






"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				(DPMSolverSinglestepScheduler,)
	__magic_name__	      =				(('''num_inference_steps''', 25),)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	**lowerCAmelCase_					: Any						)						->			Optional[Any]:
				UpperCAmelCase_     : str			     =					{
				    "num_train_timesteps": 1_000,
				    "beta_start": 0.0_0_0_1,
				    "beta_end": 0.0_2,
				    "beta_schedule": "linear",
				    "solver_order": 2,
				    "prediction_type": "epsilon",
				    "thresholding": False,
				    "sample_max_value": 1.0,
				    "algorithm_type": "dpmsolver++",
				    "solver_type": "midpoint",
				    "lambda_min_clipped": -float("inf"						),
				    "variance_type": None,
				}

				config.update(**lowerCAmelCase_						)
				return config





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

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_     : Any			     =					self.get_scheduler_config(**lowerCAmelCase_						)
							UpperCAmelCase_     : Union[str, Any]			     =					scheduler_class(**lowerCAmelCase_						)
							scheduler.set_timesteps(lowerCAmelCase_						)
							# copy over dummy past residuals
							UpperCAmelCase_     : str			     =					dummy_past_residuals[: scheduler.config.solver_order]

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

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

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





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: Tuple=0	,	**lowerCAmelCase_					: Dict						)						->			int:
				UpperCAmelCase_     : Optional[Any]			     =					dict(self.forward_default_kwargs						)
				UpperCAmelCase_     : Tuple			     =					kwargs.pop("num_inference_steps"	,	lowerCAmelCase_						)
				UpperCAmelCase_     : List[str]			     =					self.dummy_sample
				UpperCAmelCase_     : Any			     =					0.1 * sample
				UpperCAmelCase_     : List[str]			     =					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_     : Optional[int]			     =					self.get_scheduler_config()
							UpperCAmelCase_     : Tuple			     =					scheduler_class(**lowerCAmelCase_						)
							scheduler.set_timesteps(lowerCAmelCase_						)

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

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

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

							UpperCAmelCase_     : str			     =					scheduler.step(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	**lowerCAmelCase_						).prev_sample
							UpperCAmelCase_     : Tuple			     =					new_scheduler.step(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_	,	**lowerCAmelCase_						).prev_sample

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





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: int=None	,	**lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				if scheduler is None:
							UpperCAmelCase_     : List[Any]			     =					self.scheduler_classes[0]
							UpperCAmelCase_     : Dict			     =					self.get_scheduler_config(**lowerCAmelCase_						)
							UpperCAmelCase_     : Optional[int]			     =					scheduler_class(**lowerCAmelCase_						)

				UpperCAmelCase_     : List[Any]			     =					self.scheduler_classes[0]
				UpperCAmelCase_     : List[Any]			     =					self.get_scheduler_config(**lowerCAmelCase_						)
				UpperCAmelCase_     : Any			     =					scheduler_class(**lowerCAmelCase_						)

				UpperCAmelCase_     : str			     =					10
				UpperCAmelCase_     : str			     =					self.dummy_model()
				UpperCAmelCase_     : Tuple			     =					self.dummy_sample_deter
				scheduler.set_timesteps(lowerCAmelCase_						)

				for i, t in enumerate(scheduler.timesteps						):
							UpperCAmelCase_     : List[str]			     =					model(lowerCAmelCase_	,	lowerCAmelCase_						)
							UpperCAmelCase_     : int			     =					scheduler.step(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						).prev_sample

				return sample





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Union[str, Any]:
				UpperCAmelCase_     : Any			     =					DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
				UpperCAmelCase_     : Any			     =					50
				UpperCAmelCase_     : List[str]			     =					self.dummy_model()
				UpperCAmelCase_     : Union[str, Any]			     =					self.dummy_sample_deter
				scheduler.set_timesteps(lowerCAmelCase_						)

				# make sure that the first t is uneven
				for i, t in enumerate(scheduler.timesteps[3:]						):
							UpperCAmelCase_     : Dict			     =					model(lowerCAmelCase_	,	lowerCAmelCase_						)
							UpperCAmelCase_     : List[str]			     =					scheduler.step(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						).prev_sample

				UpperCAmelCase_     : Any			     =					torch.mean(torch.abs(lowerCAmelCase_						)						)

				assert abs(result_mean.item() - 0.2_5_7_4						) < 1e-3





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





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Optional[int]:
				# make sure that iterating over schedulers with same config names gives same results
				# for defaults
				UpperCAmelCase_     : Union[str, Any]			     =					DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
				UpperCAmelCase_     : List[str]			     =					self.full_loop(scheduler=lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					torch.mean(torch.abs(lowerCAmelCase_						)						)

				assert abs(result_mean.item() - 0.2_7_9_1						) < 1e-3

				UpperCAmelCase_     : Optional[int]			     =					DEISMultistepScheduler.from_config(scheduler.config						)
				UpperCAmelCase_     : List[Any]			     =					DPMSolverMultistepScheduler.from_config(scheduler.config						)
				UpperCAmelCase_     : Any			     =					UniPCMultistepScheduler.from_config(scheduler.config						)
				UpperCAmelCase_     : List[str]			     =					DPMSolverSinglestepScheduler.from_config(scheduler.config						)

				UpperCAmelCase_     : str			     =					self.full_loop(scheduler=lowerCAmelCase_						)
				UpperCAmelCase_     : List[Any]			     =					torch.mean(torch.abs(lowerCAmelCase_						)						)

				assert abs(result_mean.item() - 0.2_7_9_1						) < 1e-3





	def      _SCREAMING_SNAKE_CASE      (      self					: str						)						->			int:
				self.check_over_configs(thresholding=lowerCAmelCase_						)
				for order in [1, 2, 3]:
							for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    thresholding=lowerCAmelCase_	,	prediction_type=lowerCAmelCase_	,	sample_max_value=lowerCAmelCase_	,	algorithm_type="dpmsolver++"	,	solver_order=lowerCAmelCase_	,	solver_type=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			List[str]:
				for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Optional[Any]:
				for algorithm_type in ["dpmsolver", "dpmsolver++"]:
							for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    solver_order=lowerCAmelCase_	,	solver_type=lowerCAmelCase_	,	prediction_type=lowerCAmelCase_	,	algorithm_type=lowerCAmelCase_	,	)
																UpperCAmelCase_     : Optional[Any]			     =					self.full_loop(
																    solver_order=lowerCAmelCase_	,	solver_type=lowerCAmelCase_	,	prediction_type=lowerCAmelCase_	,	algorithm_type=lowerCAmelCase_	,	)
																assert not torch.isnan(lowerCAmelCase_						).any(), "Samples have nan numbers"





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			List[str]:
				self.check_over_configs(lower_order_final=lowerCAmelCase_						)
				self.check_over_configs(lower_order_final=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			int:
				self.check_over_configs(lambda_min_clipped=-float("inf"						)						)
				self.check_over_configs(lambda_min_clipped=-5.1						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Tuple:
				self.check_over_configs(variance_type=lowerCAmelCase_						)
				self.check_over_configs(variance_type="learned_range"						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			List[str]:
				for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
							self.check_over_forward(num_inference_steps=lowerCAmelCase_	,	time_step=0						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Tuple:
				UpperCAmelCase_     : List[Any]			     =					self.full_loop()
				UpperCAmelCase_     : List[str]			     =					torch.mean(torch.abs(lowerCAmelCase_						)						)

				assert abs(result_mean.item() - 0.2_7_9_1						) < 1e-3





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			List[Any]:
				UpperCAmelCase_     : List[str]			     =					self.full_loop(use_karras_sigmas=lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					torch.mean(torch.abs(lowerCAmelCase_						)						)

				assert abs(result_mean.item() - 0.2_2_4_8						) < 1e-3





	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Dict:
				UpperCAmelCase_     : str			     =					self.full_loop(prediction_type="v_prediction"						)
				UpperCAmelCase_     : Any			     =					torch.mean(torch.abs(lowerCAmelCase_						)						)

				assert abs(result_mean.item() - 0.1_4_5_3						) < 1e-3





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Optional[Any]:
				UpperCAmelCase_     : Optional[Any]			     =					self.full_loop(prediction_type="v_prediction"	,	use_karras_sigmas=lowerCAmelCase_						)
				UpperCAmelCase_     : Tuple			     =					torch.mean(torch.abs(lowerCAmelCase_						)						)

				assert abs(result_mean.item() - 0.0_6_4_9						) < 1e-3





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Optional[Any]:
				UpperCAmelCase_     : List[str]			     =					self.scheduler_classes[0]
				UpperCAmelCase_     : Union[str, Any]			     =					self.get_scheduler_config(thresholding=lowerCAmelCase_	,	dynamic_thresholding_ratio=0						)
				UpperCAmelCase_     : Dict			     =					scheduler_class(**lowerCAmelCase_						)

				UpperCAmelCase_     : Tuple			     =					10
				UpperCAmelCase_     : Optional[Any]			     =					self.dummy_model()
				UpperCAmelCase_     : Optional[int]			     =					self.dummy_sample_deter.half()
				scheduler.set_timesteps(lowerCAmelCase_						)

				for i, t in enumerate(scheduler.timesteps						):
							UpperCAmelCase_     : str			     =					model(lowerCAmelCase_	,	lowerCAmelCase_						)
							UpperCAmelCase_     : str			     =					scheduler.step(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						).prev_sample

				assert sample.dtype == torch.floataa











"""simple docstring"""
from typing import Mapping

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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''t5'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





	def __init__(      self					: str	,	lowerCAmelCase_					: List[Any]=32_128	,	lowerCAmelCase_					: Tuple=512	,	lowerCAmelCase_					: Optional[int]=64	,	lowerCAmelCase_					: List[str]=2_048	,	lowerCAmelCase_					: Tuple=6	,	lowerCAmelCase_					: Dict=None	,	lowerCAmelCase_					: str=8	,	lowerCAmelCase_					: Optional[int]=32	,	lowerCAmelCase_					: Dict=128	,	lowerCAmelCase_					: Optional[Any]=0.1	,	lowerCAmelCase_					: str=1e-6	,	lowerCAmelCase_					: Dict=1.0	,	lowerCAmelCase_					: str="relu"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: List[str]=True	,	lowerCAmelCase_					: Any=0	,	lowerCAmelCase_					: Tuple=1	,	**lowerCAmelCase_					: Optional[int]	,	)						->			int:
				UpperCAmelCase_     : int			     =					vocab_size
				UpperCAmelCase_     : Optional[Any]			     =					d_model
				UpperCAmelCase_     : str			     =					d_kv
				UpperCAmelCase_     : Any			     =					d_ff
				UpperCAmelCase_     : int			     =					num_layers
				UpperCAmelCase_     : Union[str, Any]			     =					(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				UpperCAmelCase_     : Optional[Any]			     =					num_heads
				UpperCAmelCase_     : Any			     =					relative_attention_num_buckets
				UpperCAmelCase_     : Optional[Any]			     =					relative_attention_max_distance
				UpperCAmelCase_     : Optional[Any]			     =					dropout_rate
				UpperCAmelCase_     : Tuple			     =					layer_norm_epsilon
				UpperCAmelCase_     : int			     =					initializer_factor
				UpperCAmelCase_     : int			     =					feed_forward_proj
				UpperCAmelCase_     : str			     =					use_cache

				UpperCAmelCase_     : Tuple			     =					self.feed_forward_proj.split("-"						)
				UpperCAmelCase_     : List[Any]			     =					act_info[-1]
				UpperCAmelCase_     : Optional[int]			     =					act_info[0] == "gated"

				if len(lowerCAmelCase_						) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_						) > 2:
							raise ValueError(
							    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
							    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
							    "'gated-gelu' or 'relu'"						)

				# for backwards compatibility
				if feed_forward_proj == "gated-gelu":
							UpperCAmelCase_     : int			     =					"gelu_new"

				super().__init__(
				    pad_token_id=lowerCAmelCase_	,	eos_token_id=lowerCAmelCase_	,	is_encoder_decoder=lowerCAmelCase_	,	**lowerCAmelCase_	,	)







class 			UpperCamelCase_       (__A     ):





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				UpperCAmelCase_     : Any			     =					{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
							UpperCAmelCase_     : List[Any]			     =					"past_encoder_sequence + sequence"
							UpperCAmelCase_     : Union[str, Any]			     =					{0: "batch"}
							UpperCAmelCase_     : Optional[Any]			     =					{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
							UpperCAmelCase_     : List[Any]			     =					{0: "batch", 1: "decoder_sequence"}
							UpperCAmelCase_     : Tuple			     =					{0: "batch", 1: "decoder_sequence"}

				if self.use_past:
							self.fill_with_past_key_values_(lowerCAmelCase_	,	direction="inputs"						)

				return common_inputs





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			int:
				return 13



