


'''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


_a      :      Optional[Any]       				=      logging.get_logger(__name__)

_a      :      Any       				=      {"vocab_file": "vocab.txt"}

_a      :      List[Any]       				=      {
    "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",
    }
}

_a      :      List[Any]       				=      {
    "tau/splinter-base": 512,
    "tau/splinter-base-qass": 512,
    "tau/splinter-large": 512,
    "tau/splinter-large-qass": 512,
}

_a      :      List[Any]       				=      {
    "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   (  _SCREAMING_SNAKE_CASE       ):
   _SCREAMING_SNAKE_CASE     :     Tuple									=  VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE     :     List[str]									=  PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE     :     Any									=  PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE     :     Optional[Any]									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  SplinterTokenizer
   def __init__(	self					:			Union[str, Any]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=None					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=None					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=True					,      SCREAMING_SNAKE_CASE_					:			Dict="[UNK]"					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]="[SEP]"					,      SCREAMING_SNAKE_CASE_					:			List[Any]="[PAD]"					,      SCREAMING_SNAKE_CASE_					:			int="[CLS]"					,      SCREAMING_SNAKE_CASE_					:			str="[MASK]"					,      SCREAMING_SNAKE_CASE_					:			Tuple="[QUESTION]"					,      SCREAMING_SNAKE_CASE_					:			int=True					,      SCREAMING_SNAKE_CASE_					:			int=None					,      **SCREAMING_SNAKE_CASE_					:			Optional[int]					,      )   ->       Union[str, Any]:
        super().__init__(
            UpperCamelCase__					,      tokenizer_file=UpperCamelCase__					,      do_lower_case=UpperCamelCase__					,      unk_token=UpperCamelCase__					,      sep_token=UpperCamelCase__					,      pad_token=UpperCamelCase__					,      cls_token=UpperCamelCase__					,      mask_token=UpperCamelCase__					,      tokenize_chinese_chars=UpperCamelCase__					,      strip_accents=UpperCamelCase__					,      additional_special_tokens=(question_token,)					,      **UpperCamelCase__					,      )

        __snake_case    			=	json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
        if (
            pre_tok_state.get('lowercase'					,      UpperCamelCase__			) != do_lower_case
            or pre_tok_state.get('strip_accents'					,      UpperCamelCase__			) != strip_accents
        ):
             __snake_case    			=	getattr(UpperCamelCase__					,      pre_tok_state.pop('type'			)			)
             __snake_case    			=	do_lower_case
             __snake_case    			=	strip_accents
             __snake_case    			=	pre_tok_class(**UpperCamelCase__			)

        __snake_case    			=	do_lower_case
   @property
   def a     (	self					:			str			)   ->       Any:
        return self.convert_tokens_to_ids(self.question_token			)
   def a     (	self					:			Optional[Any]					,      SCREAMING_SNAKE_CASE_					:			List[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[List[int]] = None			)   ->       Optional[int]:
        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

        __snake_case    			=	[self.cls_token_id]
        __snake_case    			=	[self.sep_token_id]
        __snake_case    			=	[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					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[int]					,      SCREAMING_SNAKE_CASE_					:			Optional[List[int]] = None			)   ->       Tuple:
        __snake_case    			=	[self.sep_token_id]
        __snake_case    			=	[self.cls_token_id]
        __snake_case    			=	[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					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			str					,      SCREAMING_SNAKE_CASE_					:			Optional[str] = None			)   ->       Optional[Any]:
        __snake_case    			=	self._tokenizer.model.save(UpperCamelCase__					,      name=UpperCamelCase__			)
        return tuple(UpperCamelCase__			)








'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]					,      [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3




