
import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase   :				Tuple     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Tuple     			=						{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}


class   lowercase      (       a       ):
							lowercase__  :    Optional[int]									=							"""pix2struct_text_model"""
							lowercase__  :    List[Any]									=							["""past_key_values"""]
							lowercase__  :    List[Any]									=							{
							    """hidden_size""": """hidden_size""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}




							def __init__(  self				:   int		,							_UpperCamelCase				:   Union[str, Any]=50_244		,							_UpperCamelCase				:   Tuple=768		,							_UpperCamelCase				:   Tuple=64		,							_UpperCamelCase				:   List[str]=2_048		,							_UpperCamelCase				:   Optional[Any]=12		,							_UpperCamelCase				:   int=12		,							_UpperCamelCase				:   Optional[Any]=32		,							_UpperCamelCase				:   int=128		,							_UpperCamelCase				:   Union[str, Any]=0.1		,							_UpperCamelCase				:   Any=1e-6		,							_UpperCamelCase				:   int=1.0		,							_UpperCamelCase				:   Union[str, Any]="gelu_new"		,							_UpperCamelCase				:   Optional[int]=0		,							_UpperCamelCase				:   List[Any]=False		,							_UpperCamelCase				:   List[str]=0		,							_UpperCamelCase				:   Any=1		,							_UpperCamelCase				:   Tuple=False		,							_UpperCamelCase				:   Any=True		,							**_UpperCamelCase				:   Tuple		,							)   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    vocab_size
												SCREAMING_SNAKE_CASE           =    hidden_size
												SCREAMING_SNAKE_CASE           =    d_kv
												SCREAMING_SNAKE_CASE           =    d_ff
												SCREAMING_SNAKE_CASE           =    num_layers
												SCREAMING_SNAKE_CASE           =    num_heads
												SCREAMING_SNAKE_CASE           =    relative_attention_num_buckets
												SCREAMING_SNAKE_CASE           =    relative_attention_max_distance
												SCREAMING_SNAKE_CASE           =    dropout_rate
												SCREAMING_SNAKE_CASE           =    layer_norm_epsilon
												SCREAMING_SNAKE_CASE           =    initializer_factor
												SCREAMING_SNAKE_CASE           =    use_cache

												SCREAMING_SNAKE_CASE           =    eos_token_id
												SCREAMING_SNAKE_CASE           =    decoder_start_token_id

												# for backwards compatibility
												SCREAMING_SNAKE_CASE           =    dense_act_fn

												super().__init__(
												    pad_token_id=_UpperCamelCase		,							eos_token_id=_UpperCamelCase		,							decoder_start_token_id=_UpperCamelCase		,							tie_word_embeddings=_UpperCamelCase		,							is_decoder=_UpperCamelCase		,							**_UpperCamelCase		,							)





							@classmethod
							def 		__snake_case(  cls				:   Union[str, Any]		,							_UpperCamelCase				:   Union[str, os.PathLike]		,							**_UpperCamelCase				:   Union[str, Any] )   ->			"PretrainedConfig":



												'''simple docstring'''

												cls._set_token_in_kwargs(_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    cls.get_config_dict(_UpperCamelCase		,							**_UpperCamelCase )

												# get the text config dict if we are loading from Pix2StructConfig
												if config_dict.get("model_type" ) == "pix2struct":
																	SCREAMING_SNAKE_CASE           =    config_dict["text_config"]

												if "model_type" in config_dict and hasattr(cls		,							"model_type" ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
																	    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )

												return cls.from_dict(_UpperCamelCase		,							**_UpperCamelCase )



class   lowercase      (       a       ):
							lowercase__  :    Union[str, Any]									=							"""pix2struct_vision_model"""




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   List[Any]=768		,							_UpperCamelCase				:   Union[str, Any]=768		,							_UpperCamelCase				:   Optional[Any]=2_048		,							_UpperCamelCase				:   Optional[int]=64		,							_UpperCamelCase				:   int=12		,							_UpperCamelCase				:   int=12		,							_UpperCamelCase				:   Tuple="gelu_new"		,							_UpperCamelCase				:   List[str]=1e-6		,							_UpperCamelCase				:   str=0.0		,							_UpperCamelCase				:   Dict=0.0		,							_UpperCamelCase				:   Any=1e-10		,							_UpperCamelCase				:   Any=1.0		,							_UpperCamelCase				:   Tuple=4_096		,							_UpperCamelCase				:   Tuple=32		,							_UpperCamelCase				:   Any=128		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			Optional[Any]:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    hidden_size
												SCREAMING_SNAKE_CASE           =    patch_embed_hidden_size
												SCREAMING_SNAKE_CASE           =    d_ff
												SCREAMING_SNAKE_CASE           =    dropout_rate
												SCREAMING_SNAKE_CASE           =    num_hidden_layers
												SCREAMING_SNAKE_CASE           =    num_attention_heads
												SCREAMING_SNAKE_CASE           =    initializer_range
												SCREAMING_SNAKE_CASE           =    initializer_factor
												SCREAMING_SNAKE_CASE           =    attention_dropout
												SCREAMING_SNAKE_CASE           =    layer_norm_eps
												SCREAMING_SNAKE_CASE           =    dense_act_fn
												SCREAMING_SNAKE_CASE           =    seq_len
												SCREAMING_SNAKE_CASE           =    relative_attention_num_buckets
												SCREAMING_SNAKE_CASE           =    relative_attention_max_distance
												SCREAMING_SNAKE_CASE           =    d_kv





							@classmethod
							def 		__snake_case(  cls				:   Dict		,							_UpperCamelCase				:   Union[str, os.PathLike]		,							**_UpperCamelCase				:   Optional[int] )   ->			"PretrainedConfig":



												'''simple docstring'''

												cls._set_token_in_kwargs(_UpperCamelCase )

												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    cls.get_config_dict(_UpperCamelCase		,							**_UpperCamelCase )

												# get the vision config dict if we are loading from Pix2StructConfig
												if config_dict.get("model_type" ) == "pix2struct":
																	SCREAMING_SNAKE_CASE           =    config_dict["vision_config"]

												if "model_type" in config_dict and hasattr(cls		,							"model_type" ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
																	    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )

												return cls.from_dict(_UpperCamelCase		,							**_UpperCamelCase )



class   lowercase      (       a       ):
							lowercase__  :    int									=							"""pix2struct"""
							lowercase__  :    Optional[Any]									=							True




							def __init__(  self				:   Any		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   List[Any]=None		,							_UpperCamelCase				:   Union[str, Any]=1.0		,							_UpperCamelCase				:   Union[str, Any]=0.0_2		,							_UpperCamelCase				:   Tuple=False		,							_UpperCamelCase				:   List[str]=False		,							_UpperCamelCase				:   Any=True		,							**_UpperCamelCase				:   List[str]		,							)   ->			str:



												'''simple docstring'''

												super().__init__(tie_word_embeddings=_UpperCamelCase		,							is_encoder_decoder=_UpperCamelCase		,							**_UpperCamelCase )

												if text_config is None:
																	SCREAMING_SNAKE_CASE           =    {}
																	logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values." )

												if vision_config is None:
																	SCREAMING_SNAKE_CASE           =    {}
																	logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values." )

												SCREAMING_SNAKE_CASE           =    PixaStructTextConfig(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    PixaStructVisionConfig(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.text_config.decoder_start_token_id
												SCREAMING_SNAKE_CASE           =    self.text_config.pad_token_id
												SCREAMING_SNAKE_CASE           =    self.text_config.eos_token_id

												SCREAMING_SNAKE_CASE           =    initializer_factor
												SCREAMING_SNAKE_CASE           =    initializer_range

												SCREAMING_SNAKE_CASE           =    self.initializer_range
												SCREAMING_SNAKE_CASE           =    self.initializer_range

												SCREAMING_SNAKE_CASE           =    is_vqa




							@classmethod
							def 		__snake_case(  cls				:   List[str]		,							_UpperCamelCase				:   PixaStructTextConfig		,							_UpperCamelCase				:   PixaStructVisionConfig		,							**_UpperCamelCase				:   Tuple )   ->			List[str]:



												'''simple docstring'''

												return cls(text_config=text_config.to_dict()		,							vision_config=vision_config.to_dict()		,							**_UpperCamelCase )





							def 		__snake_case(  self				:   Any )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    copy.deepcopy(self.__dict__ )
												SCREAMING_SNAKE_CASE           =    self.text_config.to_dict()
												SCREAMING_SNAKE_CASE           =    self.vision_config.to_dict()
												SCREAMING_SNAKE_CASE           =    self.__class__.model_type
												return output





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class   lowercase      (       a       ):
							lowercase__  :    Dict									=							(UniPCMultistepScheduler,)
							lowercase__  :    Optional[int]									=							(("""num_inference_steps""", 25),)




							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {
												    "num_train_timesteps": 1_000,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												    "solver_order": 2,
												    "solver_type": "bh2",
												}

												config.update(**_UpperCamelCase )
												return config




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Dict=0		,							**_UpperCamelCase				:   List[str] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    scheduler_class.from_pretrained(_UpperCamelCase )
																						new_scheduler.set_timesteps(_UpperCamelCase )
																						# copy over dummy past residuals
																						SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: new_scheduler.config.solver_order]

																	SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    sample, sample
																	for t in range(_UpperCamelCase		,							time_step + scheduler.config.solver_order + 1 ):
																						SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																						SCREAMING_SNAKE_CASE           =    new_scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Union[str, Any]=0		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )
												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.dummy_sample
												SCREAMING_SNAKE_CASE           =    0.1 * sample
												SCREAMING_SNAKE_CASE           =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )
																	scheduler.set_timesteps(_UpperCamelCase )

																	# copy over dummy past residuals (must be after setting timesteps)
																	SCREAMING_SNAKE_CASE           =    dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    scheduler_class.from_pretrained(_UpperCamelCase )
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(_UpperCamelCase )

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

																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    new_scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Tuple=None		,							**_UpperCamelCase				:   List[Any] )   ->			str:



												'''simple docstring'''

												if scheduler is None:
																	SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter
												scheduler.set_timesteps(_UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																	SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase ).prev_sample

												return sample




							def 		__snake_case(  self				:   int )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    dict(self.forward_default_kwargs )

												SCREAMING_SNAKE_CASE           =    kwargs.pop("num_inference_steps"		,							_UpperCamelCase )

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config()
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	SCREAMING_SNAKE_CASE           =    self.dummy_sample
																	SCREAMING_SNAKE_CASE           =    0.1 * sample

																	if num_inference_steps is not None and hasattr(_UpperCamelCase		,							"set_timesteps" ):
																						scheduler.set_timesteps(_UpperCamelCase )
																	elif num_inference_steps is not None and not hasattr(_UpperCamelCase		,							"set_timesteps" ):
																						SCREAMING_SNAKE_CASE           =    num_inference_steps

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

																	SCREAMING_SNAKE_CASE           =    scheduler.timesteps[5]
																	SCREAMING_SNAKE_CASE           =    scheduler.timesteps[6]

																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							**_UpperCamelCase ).prev_sample

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




							def 		__snake_case(  self				:   str )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    UniPCMultistepScheduler(**self.get_scheduler_config() )
												SCREAMING_SNAKE_CASE           =    self.full_loop(scheduler=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3

												SCREAMING_SNAKE_CASE           =    DPMSolverSinglestepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    DEISMultistepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    DPMSolverMultistepScheduler.from_config(scheduler.config )
												SCREAMING_SNAKE_CASE           =    UniPCMultistepScheduler.from_config(scheduler.config )

												SCREAMING_SNAKE_CASE           =    self.full_loop(scheduler=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase )




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



												'''simple docstring'''

												self.check_over_configs(thresholding=_UpperCamelCase )
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							sample_max_value=_UpperCamelCase		,							solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							)




							def 		__snake_case(  self				:   Tuple )   ->			int:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Dict )   ->			int:



												'''simple docstring'''

												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							)
																											SCREAMING_SNAKE_CASE           =    self.full_loop(
																											    solver_order=_UpperCamelCase		,							solver_type=_UpperCamelCase		,							prediction_type=_UpperCamelCase		,							)
																											assert not torch.isnan(_UpperCamelCase ).any(), "Samples have nan numbers"




							def 		__snake_case(  self				:   List[str] )   ->			Tuple:



												'''simple docstring'''

												self.check_over_configs(lower_order_final=_UpperCamelCase )
												self.check_over_configs(lower_order_final=_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=_UpperCamelCase		,							time_step=0 )




							def 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop()
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3




							def 		__snake_case(  self				:   List[str] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.full_loop(prediction_type="v_prediction" )
												SCREAMING_SNAKE_CASE           =    torch.mean(torch.abs(_UpperCamelCase ) )

												assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3




							def 		__snake_case(  self				:   int )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(thresholding=_UpperCamelCase		,							dynamic_thresholding_ratio=0 )
												SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    10
												SCREAMING_SNAKE_CASE           =    self.dummy_model()
												SCREAMING_SNAKE_CASE           =    self.dummy_sample_deter.half()
												scheduler.set_timesteps(_UpperCamelCase )

												for i, t in enumerate(scheduler.timesteps ):
																	SCREAMING_SNAKE_CASE           =    model(_UpperCamelCase		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler.step(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase ).prev_sample

												assert sample.dtype == torch.floataa





							def 		__snake_case(  self				:   List[str]		,							**_UpperCamelCase				:   Dict )   ->			Union[str, Any]:



												'''simple docstring'''

												for scheduler_class in self.scheduler_classes:
																	SCREAMING_SNAKE_CASE           =    self.get_scheduler_config(**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    scheduler_class(**_UpperCamelCase )

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps )
																	assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps




