






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class 				SCREAMING_SNAKE_CASE__		(							UpperCamelCase_		):





					"""simple docstring"""






					a_      =						(DEISMultistepScheduler,)
					a_      =						(('num_inference_steps', 2_5),)



					def 			_lowercase	(   self	:    Optional[int]				,						**__A	:    int			):
						snake_case__					: Any  =    {
						    "num_train_timesteps": 1_0_0_0,
						    "beta_start": 0.0_0_0_1,
						    "beta_end": 0.0_2,
						    "beta_schedule": "linear",
						    "solver_order": 2,
						}

						config.update(**_SCREAMING_SNAKE_CASE			)
						return config



					def 			_lowercase	(   self	:    str				,						__A	:    str=0				,						**__A	:    List[str]			):
						snake_case__					: Union[str, Any]  =    dict(self.forward_default_kwargs			)
						snake_case__					: Union[str, Any]  =    kwargs.pop("num_inference_steps"				,						_SCREAMING_SNAKE_CASE			)
						snake_case__					: Optional[int]  =    self.dummy_sample
						snake_case__					: str  =    0.1 * sample
						snake_case__					: List[Any]  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							snake_case__					: Any  =    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE			)
							snake_case__					: List[Any]  =    scheduler_class(**_SCREAMING_SNAKE_CASE			)
							scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
							# copy over dummy past residuals
							snake_case__					: List[Any]  =    dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(_SCREAMING_SNAKE_CASE			)
								snake_case__					: Union[str, Any]  =    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE			)
								new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
								# copy over dummy past residuals
								snake_case__					: Dict  =    dummy_past_residuals[: new_scheduler.config.solver_order]

							snake_case__,		snake_case__					: Any  =    sample, sample
							for t in range(_SCREAMING_SNAKE_CASE				,						time_step + scheduler.config.solver_order + 1			):
								snake_case__					: Optional[Any]  =    scheduler.step(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			).prev_sample
								snake_case__					: List[str]  =    new_scheduler.step(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			).prev_sample

								assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"



					def 			_lowercase	(   self	:    Tuple			):
						pass



					def 			_lowercase	(   self	:    Optional[int]				,						__A	:    List[Any]=0				,						**__A	:    List[str]			):
						snake_case__					: str  =    dict(self.forward_default_kwargs			)
						snake_case__					: Any  =    kwargs.pop("num_inference_steps"				,						_SCREAMING_SNAKE_CASE			)
						snake_case__					: Any  =    self.dummy_sample
						snake_case__					: List[Any]  =    0.1 * sample
						snake_case__					: int  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
							snake_case__					: str  =    self.get_scheduler_config()
							snake_case__					: Optional[Any]  =    scheduler_class(**_SCREAMING_SNAKE_CASE			)
							scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

							# copy over dummy past residuals (must be after setting timesteps)
							snake_case__					: Union[str, Any]  =    dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
								scheduler.save_config(_SCREAMING_SNAKE_CASE			)
								snake_case__					: Optional[int]  =    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE			)
								# copy over dummy past residuals
								new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

								# copy over dummy past residual (must be after setting timesteps)
								snake_case__					: List[str]  =    dummy_past_residuals[: new_scheduler.config.solver_order]

							snake_case__					: int  =    scheduler.step(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			).prev_sample
							snake_case__					: str  =    new_scheduler.step(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			).prev_sample

							assert torch.sum(torch.abs(output - new_output			)			) < 1e-5, "Scheduler outputs are not identical"



					def 			_lowercase	(   self	:    Optional[Any]				,						__A	:    int=None				,						**__A	:    Dict			):





						if scheduler is None:
							snake_case__					: Optional[Any]  =    self.scheduler_classes[0]
							snake_case__					: Optional[Any]  =    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE			)
							snake_case__					: Optional[Any]  =    scheduler_class(**_SCREAMING_SNAKE_CASE			)

						snake_case__					: str  =    self.scheduler_classes[0]
						snake_case__					: Optional[Any]  =    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE			)
						snake_case__					: Tuple  =    scheduler_class(**_SCREAMING_SNAKE_CASE			)

						snake_case__					: Optional[Any]  =    1_0
						snake_case__					: str  =    self.dummy_model()
						snake_case__					: Optional[Any]  =    self.dummy_sample_deter
						scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

						for i, t in enumerate(scheduler.timesteps			):
							snake_case__					: Dict  =    model(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)
							snake_case__					: List[Any]  =    scheduler.step(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			).prev_sample

						return sample



					def 			_lowercase	(   self	:    Tuple			):
						snake_case__					: List[Any]  =    dict(self.forward_default_kwargs			)

						snake_case__					: List[str]  =    kwargs.pop("num_inference_steps"				,						_SCREAMING_SNAKE_CASE			)

						for scheduler_class in self.scheduler_classes:
							snake_case__					: str  =    self.get_scheduler_config()
							snake_case__					: Optional[int]  =    scheduler_class(**_SCREAMING_SNAKE_CASE			)

							snake_case__					: Union[str, Any]  =    self.dummy_sample
							snake_case__					: Tuple  =    0.1 * sample

							if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE				,						"set_timesteps"			):
								scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)
							elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE				,						"set_timesteps"			):
								snake_case__					: int  =    num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							snake_case__					: str  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
							snake_case__					: List[str]  =    dummy_past_residuals[: scheduler.config.solver_order]

							snake_case__					: Any  =    scheduler.timesteps[5]
							snake_case__					: int  =    scheduler.timesteps[6]

							snake_case__					: Any  =    scheduler.step(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			).prev_sample
							snake_case__					: Optional[Any]  =    scheduler.step(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						**_SCREAMING_SNAKE_CASE			).prev_sample

							self.assertEqual(output_a.shape				,						sample.shape			)
							self.assertEqual(output_a.shape				,						output_a.shape			)



					def 			_lowercase	(   self	:    List[Any]			):
						snake_case__					: Tuple  =    DEISMultistepScheduler(**self.get_scheduler_config()			)
						snake_case__					: int  =    self.full_loop(scheduler=_SCREAMING_SNAKE_CASE			)
						snake_case__					: str  =    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

						assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3

						snake_case__					: Union[str, Any]  =    DPMSolverSinglestepScheduler.from_config(scheduler.config			)
						snake_case__					: Optional[int]  =    DPMSolverMultistepScheduler.from_config(scheduler.config			)
						snake_case__					: str  =    UniPCMultistepScheduler.from_config(scheduler.config			)
						snake_case__					: Any  =    DEISMultistepScheduler.from_config(scheduler.config			)

						snake_case__					: int  =    self.full_loop(scheduler=_SCREAMING_SNAKE_CASE			)
						snake_case__					: Tuple  =    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

						assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3



					def 			_lowercase	(   self	:    Optional[int]			):





						for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE			)



					def 			_lowercase	(   self	:    str			):
						self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE			)
						for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
								for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    thresholding=_SCREAMING_SNAKE_CASE				,						prediction_type=_SCREAMING_SNAKE_CASE				,						sample_max_value=_SCREAMING_SNAKE_CASE				,						algorithm_type="deis"				,						solver_order=_SCREAMING_SNAKE_CASE				,						solver_type=_SCREAMING_SNAKE_CASE				,						)



					def 			_lowercase	(   self	:    Tuple			):





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



					def 			_lowercase	(   self	:    Tuple			):





						for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
								for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
										self.check_over_configs(
										    solver_order=_SCREAMING_SNAKE_CASE				,						solver_type=_SCREAMING_SNAKE_CASE				,						prediction_type=_SCREAMING_SNAKE_CASE				,						algorithm_type=_SCREAMING_SNAKE_CASE				,						)
										snake_case__					: List[Any]  =    self.full_loop(
										    solver_order=_SCREAMING_SNAKE_CASE				,						solver_type=_SCREAMING_SNAKE_CASE				,						prediction_type=_SCREAMING_SNAKE_CASE				,						algorithm_type=_SCREAMING_SNAKE_CASE				,						)
										assert not torch.isnan(_SCREAMING_SNAKE_CASE			).any(), "Samples have nan numbers"



					def 			_lowercase	(   self	:    str			):
						self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE			)
						self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE			)



					def 			_lowercase	(   self	:    Any			):





						for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
							self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE				,						time_step=0			)



					def 			_lowercase	(   self	:    List[str]			):
						snake_case__					: List[str]  =    self.full_loop()
						snake_case__					: Any  =    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

						assert abs(result_mean.item() - 0.2_3_9_1_6			) < 1e-3



					def 			_lowercase	(   self	:    Optional[Any]			):
						snake_case__					: int  =    self.full_loop(prediction_type="v_prediction"			)
						snake_case__					: int  =    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE			)			)

						assert abs(result_mean.item() - 0.0_9_1			) < 1e-3






					def 			_lowercase	(   self	:    List[Any]			):
						snake_case__					: List[Any]  =    self.scheduler_classes[0]
						snake_case__					: List[str]  =    self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE				,						dynamic_thresholding_ratio=0			)
						snake_case__					: List[str]  =    scheduler_class(**_SCREAMING_SNAKE_CASE			)

						snake_case__					: Optional[Any]  =    1_0
						snake_case__					: List[str]  =    self.dummy_model()
						snake_case__					: Tuple  =    self.dummy_sample_deter.half()
						scheduler.set_timesteps(_SCREAMING_SNAKE_CASE			)

						for i, t in enumerate(scheduler.timesteps			):
							snake_case__					: Union[str, Any]  =    model(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)
							snake_case__					: Optional[int]  =    scheduler.step(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			).prev_sample

						assert sample.dtype == torch.floataa







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__    :Any       =				logging.get_logger(__name__)

lowerCAmelCase__    :Union[str, Any]       =				{
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Any           =      'xmod'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=30522     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=12     ,       _SCREAMING_SNAKE_CASE=3072     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE="absolute"     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=("en_XX",)     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     type_vocab_size
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     position_embedding_type
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     classifier_dropout
									_UpperCAmelCase											=     pre_norm
									_UpperCAmelCase											=     adapter_reduction_factor
									_UpperCAmelCase											=     adapter_layer_norm
									_UpperCAmelCase											=     adapter_reuse_layer_norm
									_UpperCAmelCase											=     ln_before_adapter
									_UpperCAmelCase											=     list(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     default_language



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase											=     {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)
