

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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Tuple									=				(DPMSolverSDEScheduler,)
			SCREAMING_SNAKE_CASE     :   str									=				10







			def 				snake_case__(     self :				Optional[int]							,   **_UpperCamelCase :				str							)      ->Optional[Any]:
									snake_case_   =	{
									    '''num_train_timesteps''': 1_1_0_0,
									    '''beta_start''': 0.0001,
									    '''beta_end''': 0.02,
									    '''beta_schedule''': '''linear''',
									    '''noise_sampler_seed''': 0,
									}

									config.update(**_UpperCamelCase							)
									return config







			def 				snake_case__(     self :				Optional[Any]							)      ->Any:
									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							)      ->int:
									for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]							,   [0.0002, 0.002, 0.02]							):
															self.check_over_configs(beta_start=_UpperCamelCase							,   beta_end=_UpperCamelCase							)







			def 				snake_case__(     self :				List[str]							)      ->Union[str, Any]:
									for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=_UpperCamelCase							)







			def 				snake_case__(     self :				List[Any]							)      ->Optional[Any]:
									for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=_UpperCamelCase							)







			def 				snake_case__(     self :				Optional[int]							)      ->int:
									snake_case_   =	self.scheduler_classes[0]
									snake_case_   =	self.get_scheduler_config()
									snake_case_   =	scheduler_class(**_UpperCamelCase							)

									scheduler.set_timesteps(self.num_inference_steps							)

									snake_case_   =	self.dummy_model()
									snake_case_   =	self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case_   =	sample.to(_UpperCamelCase							)

									for i, t in enumerate(scheduler.timesteps							):
															snake_case_   =	scheduler.scale_model_input(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	output.prev_sample

									snake_case_   =	torch.sum(torch.abs(_UpperCamelCase							)							)
									snake_case_   =	torch.mean(torch.abs(_UpperCamelCase							)							)

									if torch_device in ["mps"]:
															assert abs(result_sum.item() - 167.47821044921875							) < 1e-2
															assert abs(result_mean.item() - 0.2178705964565277							) < 1e-3
									elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 171.59352111816406							) < 1e-2
															assert abs(result_mean.item() - 0.22342906892299652							) < 1e-3
									else:
															assert abs(result_sum.item() - 162.52383422851562							) < 1e-2
															assert abs(result_mean.item() - 0.211619570851326							) < 1e-3







			def 				snake_case__(     self :				Optional[Any]							)      ->str:
									snake_case_   =	self.scheduler_classes[0]
									snake_case_   =	self.get_scheduler_config(prediction_type='''v_prediction'''							)
									snake_case_   =	scheduler_class(**_UpperCamelCase							)

									scheduler.set_timesteps(self.num_inference_steps							)

									snake_case_   =	self.dummy_model()
									snake_case_   =	self.dummy_sample_deter * scheduler.init_noise_sigma
									snake_case_   =	sample.to(_UpperCamelCase							)

									for i, t in enumerate(scheduler.timesteps							):
															snake_case_   =	scheduler.scale_model_input(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	model(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	output.prev_sample

									snake_case_   =	torch.sum(torch.abs(_UpperCamelCase							)							)
									snake_case_   =	torch.mean(torch.abs(_UpperCamelCase							)							)

									if torch_device in ["mps"]:
															assert abs(result_sum.item() - 124.77149200439453							) < 1e-2
															assert abs(result_mean.item() - 0.16226289014816284							) < 1e-3
									elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 128.1663360595703							) < 1e-2
															assert abs(result_mean.item() - 0.16688326001167297							) < 1e-3
									else:
															assert abs(result_sum.item() - 119.8487548828125							) < 1e-2
															assert abs(result_mean.item() - 0.1560530662536621							) < 1e-3







			def 				snake_case__(     self :				List[str]							)      ->Union[str, Any]:
									snake_case_   =	self.scheduler_classes[0]
									snake_case_   =	self.get_scheduler_config()
									snake_case_   =	scheduler_class(**_UpperCamelCase							)

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

									snake_case_   =	self.dummy_model()
									snake_case_   =	self.dummy_sample_deter.to(_UpperCamelCase							) * scheduler.init_noise_sigma

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

															snake_case_   =	model(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	output.prev_sample

									snake_case_   =	torch.sum(torch.abs(_UpperCamelCase							)							)
									snake_case_   =	torch.mean(torch.abs(_UpperCamelCase							)							)

									if torch_device in ["mps"]:
															assert abs(result_sum.item() - 167.46957397460938							) < 1e-2
															assert abs(result_mean.item() - 0.21805934607982635							) < 1e-3
									elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 171.59353637695312							) < 1e-2
															assert abs(result_mean.item() - 0.22342908382415771							) < 1e-3
									else:
															assert abs(result_sum.item() - 162.52383422851562							) < 1e-2
															assert abs(result_mean.item() - 0.211619570851326							) < 1e-3







			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	self.scheduler_classes[0]
									snake_case_   =	self.get_scheduler_config()
									snake_case_   =	scheduler_class(**_UpperCamelCase							,   use_karras_sigmas=_UpperCamelCase							)

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

									snake_case_   =	self.dummy_model()
									snake_case_   =	self.dummy_sample_deter.to(_UpperCamelCase							) * scheduler.init_noise_sigma
									snake_case_   =	sample.to(_UpperCamelCase							)

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

															snake_case_   =	model(_UpperCamelCase							,   _UpperCamelCase							)

															snake_case_   =	scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	output.prev_sample

									snake_case_   =	torch.sum(torch.abs(_UpperCamelCase							)							)
									snake_case_   =	torch.mean(torch.abs(_UpperCamelCase							)							)

									if torch_device in ["mps"]:
															assert abs(result_sum.item() - 176.66974135742188							) < 1e-2
															assert abs(result_mean.item() - 0.23003872730981811							) < 1e-2
									elif torch_device in ["cuda"]:
															assert abs(result_sum.item() - 177.63653564453125							) < 1e-2
															assert abs(result_mean.item() - 0.23003872730981811							) < 1e-2
									else:
															assert abs(result_sum.item() - 170.3135223388672							) < 1e-2
															assert abs(result_mean.item() - 0.23003872730981811							) < 1e-2






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__A									=				logging.get_logger(__name__)

__A									=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A									=				{
    "vocab_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "yjernite/retribert-base-uncased": (
            "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

__A									=				{
    "yjernite/retribert-base-uncased": 512,
}


__A									=				{
    "yjernite/retribert-base-uncased": {"do_lower_case": True},
}



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			VOCAB_FILES_NAMES
 lowercase_		=			PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=			PRETRAINED_INIT_CONFIGURATION
 lowercase_		=			RetriBertTokenizer
 lowercase_		=			["input_ids", "attention_mask"]

 def __init__(self				:   int       , UpperCAmelCase_				:   List[str]=None       , UpperCAmelCase_				:   str=None       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   Union[str, Any]="[UNK]"       , UpperCAmelCase_				:   Any="[SEP]"       , UpperCAmelCase_				:   List[str]="[PAD]"       , UpperCAmelCase_				:   Optional[Any]="[CLS]"       , UpperCAmelCase_				:   Optional[Any]="[MASK]"       , UpperCAmelCase_				:   Dict=True       , UpperCAmelCase_				:   str=None       , **UpperCAmelCase_				:   str       , )       ->List[Any]:



   '''simple docstring'''



   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_       , **UpperCAmelCase_       , )

   lowerCamelCase__:      List[Any]			  =json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"       , UpperCAmelCase_) != do_lower_case
       or normalizer_state.get("strip_accents"       , UpperCAmelCase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"       , UpperCAmelCase_) != tokenize_chinese_chars
   ):
     lowerCamelCase__:      Dict			  =getattr(UpperCAmelCase_       , normalizer_state.pop("type"))
     lowerCamelCase__:      int			  =do_lower_case
     lowerCamelCase__:      int			  =strip_accents
     lowerCamelCase__:      List[str]			  =tokenize_chinese_chars
     lowerCamelCase__:      Tuple			  =normalizer_class(**UpperCAmelCase_)

   lowerCamelCase__:      Any			  =do_lower_case

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[Any]=None)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[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 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   List[int]       , UpperCAmelCase_				:   Optional[List[int]] = None)       ->List[int]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =[self.sep_token_id]
   lowerCamelCase__:      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 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[str] = None)       ->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self._tokenizer.model.save(UpperCAmelCase_       , name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)

