




'''simple docstring'''





import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger






class 						lowercase     (   lowerCAmelCase_):
      __lowercase 		=  'config.json'






      @register_to_config
      def __init__(self ,  a__=2 ,  a__=5 ,  a__=(2, 5) ,  a__="for diffusion" ,  a__=[1, 3] ,  )       ->   Optional[Any]:
         pass






class 						lowercase     (   lowerCAmelCase_):
      __lowercase 		=  'config.json'






      @register_to_config
      def __init__(self ,  a__=2 ,  a__=5 ,  a__=(2, 5) ,  a__="for diffusion" ,  a__=[1, 3] ,  )       ->   str:
         pass






class 						lowercase     (   lowerCAmelCase_):
      __lowercase 		=  'config.json'






      @register_to_config
      def __init__(self ,  a__=2 ,  a__=5 ,  a__=(2, 5) ,  a__="for diffusion" ,  a__=[1, 3] ,  a__=[1, 3] ,  )       ->   Dict:
         pass






class 						lowercase     (   lowerCAmelCase_):
      __lowercase 		=  'config.json'






      @register_to_config
      def __init__(self ,  a__=2 ,  a__=5 ,  a__=(2, 5) ,  a__="for diffusion" ,  a__=[1, 5] ,  a__=[5, 4] ,  )       ->   Any:
         pass






class 						lowercase     (   unittest.TestCase):






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

         with self.assertRaises(lowerCamelCase_					):
            ConfigMixin.load_config('''dummy_path'''					)






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= SampleObject()
         SCREAMING_SNAKE_CASE__			:				List[str]       						= obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]

         # init ignore private arguments
         SCREAMING_SNAKE_CASE__			:				str       						= SampleObject(_name_or_path='''lalala'''					)
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]

         # can override default
         SCREAMING_SNAKE_CASE__			:				List[str]       						= SampleObject(c=6					)
         SCREAMING_SNAKE_CASE__			:				int       						= obj.config
         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == 6
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]

         # can use positional arguments.
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= SampleObject(1 ,  c=6					)
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= obj.config
         assert config["a"] == 1
         assert config["b"] == 5
         assert config["c"] == 6
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]






      def    A__(self					)       ->   Tuple:
         SCREAMING_SNAKE_CASE__			:				int       						= SampleObject()
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= obj.config

         assert config["a"] == 2
         assert config["b"] == 5
         assert config["c"] == (2, 5)
         assert config["d"] == "for diffusion"
         assert config["e"] == [1, 3]

         with tempfile.TemporaryDirectory() as tmpdirname:
            obj.save_config(lowerCamelCase_					)
            SCREAMING_SNAKE_CASE__			:				List[str]       						= SampleObject.from_config(SampleObject.load_config(lowerCamelCase_					)					)
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= new_obj.config

         # unfreeze configs
         SCREAMING_SNAKE_CASE__			:				int       						= dict(lowerCamelCase_					)
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= dict(lowerCamelCase_					)

         assert config.pop('''c'''					) == (2, 5)  # instantiated as tuple
         assert new_config.pop('''c'''					) == [2, 5]  # saved & loaded as list because of json
         config.pop('''_use_default_values'''					)
         assert config == new_config






      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= logging.get_logger('''diffusers.configuration_utils'''					)
         # 30 for warning
         logger.setLevel(3_0					)

         with CaptureLogger(lowerCamelCase_					) as cap_logger:
            SCREAMING_SNAKE_CASE__			:				Dict       						= DDIMScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch''' ,  subfolder='''scheduler'''					)

         assert ddim.__class__ == DDIMScheduler
         # no warning should be thrown
         assert cap_logger.out == ""






      def    A__(self					)       ->   Union[str, Any]:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= logging.get_logger('''diffusers.configuration_utils'''					)
         # 30 for warning
         logger.setLevel(3_0					)

         with CaptureLogger(lowerCamelCase_					) as cap_logger:
            SCREAMING_SNAKE_CASE__			:				int       						= EulerDiscreteScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch''' ,  subfolder='''scheduler'''					)

         assert euler.__class__ == EulerDiscreteScheduler
         # no warning should be thrown
         assert cap_logger.out == ""






      def    A__(self					)       ->   Union[str, Any]:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= logging.get_logger('''diffusers.configuration_utils'''					)
         # 30 for warning
         logger.setLevel(3_0					)

         with CaptureLogger(lowerCamelCase_					) as cap_logger:
            SCREAMING_SNAKE_CASE__			:				Any       						= EulerAncestralDiscreteScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch''' ,  subfolder='''scheduler'''					)

         assert euler.__class__ == EulerAncestralDiscreteScheduler
         # no warning should be thrown
         assert cap_logger.out == ""






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= logging.get_logger('''diffusers.configuration_utils'''					)
         # 30 for warning
         logger.setLevel(3_0					)

         with CaptureLogger(lowerCamelCase_					) as cap_logger:
            SCREAMING_SNAKE_CASE__			:				Tuple       						= PNDMScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch''' ,  subfolder='''scheduler'''					)

         assert pndm.__class__ == PNDMScheduler
         # no warning should be thrown
         assert cap_logger.out == ""






      def    A__(self					)       ->   List[Any]:
         SCREAMING_SNAKE_CASE__			:				Any       						= logging.get_logger('''diffusers.configuration_utils'''					)
         # 30 for warning
         logger.setLevel(3_0					)

         with CaptureLogger(lowerCamelCase_					) as cap_logger:
            SCREAMING_SNAKE_CASE__			:				List[Any]       						= DDPMScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch''' ,  subfolder='''scheduler''' ,  prediction_type='''sample''' ,  beta_end=8 ,  )

         with CaptureLogger(lowerCamelCase_					) as cap_logger_a:
            SCREAMING_SNAKE_CASE__			:				Any       						= DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256''' ,  beta_start=8_8					)

         assert ddpm.__class__ == DDPMScheduler
         assert ddpm.config.prediction_type == "sample"
         assert ddpm.config.beta_end == 8
         assert ddpm_a.config.beta_start == 8_8

         # no warning should be thrown
         assert cap_logger.out == ""
         assert cap_logger_a.out == ""






      def    A__(self					)       ->   List[str]:
         SCREAMING_SNAKE_CASE__			:				str       						= logging.get_logger('''diffusers.configuration_utils'''					)
         # 30 for warning
         logger.setLevel(3_0					)

         with CaptureLogger(lowerCamelCase_					) as cap_logger:
            SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= DPMSolverMultistepScheduler.from_pretrained(
                '''hf-internal-testing/tiny-stable-diffusion-torch''' ,  subfolder='''scheduler'''					)

         assert dpm.__class__ == DPMSolverMultistepScheduler
         # no warning should be thrown
         assert cap_logger.out == ""






      def    A__(self					)       ->   Union[str, Any]:
         SCREAMING_SNAKE_CASE__			:				List[Any]       						= SampleObject()

         SCREAMING_SNAKE_CASE__			:				int       						= {k: v for k, v in config.config.items() if not k.startswith('''_'''					)}

         # make sure that default config has all keys in `_use_default_values`
         assert set(config_dict.keys()					) == set(config.config._use_default_values					)

         with tempfile.TemporaryDirectory() as tmpdirname:
            config.save_config(lowerCamelCase_					)

            # now loading it with SampleObject2 should put f into `_use_default_values`
            SCREAMING_SNAKE_CASE__			:				Tuple       						= SampleObjecta.from_config(lowerCamelCase_					)

            assert "f" in config._use_default_values
            assert config.f == [1, 3]

         # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
         # **BECAUSE** it is part of `config._use_default_values`
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= SampleObjecta.from_config(config.config					)
         assert new_config.f == [5, 4]

         config.config._use_default_values.pop()
         SCREAMING_SNAKE_CASE__			:				Tuple       						= SampleObjecta.from_config(config.config					)
         assert new_config_a.f == [1, 3]

         # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
         assert new_config_a.e == [1, 3]







'''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__					)

