


import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(VQDiffusionScheduler,)


     def 			_lowerCamelCase			(						self			,						**__lowerCamelCase)      ->    int:
            _A				:	Union[str, Any]       =			{
                "num_vec_classes": 4_0_9_7,
                "num_train_timesteps": 1_0_0,
            }

            config.update(**__lowerCamelCase)
            return config


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Union[str, Any]:
            _A				:	Tuple       =			4
            _A				:	Tuple       =			8
            _A				:	List[str]       =			8

            _A				:	List[str]       =			torch.randint(0			,						__lowerCamelCase			,						(batch_size, height * width))

            return sample


     @property
     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            assert False


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    List[str]:
            def model(__lowerCamelCase			,						__lowerCamelCase			,						*__lowerCamelCase):
                   _A   ,      _A				:	List[Any]       =			sample.shape
                   _A				:	List[Any]       =			torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels))
                   _A				:	Any       =			F.log_softmax(logits.double()			,						dim=1).float()
                   return return_value

            return model


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
                   self.check_over_configs(num_train_timesteps=__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    str:
            for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
                   self.check_over_configs(num_vec_classes=__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            for t in [0, 5_0, 9_9]:
                   self.check_over_forward(time_step=__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass




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


lowerCAmelCase__						=			logging.get_logger(__name__)

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

lowerCAmelCase__						=			{
    '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',
    }
}

lowerCAmelCase__						=			{
    'tau/splinter-base': 5_12,
    'tau/splinter-base-qass': 5_12,
    'tau/splinter-large': 5_12,
    'tau/splinter-large-qass': 5_12,
}

lowerCAmelCase__						=			{
    '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 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_INIT_CONFIGURATION
     __SCREAMING_SNAKE_CASE        =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE        =			SplinterTokenizer


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

            _A				:	Tuple       =			json.loads(self.backend_tokenizer.normalizer.__getstate__())
            if (
                pre_tok_state.get("lowercase"			,						__lowerCamelCase) != do_lower_case
                or pre_tok_state.get("strip_accents"			,						__lowerCamelCase) != strip_accents
            ):
                   _A				:	Dict       =			getattr(__lowerCamelCase			,						pre_tok_state.pop("type"))
                   _A				:	List[str]       =			do_lower_case
                   _A				:	Any       =			strip_accents
                   _A				:	int       =			pre_tok_class(**__lowerCamelCase)

            _A				:	int       =			do_lower_case


     @property
     def 			_lowerCamelCase			(						self)      ->    str:
            return self.convert_tokens_to_ids(self.question_token)


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

            _A				:	Tuple       =			[self.cls_token_id]
            _A				:	str       =			[self.sep_token_id]
            _A				:	int       =			[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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    List[int]:
            _A				:	str       =			[self.sep_token_id]
            _A				:	int       =			[self.cls_token_id]
            _A				:	Union[str, 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 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase = None)      ->    Tuple[str]:
            _A				:	Tuple       =			self._tokenizer.model.save(__lowerCamelCase			,						name=__lowerCamelCase)
            return tuple(__lowerCamelCase)
