
'''simple docstring'''
import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						(DDPMParallelScheduler,)



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	**UpperCamelCase__				:			Optional[int]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						{
				    '''num_train_timesteps''': 1000,
				    '''beta_start''': 0.0_001,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				    '''variance_type''': '''fixed_small''',
				    '''clip_sample''': True,
				}

				config.update(**UpperCamelCase__      )
				return config



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






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			int      )						->			Tuple:






				"""simple docstring"""







				for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]  ,	[0.002, 0.02, 0.2, 2]      ):
						self.check_over_configs(beta_start=UpperCamelCase__  ,	beta_end=UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			str      )						->			Optional[Any]:






				"""simple docstring"""







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



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






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			str:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			int      )						->			List[str]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Dict      )						->			Optional[Any]:






				"""simple docstring"""







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



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






				"""simple docstring"""







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



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






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.scheduler_classes[0]
				snake_case  :       List[str]     			=						self.get_scheduler_config()
				snake_case  :       Optional[int]     			=						scheduler_class(**UpperCamelCase__      )

				assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.00_979      )      ) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1e-5



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






				"""simple docstring"""







				snake_case  :       str     			=						self.scheduler_classes[0]
				snake_case  :       Tuple     			=						self.get_scheduler_config()
				snake_case  :       Union[str, Any]     			=						scheduler_class(**UpperCamelCase__      )

				snake_case  :       int     			=						len(UpperCamelCase__      )

				snake_case  :       int     			=						self.dummy_model()
				snake_case  :       str     			=						self.dummy_sample_deter
				snake_case  :       Any     			=						self.dummy_sample_deter + 0.1
				snake_case  :       int     			=						self.dummy_sample_deter - 0.1

				snake_case  :       Optional[int]     			=						samplea.shape[0]
				snake_case  :       Optional[Any]     			=						torch.stack([samplea, samplea, samplea]  ,	dim=0      )
				snake_case  :       str     			=						torch.arange(UpperCamelCase__      )[0:3, None].repeat(1  ,	UpperCamelCase__      )

				snake_case  :       int     			=						model(samples.flatten(0  ,	1      )  ,	timesteps.flatten(0  ,	1      )      )
				snake_case  :       Optional[int]     			=						scheduler.batch_step_no_noise(UpperCamelCase__  ,	timesteps.flatten(0  ,	1      )  ,	samples.flatten(0  ,	1      )      )

				snake_case  :       Optional[Any]     			=						torch.sum(torch.abs(UpperCamelCase__      )      )
				snake_case  :       str     			=						torch.mean(torch.abs(UpperCamelCase__      )      )

				assert abs(result_sum.item() - 1_153.1_833      ) < 1e-2
				assert abs(result_mean.item() - 0.5_005      ) < 1e-3



		def       lowerCAmelCase				(      self				:			Dict      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Any     			=						self.scheduler_classes[0]
				snake_case  :       Any     			=						self.get_scheduler_config()
				snake_case  :       Optional[int]     			=						scheduler_class(**UpperCamelCase__      )

				snake_case  :       Tuple     			=						len(UpperCamelCase__      )

				snake_case  :       Tuple     			=						self.dummy_model()
				snake_case  :       Dict     			=						self.dummy_sample_deter
				snake_case  :       str     			=						torch.manual_seed(0      )

				for t in reversed(range(UpperCamelCase__      )      ):
						# 1. predict noise residual
						snake_case  :       Tuple     			=						model(UpperCamelCase__  ,	UpperCamelCase__      )

						# 2. predict previous mean of sample x_t-1
						snake_case  :       List[Any]     			=						scheduler.step(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	generator=UpperCamelCase__      ).prev_sample

						snake_case  :       str     			=						pred_prev_sample

				snake_case  :       Any     			=						torch.sum(torch.abs(UpperCamelCase__      )      )
				snake_case  :       Dict     			=						torch.mean(torch.abs(UpperCamelCase__      )      )

				assert abs(result_sum.item() - 258.9_606      ) < 1e-2
				assert abs(result_mean.item() - 0.3_372      ) < 1e-3



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.scheduler_classes[0]
				snake_case  :       Any     			=						self.get_scheduler_config(prediction_type='''v_prediction'''      )
				snake_case  :       List[Any]     			=						scheduler_class(**UpperCamelCase__      )

				snake_case  :       int     			=						len(UpperCamelCase__      )

				snake_case  :       Dict     			=						self.dummy_model()
				snake_case  :       Optional[Any]     			=						self.dummy_sample_deter
				snake_case  :       str     			=						torch.manual_seed(0      )

				for t in reversed(range(UpperCamelCase__      )      ):
						# 1. predict noise residual
						snake_case  :       Any     			=						model(UpperCamelCase__  ,	UpperCamelCase__      )

						# 2. predict previous mean of sample x_t-1
						snake_case  :       Tuple     			=						scheduler.step(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	generator=UpperCamelCase__      ).prev_sample

						snake_case  :       List[Any]     			=						pred_prev_sample

				snake_case  :       Optional[int]     			=						torch.sum(torch.abs(UpperCamelCase__      )      )
				snake_case  :       Optional[int]     			=						torch.mean(torch.abs(UpperCamelCase__      )      )

				assert abs(result_sum.item() - 202.0_296      ) < 1e-2
				assert abs(result_mean.item() - 0.2_631      ) < 1e-3



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






				"""simple docstring"""







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

				snake_case  :       List[str]     			=						[100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=UpperCamelCase__      )

				snake_case  :       Optional[int]     			=						scheduler.timesteps

				for i, timestep in enumerate(UpperCamelCase__      ):
						if i == len(UpperCamelCase__      ) - 1:
								snake_case  :       List[str]     			=						-1
						else:
								snake_case  :       Dict     			=						timesteps[i + 1]

						snake_case  :       Optional[int]     			=						scheduler.previous_timestep(UpperCamelCase__      )
						snake_case  :       List[Any]     			=						prev_t.item()

						self.assertEqual(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Dict      )						->			Any:






				"""simple docstring"""







				snake_case  :       str     			=						self.scheduler_classes[0]
				snake_case  :       Union[str, Any]     			=						self.get_scheduler_config()
				snake_case  :       Dict     			=						scheduler_class(**UpperCamelCase__      )

				snake_case  :       Tuple     			=						[100, 87, 50, 51, 0]

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



		def       lowerCAmelCase				(      self				:			Any      )						->			int:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.scheduler_classes[0]
				snake_case  :       int     			=						self.get_scheduler_config()
				snake_case  :       int     			=						scheduler_class(**UpperCamelCase__      )

				snake_case  :       List[Any]     			=						[100, 87, 50, 1, 0]
				snake_case  :       Optional[Any]     			=						len(UpperCamelCase__      )

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



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






				"""simple docstring"""







				snake_case  :       str     			=						self.scheduler_classes[0]
				snake_case  :       Optional[int]     			=						self.get_scheduler_config()
				snake_case  :       List[Any]     			=						scheduler_class(**UpperCamelCase__      )

				snake_case  :       Tuple     			=						[scheduler.config.num_train_timesteps]

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




'''simple docstring'''
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@register_to_config
		def __init__(      self				:			List[str]  ,	UpperCamelCase__				:			int = 16  ,	UpperCamelCase__				:			int = 88  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			int = 1  ,	UpperCamelCase__				:			float = 0.0  ,	UpperCamelCase__				:			int = 32  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[int] = None  ,	UpperCamelCase__				:			str = "geglu"  ,	UpperCamelCase__				:			bool = True  ,	UpperCamelCase__				:			bool = True  ,	)						->			List[Any]:






				"""simple docstring"""







				super().__init__()
				snake_case  :       Optional[int]     			=						num_attention_heads
				snake_case  :       int     			=						attention_head_dim
				snake_case  :       Dict     			=						num_attention_heads * attention_head_dim

				snake_case  :       Tuple     			=						in_channels

				snake_case  :       Optional[int]     			=						torch.nn.GroupNorm(num_groups=UpperCamelCase__  ,	num_channels=UpperCamelCase__  ,	eps=1e-6  ,	affine=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )

				# 3. Define transformers blocks
				snake_case  :       Union[str, Any]     			=						nn.ModuleList(
				    [
				        BasicTransformerBlock(
				            UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	dropout=UpperCamelCase__  ,	cross_attention_dim=UpperCamelCase__  ,	activation_fn=UpperCamelCase__  ,	attention_bias=UpperCamelCase__  ,	double_self_attention=UpperCamelCase__  ,	norm_elementwise_affine=UpperCamelCase__  ,	)
				        for d in range(UpperCamelCase__      )
				    ]      )

				snake_case  :       Any     			=						nn.Linear(UpperCamelCase__  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Any=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Dict=None  ,	UpperCamelCase__				:			Union[str, Any]=1  ,	UpperCamelCase__				:			Optional[int]=None  ,	UpperCamelCase__				:			bool = True  ,	)						->			Any:






				"""simple docstring"""







				snake_case			,snake_case			,snake_case			,snake_case  :       Tuple     			=						hidden_states.shape
				snake_case  :       List[Any]     			=						batch_frames // num_frames

				snake_case  :       Union[str, Any]     			=						hidden_states

				snake_case  :       Optional[Any]     			=						hidden_states[None, :].reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       int     			=						hidden_states.permute(0  ,	2  ,	1  ,	3  ,	4      )

				snake_case  :       Tuple     			=						self.norm(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						hidden_states.permute(0  ,	3  ,	4  ,	2  ,	1      ).reshape(batch_size * height * width  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						self.proj_in(UpperCamelCase__      )

				# 2. Blocks
				for block in self.transformer_blocks:
						snake_case  :       Union[str, Any]     			=						block(
						    UpperCamelCase__  ,	encoder_hidden_states=UpperCamelCase__  ,	timestep=UpperCamelCase__  ,	cross_attention_kwargs=UpperCamelCase__  ,	class_labels=UpperCamelCase__  ,	)

				# 3. Output
				snake_case  :       Dict     			=						self.proj_out(UpperCamelCase__      )
				snake_case  :       Tuple     			=						(
				    hidden_states[None, None, :]
				    .reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				    .permute(0  ,	3  ,	4  ,	1  ,	2      )
				    .contiguous()
				)
				snake_case  :       List[Any]     			=						hidden_states.reshape(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				snake_case  :       Any     			=						hidden_states + residual

				if not return_dict:
						return (output,)

				return TransformerTemporalModelOutput(sample=UpperCamelCase__      )


