





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        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  (DPMSolverSDEScheduler,)
     __UpperCamelCase            =  10



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   **snake_case							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					int        =      {
            "num_train_timesteps": 1_100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "noise_sampler_seed": 0,
        }

        config.update(**SCREAMING_SNAKE_CASE_	)
        return config



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        for timesteps in [10, 50, 100, 1_000]:
           self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_	)



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        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=SCREAMING_SNAKE_CASE_      ,   beta_end=SCREAMING_SNAKE_CASE_	)



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					Tuple        =      self.scheduler_classes[0]
        A_       :					int        =      self.get_scheduler_config()
        A_       :					Union[str, Any]        =      scheduler_class(**SCREAMING_SNAKE_CASE_	)

        scheduler.set_timesteps(self.num_inference_steps	)

        A_       :					List[str]        =      self.dummy_model()
        A_       :					int        =      self.dummy_sample_deter * scheduler.init_noise_sigma
        A_       :					str        =      sample.to(SCREAMING_SNAKE_CASE_	)

        for i, t in enumerate(scheduler.timesteps	):
           A_       :					Optional[int]        =      scheduler.scale_model_input(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)

           A_       :					str        =      model(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)

           A_       :					List[str]        =      scheduler.step(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)
           A_       :					Any        =      output.prev_sample

        A_       :					Tuple        =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        A_       :					Dict        =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        if torch_device in ["mps"]:
           assert abs(result_sum.item() - 167.47_821_044_921_875	) < 1e-2
           assert abs(result_mean.item() - 0.2178705964565277	) < 1e-3
        elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 171.59_352_111_816_406	) < 1e-2
           assert abs(result_mean.item() - 0.22342906892299652	) < 1e-3
        else:
           assert abs(result_sum.item() - 162.52_383_422_851_562	) < 1e-2
           assert abs(result_mean.item() - 0.211619570851326	) < 1e-3



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					str        =      self.scheduler_classes[0]
        A_       :					Optional[int]        =      self.get_scheduler_config(prediction_type="v_prediction"	)
        A_       :					Any        =      scheduler_class(**SCREAMING_SNAKE_CASE_	)

        scheduler.set_timesteps(self.num_inference_steps	)

        A_       :					List[str]        =      self.dummy_model()
        A_       :					str        =      self.dummy_sample_deter * scheduler.init_noise_sigma
        A_       :					Union[str, Any]        =      sample.to(SCREAMING_SNAKE_CASE_	)

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

           A_       :					Tuple        =      model(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)

           A_       :					Optional[int]        =      scheduler.step(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)
           A_       :					str        =      output.prev_sample

        A_       :					Optional[int]        =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        A_       :					Any        =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        if torch_device in ["mps"]:
           assert abs(result_sum.item() - 124.77_149_200_439_453	) < 1e-2
           assert abs(result_mean.item() - 0.16226289014816284	) < 1e-3
        elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 128.1_663_360_595_703	) < 1e-2
           assert abs(result_mean.item() - 0.16688326001167297	) < 1e-3
        else:
           assert abs(result_sum.item() - 119.8_487_548_828_125	) < 1e-2
           assert abs(result_mean.item() - 0.1560530662536621	) < 1e-3



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					List[str]        =      self.scheduler_classes[0]
        A_       :					List[str]        =      self.get_scheduler_config()
        A_       :					Optional[Any]        =      scheduler_class(**SCREAMING_SNAKE_CASE_	)

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

        A_       :					Tuple        =      self.dummy_model()
        A_       :					Tuple        =      self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_	) * scheduler.init_noise_sigma

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

           A_       :					str        =      model(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)

           A_       :					Any        =      scheduler.step(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)
           A_       :					str        =      output.prev_sample

        A_       :					str        =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        A_       :					Union[str, Any]        =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        if torch_device in ["mps"]:
           assert abs(result_sum.item() - 167.46_957_397_460_938	) < 1e-2
           assert abs(result_mean.item() - 0.21805934607982635	) < 1e-3
        elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 171.59_353_637_695_312	) < 1e-2
           assert abs(result_mean.item() - 0.22342908382415771	) < 1e-3
        else:
           assert abs(result_sum.item() - 162.52_383_422_851_562	) < 1e-2
           assert abs(result_mean.item() - 0.211619570851326	) < 1e-3





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      self.scheduler_classes[0]
        A_       :					List[str]        =      self.get_scheduler_config()
        A_       :					List[str]        =      scheduler_class(**SCREAMING_SNAKE_CASE_      ,   use_karras_sigmas=SCREAMING_SNAKE_CASE_	)

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

        A_       :					Tuple        =      self.dummy_model()
        A_       :					Any        =      self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_	) * scheduler.init_noise_sigma
        A_       :					str        =      sample.to(SCREAMING_SNAKE_CASE_	)

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

           A_       :					Any        =      model(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)

           A_       :					Union[str, Any]        =      scheduler.step(SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_      ,   SCREAMING_SNAKE_CASE_	)
           A_       :					str        =      output.prev_sample

        A_       :					Dict        =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_	)	)
        A_       :					List[str]        =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_	)	)

        if torch_device in ["mps"]:
           assert abs(result_sum.item() - 176.66_974_135_742_188	) < 1e-2
           assert abs(result_mean.item() - 0.23003872730981811	) < 1e-2
        elif torch_device in ["cuda"]:
           assert abs(result_sum.item() - 177.63_653_564_453_125	) < 1e-2
           assert abs(result_mean.item() - 0.23003872730981811	) < 1e-2
        else:
           assert abs(result_sum.item() - 170.3_135_223_388_672	) < 1e-2
           assert abs(result_mean.item() - 0.23003872730981811	) < 1e-2


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a         =   logging.get_logger(__name__)

__a         =   {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[Any]       	=      'xlm'
	a     :Dict       	=      {
	    'hidden_size': 'emb_dim',
	    'num_attention_heads': 'n_heads',
	    'num_hidden_layers': 'n_layers',
	    'n_words': 'vocab_size',  # For backward compatibility
	}
	def __init__(			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict=3_0_1_4_5			,				SCREAMING_SNAKE_CASE_    :     Dict=2_0_4_8			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_6			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     str=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Dict=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=2_0_4_8**-0.5			,				SCREAMING_SNAKE_CASE_    :     Dict=1e-12			,				SCREAMING_SNAKE_CASE_    :     Any=0.02			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2			,				SCREAMING_SNAKE_CASE_    :     int=3			,				SCREAMING_SNAKE_CASE_    :     Dict=5			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]="first"			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				SCREAMING_SNAKE_CASE_    :     str=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=5			,				SCREAMING_SNAKE_CASE_    :     List[str]=5			,				SCREAMING_SNAKE_CASE_    :     Any=0			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0			,				**SCREAMING_SNAKE_CASE_    :     Dict			,				)			->	List[Any]:
					lowercase_	    =   vocab_size
					lowercase_	    =   emb_dim
					lowercase_	    =   n_layers
					lowercase_	    =   n_heads
					lowercase_	    =   dropout
					lowercase_	    =   attention_dropout
					lowercase_	    =   gelu_activation
					lowercase_	    =   sinusoidal_embeddings
					lowercase_	    =   causal
					lowercase_	    =   asm
					lowercase_	    =   n_langs
					lowercase_	    =   use_lang_emb
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   bos_index
					lowercase_	    =   eos_index
					lowercase_	    =   pad_index
					lowercase_	    =   unk_index
					lowercase_	    =   mask_index
					lowercase_	    =   is_encoder
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   embed_init_std
					lowercase_	    =   init_std
					lowercase_	    =   summary_type
					lowercase_	    =   summary_use_proj
					lowercase_	    =   summary_activation
					lowercase_	    =   summary_proj_to_labels
					lowercase_	    =   summary_first_dropout
					lowercase_	    =   start_n_top
					lowercase_	    =   end_n_top
					lowercase_	    =   mask_token_id
					lowercase_	    =   lang_id

					if "n_words" in kwargs:
									lowercase_	    =   kwargs['''n_words''']

					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_			,				bos_token_id=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	@property
	def 		_lowercase    (			self    :     int			)			->	Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
									lowercase_	    =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
									lowercase_	    =   {0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					        ('''token_type_ids''', dynamic_axis),
					    ]			)




