
'''simple docstring'''



import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
						from transformers import LlamaTokenizerFast
except ImportError as e:
						warnings.warn(e)
						warnings.warn(
						    """The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
						)
						_lowercase          =						None
_lowercase          =						{
    """7B""": 11008,
    """13B""": 13824,
    """30B""": 17920,
    """65B""": 22016,
    """70B""": 28672,
}
_lowercase          =						{
    """7B""": 1,
    """7Bf""": 1,
    """13B""": 2,
    """13Bf""": 2,
    """30B""": 4,
    """65B""": 8,
    """70B""": 8,
    """70Bf""": 8,
}


def A (__lowerCamelCase :int					,      __lowerCamelCase :Optional[Any]=1					,      __lowerCamelCase :List[Any]=256						):
						return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3						)						) + multiple_of - 1) // multiple_of)


def A (__lowerCamelCase :Any						):
						with open(__lowerCamelCase					,      """r"""						) as f:
												return json.load(__lowerCamelCase						)


def A (__lowerCamelCase :List[Any]					,      __lowerCamelCase :int						):
						with open(__lowerCamelCase					,      """w"""						) as f:
												json.dump(__lowerCamelCase					,      __lowerCamelCase						)


def A (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Tuple=True						):
						os.makedirs(__lowerCamelCase					,      exist_ok=__lowerCamelCase						)
						_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      """tmp"""						)
						os.makedirs(__lowerCamelCase					,      exist_ok=__lowerCamelCase						)

						_lowerCAmelCase    		=					read_json(os.path.join(__lowerCamelCase					,      """params.json"""						)						)
						_lowerCAmelCase    		=					NUM_SHARDS[model_size]
						_lowerCAmelCase    		=					params["""n_layers"""]
						_lowerCAmelCase    		=					params["""n_heads"""]
						_lowerCAmelCase    		=					n_heads // num_shards
						_lowerCAmelCase    		=					params["""dim"""]
						_lowerCAmelCase    		=					dim // n_heads
						_lowerCAmelCase    		=					10_000.0
						_lowerCAmelCase    		=					1.0 / (base ** (torch.arange(0					,      __lowerCamelCase					,      2						).float() / dims_per_head))

						if "n_kv_heads" in params:
												_lowerCAmelCase    		=					params["""n_kv_heads"""]  # for GQA / MQA
												_lowerCAmelCase    		=					n_heads_per_shard // num_key_value_heads
												_lowerCAmelCase    		=					dim // num_key_value_heads
						else:  # compatibility with other checkpoints
												_lowerCAmelCase    		=					n_heads
												_lowerCAmelCase    		=					n_heads_per_shard
												_lowerCAmelCase    		=					dim

						# permute for sliced rotary
						def permute(__lowerCamelCase :Optional[int]					,      __lowerCamelCase :str=n_heads					,      __lowerCamelCase :str=dim					,      __lowerCamelCase :List[Any]=dim						):
												return w.view(__lowerCamelCase					,      dima // n_heads // 2					,      2					,      __lowerCamelCase						).transpose(1					,      2						).reshape(__lowerCamelCase					,      __lowerCamelCase						)

						print(f'Fetching all parameters from the checkpoint at {input_base_path}.'						)
						# Load weights
						if model_size == "7B":
												# Not sharded
												# (The sharded implementation would also work, but this is simpler.)
												_lowerCAmelCase    		=					torch.load(os.path.join(__lowerCamelCase					,      """consolidated.00.pth"""						)					,      map_location="""cpu"""						)
						else:
												# Sharded
												_lowerCAmelCase    		=					[
												    torch.load(os.path.join(__lowerCamelCase					,      f'consolidated.{i:02d}.pth'						)					,      map_location="""cpu"""						)
												    for i in range(__lowerCamelCase						)
												]
						_lowerCAmelCase    		=					0
						_lowerCAmelCase    		=					{"""weight_map""": {}}
						for layer_i in range(__lowerCamelCase						):
												_lowerCAmelCase    		=					f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
												if model_size == "7B":
																		# Unsharded
																		_lowerCAmelCase    		=					{
																		    f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
																		        loaded[f'layers.{layer_i}.attention.wq.weight']						),
																		    f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
																		        loaded[f'layers.{layer_i}.attention.wk.weight']						),
																		    f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
																		    f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
																		    f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
																		    f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
																		    f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
																		    f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
																		    f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
																		}
												else:
																		# Sharded
																		# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																		# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																		# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																		_lowerCAmelCase    		=					{
																		    f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
																		        f'layers.{layer_i}.attention_norm.weight'
																		    ].clone(),
																		    f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
																		        f'layers.{layer_i}.ffn_norm.weight'
																		    ].clone(),
																		}
																		_lowerCAmelCase    		=					permute(
																		    torch.cat(
																		        [
																		            loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		            for i in range(__lowerCamelCase						)
																		        ]					,      dim=0					,      ).reshape(__lowerCamelCase					,      __lowerCamelCase						)						)
																		_lowerCAmelCase    		=					permute(
																		    torch.cat(
																		        [
																		            loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
																		                __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		            for i in range(__lowerCamelCase						)
																		        ]					,      dim=0					,      ).reshape(__lowerCamelCase					,      __lowerCamelCase						)					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      )
																		_lowerCAmelCase    		=					torch.cat(
																		    [
																		        loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
																		            __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		        for i in range(__lowerCamelCase						)
																		    ]					,      dim=0					,      ).reshape(__lowerCamelCase					,      __lowerCamelCase						)

																		_lowerCAmelCase    		=					torch.cat(
																		    [loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(__lowerCamelCase						)]					,      dim=1						)
																		_lowerCAmelCase    		=					torch.cat(
																		    [loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__lowerCamelCase						)]					,      dim=0						)
																		_lowerCAmelCase    		=					torch.cat(
																		    [loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__lowerCamelCase						)]					,      dim=1						)
																		_lowerCAmelCase    		=					torch.cat(
																		    [loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__lowerCamelCase						)]					,      dim=0						)

												_lowerCAmelCase    		=					inv_freq
												for k, v in state_dict.items():
																		_lowerCAmelCase    		=					filename
																		param_count += v.numel()
												torch.save(__lowerCamelCase					,      os.path.join(__lowerCamelCase					,      __lowerCamelCase						)						)

						_lowerCAmelCase    		=					f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
						if model_size == "7B":
												# Unsharded
												_lowerCAmelCase    		=					{
												    """model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
												    """model.norm.weight""": loaded["""norm.weight"""],
												    """lm_head.weight""": loaded["""output.weight"""],
												}
						else:
												_lowerCAmelCase    		=					{
												    """model.norm.weight""": loaded[0]["""norm.weight"""],
												    """model.embed_tokens.weight""": torch.cat(
												        [loaded[i]["""tok_embeddings.weight"""] for i in range(__lowerCamelCase						)]					,      dim=1						),
												    """lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(__lowerCamelCase						)]					,      dim=0						),
												}

						for k, v in state_dict.items():
												_lowerCAmelCase    		=					filename
												param_count += v.numel()
						torch.save(__lowerCamelCase					,      os.path.join(__lowerCamelCase					,      __lowerCamelCase						)						)

						# Write configs
						_lowerCAmelCase    		=					{"""total_size""": param_count * 2}
						write_json(__lowerCamelCase					,      os.path.join(__lowerCamelCase					,      """pytorch_model.bin.index.json"""						)						)
						_lowerCAmelCase    		=					params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
						_lowerCAmelCase    		=					params["""multiple_of"""] if """multiple_of""" in params else 256
						_lowerCAmelCase    		=					LlamaConfig(
						    hidden_size=__lowerCamelCase					,      intermediate_size=compute_intermediate_size(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)					,      num_attention_heads=params["""n_heads"""]					,      num_hidden_layers=params["""n_layers"""]					,      rms_norm_eps=params["""norm_eps"""]					,      num_key_value_heads=__lowerCamelCase					,      )
						config.save_pretrained(__lowerCamelCase						)

						# Make space so we can load the model properly now.
						del state_dict
						del loaded
						gc.collect()

						print("""Loading the checkpoint in a Llama model."""						)
						_lowerCAmelCase    		=					LlamaForCausalLM.from_pretrained(__lowerCamelCase					,      torch_dtype=torch.floataa					,      low_cpu_mem_usage=__lowerCamelCase						)
						# Avoid saving this as part of the config.
						del model.config._name_or_path

						print("""Saving in the Transformers format."""						)
						model.save_pretrained(__lowerCamelCase					,      safe_serialization=__lowerCamelCase						)
						shutil.rmtree(__lowerCamelCase						)


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Union[str, Any]						):
						# Initialize the tokenizer based on the `spm` model
						_lowerCAmelCase    		=					LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
						print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.'						)
						_lowerCAmelCase    		=					tokenizer_class(__lowerCamelCase						)
						tokenizer.save_pretrained(__lowerCamelCase						)


def A ():
						_lowerCAmelCase    		=					argparse.ArgumentParser()
						parser.add_argument(
						    """--input_dir"""					,      help="""Location of LLaMA weights, which contains tokenizer.model and model folders"""					,      )
						parser.add_argument(
						    """--model_size"""					,      choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""]					,      )
						parser.add_argument(
						    """--output_dir"""					,      help="""Location to write HF model and tokenizer"""					,      )
						parser.add_argument("""--safe_serialization"""					,      type=__lowerCamelCase					,      help="""Whether or not to save using `safetensors`."""						)
						_lowerCAmelCase    		=					parser.parse_args()
						if args.model_size != "tokenizer_only":
												write_model(
												    model_path=args.output_dir					,      input_base_path=os.path.join(args.input_dir					,      args.model_size						)					,      model_size=args.model_size					,      safe_serialization=args.safe_serialization					,      )
						_lowerCAmelCase    		=					os.path.join(args.input_dir					,      """tokenizer.model"""						)
						write_tokenizer(args.output_dir					,      __lowerCamelCase						)


if __name__ == "__main__":
						main()





"""simple docstring"""

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= (DEISMultistepScheduler,)
	_SCREAMING_SNAKE_CASE	:		str  						= (('''num_inference_steps''', 25),)





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       **_SCREAMING_SNAKE_CASE				:       int    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     {
					    'num_train_timesteps': 1_000,
					    '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 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int=0     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     dict(self.forward_default_kwargs    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_sample
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     0.1 * sample
					SCREAMING_SNAKE_CASE						:							Any							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							List[str]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE						:							Dict							=     dummy_past_residuals[: scheduler.config.solver_order]

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

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     sample, sample
									for t in range(_SCREAMING_SNAKE_CASE     ,       time_step + scheduler.config.solver_order + 1    ):
													SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
													SCREAMING_SNAKE_CASE						:							Tuple							=     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 			_lowerCAmelCase     (      self				:       List[str]    )						->	Any:





					"""simple docstring"""


					pass





	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict=0     ,       **_SCREAMING_SNAKE_CASE				:       Dict    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     dict(self.forward_default_kwargs    )
					SCREAMING_SNAKE_CASE						:							Dict							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.dummy_sample
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     0.1 * sample
					SCREAMING_SNAKE_CASE						:							int							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

									with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Any							=     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)
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE						:							Optional[int]							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									SCREAMING_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 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[str]    )						->	Any:





					"""simple docstring"""


					if scheduler is None:
									SCREAMING_SNAKE_CASE						:							int							=     self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     10
					SCREAMING_SNAKE_CASE						:							Any							=     self.dummy_model()
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_sample_deter
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

					return sample





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     dict(self.forward_default_kwargs    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							int							=     self.get_scheduler_config()
									SCREAMING_SNAKE_CASE						:							Dict							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.dummy_sample
									SCREAMING_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'    ):
													SCREAMING_SNAKE_CASE						:							List[str]							=     num_inference_steps

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

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

									SCREAMING_SNAKE_CASE						:							str							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									SCREAMING_SNAKE_CASE						:							List[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 			_lowerCAmelCase     (      self				:       Dict    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     DEISMultistepScheduler(**self.get_scheduler_config()    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     DPMSolverSinglestepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     DPMSolverMultistepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							Any							=     UniPCMultistepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     DEISMultistepScheduler.from_config(scheduler.config    )

					SCREAMING_SNAKE_CASE						:							str							=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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





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





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       str    )						->	List[str]:





					"""simple docstring"""


					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 			_lowerCAmelCase     (      self				:       str    )						->	Optional[int]:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Tuple:





					"""simple docstring"""


					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     ,       )
																					SCREAMING_SNAKE_CASE						:							Dict							=     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 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )
					self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	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=_SCREAMING_SNAKE_CASE     ,       time_step=0    )





	def 			_lowerCAmelCase     (      self				:       int    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.full_loop()
					SCREAMING_SNAKE_CASE						:							Tuple							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.full_loop(prediction_type='v_prediction'    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

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







	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE						:							Tuple							=     self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0    )
					SCREAMING_SNAKE_CASE						:							Any							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     10
					SCREAMING_SNAKE_CASE						:							Dict							=     self.dummy_model()
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.dummy_sample_deter.half()
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

					assert sample.dtype == torch.floataa





