

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 				SCREAMING_SNAKE_CASE__ (lowercase__					):
    __lowerCamelCase						:	Union[str, Any]														=					(DPMSolverSDEScheduler,)
    __lowerCamelCase						:	Dict														=					10





    def 				snake_case_    (				self						,       **a):
         lowercase__   :       Union[str, Any]				=	{
             """num_train_timesteps""": 1100,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """noise_sampler_seed""": 0,
         }

         config.update(**_UpperCamelCase)
         return config





    def 				snake_case_    (				self):
         for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_UpperCamelCase)





    def 				snake_case_    (				self):
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]):
              self.check_over_configs(beta_start=_UpperCamelCase						,       beta_end=_UpperCamelCase)





    def 				snake_case_    (				self):
         for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=_UpperCamelCase)





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





    def 				snake_case_    (				self):
         lowercase__   :       Any				=	self.scheduler_classes[0]
         lowercase__   :       Dict				=	self.get_scheduler_config()
         lowercase__   :       str				=	scheduler_class(**_UpperCamelCase)

         scheduler.set_timesteps(self.num_inference_steps)

         lowercase__   :       Dict				=	self.dummy_model()
         lowercase__   :       Optional[Any]				=	self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase__   :       Any				=	sample.to(_UpperCamelCase)

         for i, t in enumerate(scheduler.timesteps):
              lowercase__   :       List[Any]				=	scheduler.scale_model_input(_UpperCamelCase						,       _UpperCamelCase)

              lowercase__   :       Tuple				=	model(_UpperCamelCase						,       _UpperCamelCase)

              lowercase__   :       List[Any]				=	scheduler.step(_UpperCamelCase						,       _UpperCamelCase						,       _UpperCamelCase)
              lowercase__   :       Any				=	output.prev_sample

         lowercase__   :       Tuple				=	torch.sum(torch.abs(_UpperCamelCase))
         lowercase__   :       List[str]				=	torch.mean(torch.abs(_UpperCamelCase))

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
              assert abs(result_mean.item() - 0.2_178_705_964_565_277) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
              assert abs(result_mean.item() - 0.22_342_906_892_299_652) < 1e-3
         else:
              assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
              assert abs(result_mean.item() - 0.211_619_570_851_326) < 1e-3





    def 				snake_case_    (				self):
         lowercase__   :       str				=	self.scheduler_classes[0]
         lowercase__   :       Any				=	self.get_scheduler_config(prediction_type='v_prediction')
         lowercase__   :       Tuple				=	scheduler_class(**_UpperCamelCase)

         scheduler.set_timesteps(self.num_inference_steps)

         lowercase__   :       Optional[int]				=	self.dummy_model()
         lowercase__   :       Dict				=	self.dummy_sample_deter * scheduler.init_noise_sigma
         lowercase__   :       int				=	sample.to(_UpperCamelCase)

         for i, t in enumerate(scheduler.timesteps):
              lowercase__   :       List[str]				=	scheduler.scale_model_input(_UpperCamelCase						,       _UpperCamelCase)

              lowercase__   :       int				=	model(_UpperCamelCase						,       _UpperCamelCase)

              lowercase__   :       Tuple				=	scheduler.step(_UpperCamelCase						,       _UpperCamelCase						,       _UpperCamelCase)
              lowercase__   :       str				=	output.prev_sample

         lowercase__   :       Dict				=	torch.sum(torch.abs(_UpperCamelCase))
         lowercase__   :       Optional[Any]				=	torch.mean(torch.abs(_UpperCamelCase))

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
              assert abs(result_mean.item() - 0.16_226_289_014_816_284) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
              assert abs(result_mean.item() - 0.16_688_326_001_167_297) < 1e-3
         else:
              assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
              assert abs(result_mean.item() - 0.1_560_530_662_536_621) < 1e-3





    def 				snake_case_    (				self):
         lowercase__   :       Tuple				=	self.scheduler_classes[0]
         lowercase__   :       List[str]				=	self.get_scheduler_config()
         lowercase__   :       Optional[Any]				=	scheduler_class(**_UpperCamelCase)

         scheduler.set_timesteps(self.num_inference_steps						,       device=_UpperCamelCase)

         lowercase__   :       Optional[Any]				=	self.dummy_model()
         lowercase__   :       Optional[int]				=	self.dummy_sample_deter.to(_UpperCamelCase) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
              lowercase__   :       Dict				=	scheduler.scale_model_input(_UpperCamelCase						,       _UpperCamelCase)

              lowercase__   :       str				=	model(_UpperCamelCase						,       _UpperCamelCase)

              lowercase__   :       List[str]				=	scheduler.step(_UpperCamelCase						,       _UpperCamelCase						,       _UpperCamelCase)
              lowercase__   :       List[str]				=	output.prev_sample

         lowercase__   :       Dict				=	torch.sum(torch.abs(_UpperCamelCase))
         lowercase__   :       str				=	torch.mean(torch.abs(_UpperCamelCase))

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
              assert abs(result_mean.item() - 0.21_805_934_607_982_635) < 1e-3
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
              assert abs(result_mean.item() - 0.22_342_908_382_415_771) < 1e-3
         else:
              assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
              assert abs(result_mean.item() - 0.211_619_570_851_326) < 1e-3





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	self.scheduler_classes[0]
         lowercase__   :       Any				=	self.get_scheduler_config()
         lowercase__   :       List[str]				=	scheduler_class(**_UpperCamelCase						,       use_karras_sigmas=_UpperCamelCase)

         scheduler.set_timesteps(self.num_inference_steps						,       device=_UpperCamelCase)

         lowercase__   :       Dict				=	self.dummy_model()
         lowercase__   :       Tuple				=	self.dummy_sample_deter.to(_UpperCamelCase) * scheduler.init_noise_sigma
         lowercase__   :       List[Any]				=	sample.to(_UpperCamelCase)

         for t in scheduler.timesteps:
              lowercase__   :       Any				=	scheduler.scale_model_input(_UpperCamelCase						,       _UpperCamelCase)

              lowercase__   :       int				=	model(_UpperCamelCase						,       _UpperCamelCase)

              lowercase__   :       List[Any]				=	scheduler.step(_UpperCamelCase						,       _UpperCamelCase						,       _UpperCamelCase)
              lowercase__   :       Any				=	output.prev_sample

         lowercase__   :       Optional[Any]				=	torch.sum(torch.abs(_UpperCamelCase))
         lowercase__   :       Dict				=	torch.mean(torch.abs(_UpperCamelCase))

         if torch_device in ["mps"]:
              assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1e-2
         elif torch_device in ["cuda"]:
              assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1e-2
         else:
              assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
              assert abs(result_mean.item() - 0.23_003_872_730_981_811) < 1e-2




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

snake_case_        =			{
    '''junnyu/roformer_chinese_small''': 1_536,
    '''junnyu/roformer_chinese_base''': 1_536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


snake_case_        =			{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	int														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	Any														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	int														=					PRETRAINED_INIT_CONFIGURATION
    __lowerCamelCase						:	Tuple														=					RoFormerTokenizer





    def __init__(				self						,       a=None						,       a=None						,       a=True						,       a="[UNK]"						,       a="[SEP]"						,       a="[PAD]"						,       a="[CLS]"						,       a="[MASK]"						,       a=True						,       a=None						,       **a						,       ):
         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						,       **a						,       )

         lowercase__   :       Optional[Any]				=	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
         ):
              lowercase__   :       Optional[int]				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       str				=	do_lower_case
              lowercase__   :       Union[str, Any]				=	strip_accents
              lowercase__   :       int				=	pre_tok_class(**a)

         lowercase__   :       Optional[int]				=	do_lower_case





    def __getstate__(				self):
         lowercase__   :       str				=	self.__dict__.copy()
         lowercase__   :       Any				=	BertPreTokenizer()
         return state





    def __setstate__(				self						,       a):
         lowercase__   :       Union[str, Any]				=	d
         lowercase__   :       int				=	self.__dict__['_tokenizer'].get_vocab()
         lowercase__   :       List[str]				=	PreTokenizer.custom(JiebaPreTokenizer(a))





    def 				snake_case_    (				self						,       a						,       a=None):
         lowercase__   :       Union[str, Any]				=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

         return output





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Tuple				=	[self.sep_token_id]
         lowercase__   :       Optional[int]				=	[self.cls_token_id]
         if token_ids_a is None:
              return len(cls + token_ids_a + sep) * [0]
         return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Optional[Any]				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a						,       a=None						,       a=None						,       a=False						,       **a						,       ):
         lowercase__   :       List[str]				=	BertPreTokenizer()
         return super().save_pretrained(a						,       a						,       a						,       a						,       **a)

