






"""simple docstring"""



import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)



def 						lowercase    (				__snake_case				: List[str]	):





		if model.config.model_type == "gpt2":
				return model.transformer.h[0].mlp.c_fc
		return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
						import torch
						import torch.nn as nn





						class 					_UpperCAmelCase       ( nn.Module							):




											def __init__(				self     :						Union[str, Any]    ,						A     :						int    ,						A     :						int   )  ->  Any:
													super().__init__()
													lowercase_ : Optional[Any]							       =   module
													lowercase_ : Optional[int]							       =   nn.Sequential(
													    nn.Linear(module.in_features    ,						lowerCamelCase_    ,						bias=lowerCamelCase_   )    ,						nn.Linear(lowerCamelCase_    ,						module.out_features    ,						bias=lowerCamelCase_   )    ,						)
													lowercase_ : Union[str, Any]							       =   (2.0 / (5 * min(module.in_features    ,						module.out_features   ))) ** 0.5
													nn.init.normal_(self.adapter[0].weight    ,						std=lowerCamelCase_   )
													nn.init.zeros_(self.adapter[1].weight   )
													self.adapter.to(module.weight.device   )






											def   A  (				self     :						Union[str, Any]    ,						A     :						int    ,						*A     :						Dict    ,						**A     :						List[Any]   )  ->  int:
													return self.module(lowerCamelCase_    ,						*lowerCamelCase_    ,						**lowerCamelCase_   ) + self.adapter(lowerCamelCase_   )





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					_UpperCAmelCase       ( unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				str							    =   "bigscience/bloom-1b7"

					# Constant values
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   2.109_659_552_692_574

					SCREAMING_SNAKE_CASE_		:				List[str]							    =   "Hello my name is"
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   set()
					EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"							)
					EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"							)
					EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"							)
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   10




					def   A  (				self     :						str   )  ->  Dict:
							lowercase_ : Optional[int]							       =   AutoTokenizer.from_pretrained(self.model_name   )





class 					_UpperCAmelCase       ( _A							):




					def   A  (				self     :						List[Any]   )  ->  List[str]:
							super().setUp()

							# Models and tokenizer
							lowercase_ : Any							       =   AutoModelForCausalLM.from_pretrained(
							    self.model_name    ,						torch_dtype=torch.floataa    ,						device_map='''auto'''   )
							lowercase_ : Optional[Any]							       =   AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )




					def   A  (				self     :						List[str]   )  ->  Optional[int]:
							del self.model_fpaa
							del self.model_abit

							gc.collect()
							torch.cuda.empty_cache()




					def   A  (				self     :						Union[str, Any]   )  ->  str:
							lowercase_ : Dict							       =   self.model_abit.config

							self.assertTrue(hasattr(lowerCamelCase_    ,						'''quantization_config'''   )   )

							lowercase_ : Dict							       =   config.to_dict()
							lowercase_ : Union[str, Any]							       =   config.to_diff_dict()

							lowercase_ : Optional[Any]							       =   config.to_json_string()




					def   A  (				self     :						List[str]   )  ->  int:
							from bitsandbytes.nn import Paramsabit

							lowercase_ : List[str]							       =   self.model_fpaa.get_memory_footprint()
							lowercase_ : Dict							       =   self.model_abit.get_memory_footprint()

							self.assertAlmostEqual(mem_fpaa / mem_abit    ,						self.EXPECTED_RELATIVE_DIFFERENCE   )
							lowercase_ : Any							       =   get_some_linear_layer(self.model_abit   )
							self.assertTrue(linear.weight.__class__ == Paramsabit   )




					def   A  (				self     :						Dict   )  ->  Optional[int]:
							from transformers import TaPreTrainedModel

							self.model_fpaa.get_memory_footprint()
							self.model_abit.get_memory_footprint()

							for name, module in self.model_abit.named_modules():
									if isinstance(lowerCamelCase_    ,						torch.nn.Linear   ):
											if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
													# 4-bit parameters are packed in uint8 variables
													self.assertTrue(module.weight.dtype == torch.uinta   )




					def   A  (				self     :						str   )  ->  Optional[int]:
							lowercase_ : List[Any]							       =   self.tokenizer(self.input_text    ,						return_tensors='''pt'''   )
							lowercase_ : List[Any]							       =   self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0   )    ,						max_new_tokens=10   )

							self.assertIn(self.tokenizer.decode(output_sequences[0]    ,						skip_special_tokens=lowerCamelCase_   )    ,						self.EXPECTED_OUTPUTS   )




					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							lowercase_ : List[Any]							       =   BitsAndBytesConfig()
							lowercase_ : Dict							       =   True

							lowercase_ : str							       =   AutoModelForCausalLM.from_pretrained(
							    self.model_name    ,						quantization_config=lowerCamelCase_    ,						device_map='''auto'''   )

							lowercase_ : Any							       =   self.tokenizer(self.input_text    ,						return_tensors='''pt'''   )
							lowercase_ : List[str]							       =   model_abit_from_config.generate(
							    input_ids=encoded_input['''input_ids'''].to(0   )    ,						max_new_tokens=10   )

							self.assertIn(self.tokenizer.decode(output_sequences[0]    ,						skip_special_tokens=lowerCamelCase_   )    ,						self.EXPECTED_OUTPUTS   )




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

							with self.assertRaises(lowerCamelCase_   ), tempfile.TemporaryDirectory() as tmpdirname:
									self.model_abit.save_pretrained(lowerCamelCase_   )




					def   A  (				self     :						Any   )  ->  int:
							lowercase_ : Optional[Any]							       =   BitsAndBytesConfig()

							with self.assertRaises(lowerCamelCase_   ):
									lowercase_ : List[Any]							       =   AutoModelForCausalLM.from_pretrained(
									    self.model_name    ,						quantization_config=lowerCamelCase_    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''    ,						bnb_abit_quant_type='''nf4'''    ,						)




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

							with self.assertRaises(lowerCamelCase_   ):
									# Tries with `str`
									self.model_abit.to('''cpu'''   )

							with self.assertRaises(lowerCamelCase_   ):
									# Tries with a `dtype``
									self.model_abit.to(torch.floataa   )

							with self.assertRaises(lowerCamelCase_   ):
									# Tries with a `device`
									self.model_abit.to(torch.device('''cuda:0'''   )   )

							with self.assertRaises(lowerCamelCase_   ):
									# Tries with a `device`
									self.model_abit.float()

							with self.assertRaises(lowerCamelCase_   ):
									# Tries with a `device`
									self.model_abit.half()

							# Test if we did not break anything
							lowercase_ : List[Any]							       =   self.tokenizer(self.input_text    ,						return_tensors='''pt'''   )

							lowercase_ : Optional[int]							       =   self.model_fpaa.to(torch.floataa   )
							lowercase_ : Tuple							       =   self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0   )    ,						max_new_tokens=10   )

							# Check this does not throw an error
							lowercase_ : Optional[Any]							       =   self.model_fpaa.to('''cpu'''   )

							# Check this does not throw an error
							lowercase_ : Tuple							       =   self.model_fpaa.half()

							# Check this does not throw an error
							lowercase_ : List[str]							       =   self.model_fpaa.float()






					def   A  (				self     :						Tuple   )  ->  Dict:
							lowercase_ : Tuple							       =   AutoModelForSeqaSeqLM.from_pretrained('''t5-small'''    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )
							self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa   )





@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@classmethod
					def   A  (				cls     :						Optional[Any]   )  ->  Dict:
							lowercase_ : Tuple							       =   '''t5-small'''
							lowercase_ : Tuple							       =   '''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
							lowercase_ : str							       =   AutoTokenizer.from_pretrained(cls.model_name   )
							lowercase_ : Optional[Any]							       =   '''Translate in German: Hello, my dog is cute'''




					def   A  (				self     :						Dict   )  ->  List[str]:
							gc.collect()
							torch.cuda.empty_cache()




					def   A  (				self     :						Dict   )  ->  int:
							from transformers import TaForConditionalGeneration

							lowercase_ : Any							       =   TaForConditionalGeneration._keep_in_fpaa_modules
							lowercase_ : Any							       =   None

							# test with `t5-small`
							lowercase_ : int							       =   TaForConditionalGeneration.from_pretrained(self.model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )
							lowercase_ : List[str]							       =   self.tokenizer(self.input_text    ,						return_tensors='''pt'''   ).to(0   )
							lowercase_ : Dict							       =   model.generate(**lowerCamelCase_   )

							# test with `flan-t5-small`
							lowercase_ : Optional[Any]							       =   TaForConditionalGeneration.from_pretrained(
							    self.dense_act_model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )
							lowercase_ : List[Any]							       =   self.tokenizer(self.input_text    ,						return_tensors='''pt'''   ).to(0   )
							lowercase_ : int							       =   model.generate(**lowerCamelCase_   )
							lowercase_ : List[str]							       =   modules






					def   A  (				self     :						Any   )  ->  str:
							import bitsandbytes as bnb

							from transformers import TaForConditionalGeneration

							# test with `t5-small`
							lowercase_ : Optional[Any]							       =   TaForConditionalGeneration.from_pretrained(self.model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )

							# there was a bug with decoders - this test checks that it is fixed
							self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q    ,						bnb.nn.Linearabit   )   )

							lowercase_ : Union[str, Any]							       =   self.tokenizer(self.input_text    ,						return_tensors='''pt'''   ).to(0   )
							lowercase_ : Any							       =   model.generate(**lowerCamelCase_   )

							# test with `flan-t5-small`
							lowercase_ : List[Any]							       =   TaForConditionalGeneration.from_pretrained(
							    self.dense_act_model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )
							lowercase_ : str							       =   self.tokenizer(self.input_text    ,						return_tensors='''pt'''   ).to(0   )
							lowercase_ : Optional[Any]							       =   model.generate(**lowerCamelCase_   )





class 					_UpperCAmelCase       ( _A							):




					def   A  (				self     :						List[Any]   )  ->  Union[str, Any]:
							super().setUp()
							# model_name
							lowercase_ : Optional[Any]							       =   '''bigscience/bloom-560m'''
							lowercase_ : Optional[Any]							       =   '''t5-small'''

							# Different types of model

							lowercase_ : Dict							       =   AutoModel.from_pretrained(self.model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )
							# Sequence classification model
							lowercase_ : List[Any]							       =   AutoModelForSequenceClassification.from_pretrained(
							    self.model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )
							# CausalLM model
							lowercase_ : Optional[Any]							       =   AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )
							# Seq2seq model
							lowercase_ : str							       =   AutoModelForSeqaSeqLM.from_pretrained(
							    self.seq_to_seq_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''auto'''   )




					def   A  (				self     :						Any   )  ->  Any:
							del self.base_model
							del self.sequence_model
							del self.model_abit
							del self.seq_to_seq_model

							gc.collect()
							torch.cuda.empty_cache()






					def   A  (				self     :						Tuple   )  ->  Optional[int]:
							from bitsandbytes.nn import Paramsabit

							self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit   )

							# Other heads should be nn.Parameter
							self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter   )
							self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter   )
							self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter   )





class 					_UpperCAmelCase       ( _A							):




					def   A  (				self     :						Dict   )  ->  Any:
							super().setUp()




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							del self.pipe

							gc.collect()
							torch.cuda.empty_cache()






					def   A  (				self     :						List[str]   )  ->  Optional[Any]:
							lowercase_ : Optional[Any]							       =   pipeline(
							    '''text-generation'''    ,						model=self.model_name    ,						model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa}    ,						max_new_tokens=self.MAX_NEW_TOKENS    ,						)

							# Real second forward pass
							lowercase_ : Any							       =   self.pipe(self.input_text   )
							self.assertIn(pipeline_output[0]['''generated_text''']    ,						self.EXPECTED_OUTPUTS   )





@require_torch_multi_gpu
class 					_UpperCAmelCase       ( _A							):




					def   A  (				self     :						Optional[Any]   )  ->  Dict:
							super().setUp()






					def   A  (				self     :						int   )  ->  List[str]:
							lowercase_ : Optional[Any]							       =   AutoModelForCausalLM.from_pretrained(
							    self.model_name    ,						load_in_abit=lowerCamelCase_    ,						device_map='''balanced'''   )

							# Check correct device map
							self.assertEqual(set(model_parallel.hf_device_map.values()   )    ,						{0, 1}   )

							# Check that inference pass works on the model
							lowercase_ : Tuple							       =   self.tokenizer(self.input_text    ,						return_tensors='''pt'''   )

							# Second real batch
							lowercase_ : Dict							       =   model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0   )    ,						max_new_tokens=10   )
							self.assertIn(self.tokenizer.decode(output_parallel[0]    ,						skip_special_tokens=lowerCamelCase_   )    ,						self.EXPECTED_OUTPUTS   )





class 					_UpperCAmelCase       ( _A							):




					def   A  (				self     :						List[str]   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   '''facebook/opt-350m'''
							super().setUp()






					def   A  (				self     :						Dict   )  ->  Optional[int]:

							if version.parse(importlib.metadata.version('''bitsandbytes'''   )   ) < version.parse('''0.37.0'''   ):
									return

							# Step 1: freeze all parameters
							lowercase_ : Any							       =   AutoModelForCausalLM.from_pretrained(self.model_name    ,						load_in_abit=lowerCamelCase_   )

							self.assertEqual(set(model.hf_device_map.values()   )    ,						{torch.cuda.current_device()}   )

							for param in model.parameters():
									lowercase_ : Dict							       =   False  # freeze the model - train adapters later
									if param.ndim == 1:
											# cast the small parameters (e.g. layernorm) to fp32 for stability
											lowercase_ : int							       =   param.data.to(torch.floataa   )

        # Step 2: add adapters
							for _, module in model.named_modules():
									if "OPTAttention" in repr(type(lowerCamelCase_   )   ):
											lowercase_ : Dict							       =   LoRALayer(module.q_proj    ,						rank=16   )
											lowercase_ : Dict							       =   LoRALayer(module.k_proj    ,						rank=16   )
											lowercase_ : Optional[Any]							       =   LoRALayer(module.v_proj    ,						rank=16   )

        # Step 3: dummy batch
							lowercase_ : Union[str, Any]							       =   self.tokenizer('''Test batch '''    ,						return_tensors='''pt'''   ).to(0   )

							# Step 4: Check if the gradient is not None
							with torch.cuda.amp.autocast():
									lowercase_ : Optional[Any]							       =   model.forward(**lowerCamelCase_   )
									out.logits.norm().backward()

							for module in model.modules():
									if isinstance(lowerCamelCase_    ,						lowerCamelCase_   ):
											self.assertTrue(module.adapter[1].weight.grad is not None   )
											self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0   )
									elif isinstance(lowerCamelCase_    ,						nn.Embedding   ):
											self.assertTrue(module.weight.grad is None   )





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   "gpt2-xl"
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   3.3_191_854_854_152_187








import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							(DDPMScheduler,)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "variance_type": "fixed_small",
						    "clip_sample": True,
						}

						config.update(**lowerCamelCase_		)
						return config




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for timesteps in [1, 5, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,       [0.002, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=lowerCamelCase_		,       beta_end=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.check_over_configs(thresholding=lowerCamelCase_		)
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCamelCase_		,       prediction_type=lowerCamelCase_		,       sample_max_value=lowerCamelCase_		,       )




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for t in [0, 500, 999]:
											self.check_over_forward(time_step=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

						for t in reversed(range(lowerCamelCase_		)		):
											# 1. predict noise residual
											a					     =   model(lowerCamelCase_		,       lowerCamelCase_		)

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 258.9606		) < 1E-2
						assert abs(result_mean.item() - 0.3372		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(prediction_type="v_prediction"		)
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

						a					     =   self.dummy_model()
						a					     =   self.dummy_sample_deter
						a					     =   torch.manual_seed(0		)

						for t in reversed(range(lowerCamelCase_		)		):
											# 1. predict noise residual
											a					     =   model(lowerCamelCase_		,       lowerCamelCase_		)

											# 2. predict previous mean of sample x_t-1
											a					     =   scheduler.step(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       generator=lowerCamelCase_		).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											a					     =   pred_prev_sample

						a					     =   torch.sum(torch.abs(lowerCamelCase_		)		)
						a					     =   torch.mean(torch.abs(lowerCamelCase_		)		)

						assert abs(result_sum.item() - 202.0296		) < 1E-2
						assert abs(result_mean.item() - 0.2631		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=lowerCamelCase_		)

						a					     =   scheduler.timesteps

						for i, timestep in enumerate(lowerCamelCase_		):
											if i == len(lowerCamelCase_		) - 1:
																a					     =   -1
											else:
																a					     =   timesteps[i + 1]

											a					     =   scheduler.previous_timestep(lowerCamelCase_		)
											a					     =   prev_t.item()

											self.assertEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 51, 0]

						with self.assertRaises(lowerCamelCase_		,       msg="`custom_timesteps` must be in descending order."		):
											scheduler.set_timesteps(timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [100, 87, 50, 1, 0]
						a					     =   len(lowerCamelCase_		)

						with self.assertRaises(lowerCamelCase_		,       msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
											scheduler.set_timesteps(num_inference_steps=lowerCamelCase_		,       timesteps=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [scheduler.config.num_train_timesteps]

						with self.assertRaises(
						    lowerCamelCase_		,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"		,       ):
											scheduler.set_timesteps(timesteps=lowerCamelCase_		)
