE_CASE__			:				Any       						= self.full_loop(
                         solver_order=a__ ,  solver_type=a__ ,  prediction_type=a__ ,  algorithm_type=a__ ,  )
                     assert not torch.isnan(a__					).any(), "Samples have nan numbers"






      def    A__(self					)       ->   Any:
         self.check_over_configs(lower_order_final=a__					)
         self.check_over_configs(lower_order_final=a__					)






      def    A__(self					)       ->   Optional[Any]:
         self.check_over_configs(lambda_min_clipped=-float('''inf'''					)					)
         self.check_over_configs(lambda_min_clipped=-5.1					)






      def    A__(self					)       ->   Optional[int]:
         self.check_over_configs(variance_type=a__					)
         self.check_over_configs(variance_type='''learned_range'''					)






      def    A__(self					)       ->   Union[str, Any]:
         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=a__ ,  time_step=0					)






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.full_loop()
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= torch.mean(torch.abs(a__					)					)

         assert abs(result_mean.item() - 0.33_01					) < 1E-3






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.full_loop(thresholding=a__ ,  dynamic_thresholding_ratio=0.87 ,  sample_max_value=0.5					)
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= torch.mean(torch.abs(a__					)					)

         assert abs(result_mean.item() - 1.13_64					) < 1E-3






      def    A__(self					)       ->   Any:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.full_loop(prediction_type='''v_prediction'''					)
         SCREAMING_SNAKE_CASE__			:				List[str]       						= torch.mean(torch.abs(a__					)					)

         assert abs(result_mean.item() - 0.22_51					) < 1E-3






      def    A__(self					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.full_loop(prediction_type='''v_prediction''' ,  use_karras_sigmas=a__					)
         SCREAMING_SNAKE_CASE__			:				int       						= torch.mean(torch.abs(a__					)					)

         assert abs(result_mean.item() - 0.20_96					) < 1E-3






      def    A__(self					)       ->   Optional[int]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= DPMSolverMultistepScheduler(**self.get_scheduler_config()					)
         SCREAMING_SNAKE_CASE__			:				List[str]       						= self.full_loop(scheduler=a__					)
         SCREAMING_SNAKE_CASE__			:				Dict       						= torch.mean(torch.abs(a__					)					)

         assert abs(result_mean.item() - 0.33_01					) < 1E-3

         SCREAMING_SNAKE_CASE__			:				Any       						= DPMSolverSinglestepScheduler.from_config(scheduler.config					)
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= UniPCMultistepScheduler.from_config(scheduler.config					)
         SCREAMING_SNAKE_CASE__			:				str       						= DEISMultistepScheduler.from_config(scheduler.config					)
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= DPMSolverMultistepScheduler.from_config(scheduler.config					)

         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.full_loop(scheduler=a__					)
         SCREAMING_SNAKE_CASE__			:				Tuple       						= torch.mean(torch.abs(a__					)					)

         assert abs(result_mean.item() - 0.33_01					) < 1E-3






      def    A__(self					)       ->   int:
         SCREAMING_SNAKE_CASE__			:				str       						= self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE__			:				Dict       						= self.get_scheduler_config(thresholding=a__ ,  dynamic_thresholding_ratio=0					)
         SCREAMING_SNAKE_CASE__			:				str       						= scheduler_class(**a__					)

         SCREAMING_SNAKE_CASE__			:				Any       						= 1_0
         SCREAMING_SNAKE_CASE__			:				Tuple       						= self.dummy_model()
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.dummy_sample_deter.half()
         scheduler.set_timesteps(a__					)

         for i, t in enumerate(scheduler.timesteps					):
            SCREAMING_SNAKE_CASE__			:				Any       						= model(a__ ,  a__					)
            SCREAMING_SNAKE_CASE__			:				Any       						= scheduler.step(a__ ,  a__ ,  a__					).prev_sample

         assert sample.dtype == torch.floataa






      def    A__(self ,  **a__					)       ->   Dict:
         for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.get_scheduler_config(**a__					)
            SCREAMING_SNAKE_CASE__			:				List[str]       						= scheduler_class(**a__					)

            scheduler.set_timesteps(scheduler.config.num_train_timesteps					)
            assert len(scheduler.timesteps.unique()					) == scheduler.num_inference_steps







'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowercase__			=	logging.get_logger(__name__)

lowercase__			=	{'''vocab_file''': '''vocab.txt'''}

lowercase__			=	{
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

lowercase__			=	{
    '''tau/splinter-base''': 5_1_2,
    '''tau/splinter-base-qass''': 5_1_2,
    '''tau/splinter-large''': 5_1_2,
    '''tau/splinter-large-qass''': 5_1_2,
}

lowercase__			=	{
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}






class 						lowercase     (   UpperCAmelCase_):
      __lowercase 		=  VOCAB_FILES_NAMES
      __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
      __lowercase 		=  PRETRAINED_INIT_CONFIGURATION
      __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __lowercase 		=  SplinterTokenizer






      def __init__(self ,  a__=None ,  a__=None ,  a__=True ,  a__="[UNK]" ,  a__="[SEP]" ,  a__="[PAD]" ,  a__="[CLS]" ,  a__="[MASK]" ,  a__="[QUESTION]" ,  a__=True ,  a__=None ,  **a__ ,  )       ->   Any:
         super().__init__(
             a__ ,  tokenizer_file=a__ ,  do_lower_case=a__ ,  unk_token=a__ ,  sep_token=a__ ,  pad_token=a__ ,  cls_token=a__ ,  mask_token=a__ ,  tokenize_chinese_chars=a__ ,  strip_accents=a__ ,  additional_special_tokens=(question_token,) ,  **a__ ,  )

         SCREAMING_SNAKE_CASE__			:				Tuple       						= json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
         if (
             pre_tok_state.get('''lowercase''' ,  a__					) != do_lower_case
             or pre_tok_state.get('''strip_accents''' ,  a__					) != strip_accents
         ):
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= getattr(a__ ,  pre_tok_state.pop('''type'''					)					)
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case
            SCREAMING_SNAKE_CASE__			:				Optional[int]       						= strip_accents
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pre_tok_class(**a__					)

         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case






      @property
      def    A__(self					)       ->   List[str]:
         return self.convert_tokens_to_ids(self.question_token					)






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         SCREAMING_SNAKE_CASE__			:				Dict       						= [self.cls_token_id]
         SCREAMING_SNAKE_CASE__			:				Tuple       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
         if self.padding_side == "right":
            # Input is question-then-context
            return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
         else:
            # Input is context-then-question
            return cls + token_ids_a + sep + token_ids_a + question_suffix + sep






      def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.sep_token_id]
         SCREAMING_SNAKE_CASE__			:				Any       						= [self.cls_token_id]
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep					) * [0]

         if self.padding_side == "right":
            # Input is question-then-context
            return len(cls + token_ids_a + question_suffix + sep					) * [0] + len(token_ids_a + sep					) * [1]
         else:
            # Input is context-then-question
            return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + question_suffix + sep					) * [1]






      def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
         SCREAMING_SNAKE_CASE__			:				int       						= self._tokenizer.model.save(a__ ,  name=a__					)
         return tuple(a__					)

