
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	_UpperCAmelCase       :		Any       =					(IPNDMScheduler,)
	_UpperCAmelCase       :		str       =					(("num_inference_steps", 5_0),)



	def  A							(  self	:    Tuple			,  **lowercase	:    Any	):


				'''simple docstring'''


				_snake_case		=			{'num_train_timesteps': 1_000}
				config.update(**lowercase	)
				return config



	def  A							(  self	:    Optional[int]			,  lowercase	:    List[Any]=0			,  **lowercase	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			dict(self.forward_default_kwargs	)
				_snake_case		=			kwargs.pop('num_inference_steps'			,  lowercase	)
				_snake_case		=			self.dummy_sample
				_snake_case		=			0.1 * sample
				_snake_case		=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
							_snake_case		=			self.get_scheduler_config(**lowercase	)
							_snake_case		=			scheduler_class(**lowercase	)
							scheduler.set_timesteps(lowercase	)
							# copy over dummy past residuals
							_snake_case		=			dummy_past_residuals[:]

							if time_step is None:
										_snake_case		=			scheduler.timesteps[len(scheduler.timesteps	) // 2]

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

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample
							_snake_case		=			new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample

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

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample
							_snake_case		=			new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample

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



	def  A							(  self	:    int	):


				'''simple docstring'''


				pass



	def  A							(  self	:    Tuple			,  lowercase	:    Tuple=0			,  **lowercase	:    Optional[Any]	):


				'''simple docstring'''


				_snake_case		=			dict(self.forward_default_kwargs	)
				_snake_case		=			kwargs.pop('num_inference_steps'			,  lowercase	)
				_snake_case		=			self.dummy_sample
				_snake_case		=			0.1 * sample
				_snake_case		=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
							_snake_case		=			self.get_scheduler_config()
							_snake_case		=			scheduler_class(**lowercase	)
							scheduler.set_timesteps(lowercase	)

							# copy over dummy past residuals (must be after setting timesteps)
							_snake_case		=			dummy_past_residuals[:]

							if time_step is None:
										_snake_case		=			scheduler.timesteps[len(scheduler.timesteps	) // 2]

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

										# copy over dummy past residual (must be after setting timesteps)
										_snake_case		=			dummy_past_residuals[:]

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample
							_snake_case		=			new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample

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

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample
							_snake_case		=			new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample

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



	def  A							(  self	:    List[Any]			,  **lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			self.scheduler_classes[0]
				_snake_case		=			self.get_scheduler_config(**lowercase	)
				_snake_case		=			scheduler_class(**lowercase	)

				_snake_case		=			10
				_snake_case		=			self.dummy_model()
				_snake_case		=			self.dummy_sample_deter
				scheduler.set_timesteps(lowercase	)

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

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

				return sample



	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			dict(self.forward_default_kwargs	)

				_snake_case		=			kwargs.pop('num_inference_steps'			,  lowercase	)

				for scheduler_class in self.scheduler_classes:
							_snake_case		=			self.get_scheduler_config()
							_snake_case		=			scheduler_class(**lowercase	)

							_snake_case		=			self.dummy_sample
							_snake_case		=			0.1 * sample

							if num_inference_steps is not None and hasattr(lowercase			,  'set_timesteps'	):
										scheduler.set_timesteps(lowercase	)
							elif num_inference_steps is not None and not hasattr(lowercase			,  'set_timesteps'	):
										_snake_case		=			num_inference_steps

							# copy over dummy past residuals (must be done after set_timesteps)
							_snake_case		=			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
							_snake_case		=			dummy_past_residuals[:]

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

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample
							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample

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

							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample
							_snake_case		=			scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase	).prev_sample

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



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				for timesteps in [100, 1_000]:
							self.check_over_configs(num_train_timesteps=lowercase			,  time_step=lowercase	)



	def  A							(  self	:    int	):


				'''simple docstring'''


				for t, num_inference_steps in zip([1, 5, 10]			,  [10, 50, 100]	):
							self.check_over_forward(num_inference_steps=lowercase			,  time_step=lowercase	)





	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			self.full_loop()
				_snake_case		=			torch.mean(torch.abs(lowercase	)	)

				assert abs(result_mean.item() - 2_540_529	) < 10

import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCamelCase       :				List[Any]       =			HfApi()

_lowerCamelCase       :				Dict       =			{}
# fmt: off
_lowerCamelCase       :				List[Any]       =			torch.tensor([
    -0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
    1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
    -1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
    0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
_lowerCamelCase       :				int       =			torch.tensor([
    -2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
    1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
    -2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
    2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
_lowerCamelCase       :				Optional[int]       =			torch.tensor([
    -0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
    -0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
    -0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
    0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
_lowerCamelCase       :				Dict       =			torch.tensor([
    0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
    -0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
    0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
    -0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
_lowerCamelCase       :				Dict       =			torch.tensor([
    0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
    -0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
    0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
    -0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
_lowerCamelCase       :				List[Any]       =			torch.tensor([
    0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
    -0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
    0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
    -0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
_lowerCamelCase       :				Dict       =			torch.tensor([
    0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
    -0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
    0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
    -0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
_lowerCamelCase       :				int       =			torch.tensor([
    0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
    -0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
    0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
    -0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
_lowerCamelCase       :				int       =			torch.tensor([
    -1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
    1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
    -2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
    1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
_lowerCamelCase       :				Tuple       =			torch.tensor([
    -1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
    0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
    -2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
    1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
_lowerCamelCase       :				List[str]       =			torch.tensor([
    -1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
    0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
    -2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
    1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
_lowerCamelCase       :				int       =			torch.tensor([
    -2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
    1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
    -3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
    3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
_lowerCamelCase       :				Tuple       =			torch.tensor([
    -2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
    1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
    -2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
    2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
_lowerCamelCase       :				int       =			torch.tensor([
    -2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
    1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
    -3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
    3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
_lowerCamelCase       :				List[Any]       =			torch.tensor([
    -1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
    1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
    -2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
    1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on

_lowerCamelCase       :				List[str]       =			api.list_models(filter='''diffusers''')
for mod in models:
			if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
						_lowerCamelCase       :				Any       =			'''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

						print(F'Started running {mod.modelId}!!!')

						if mod.modelId.startswith('''CompVis'''):
									_lowerCamelCase       :				Optional[Any]       =			UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
						else:
									_lowerCamelCase       :				int       =			UNetaDModel.from_pretrained(local_checkpoint)

						torch.manual_seed(0)
						random.seed(0)

						_lowerCamelCase       :				Union[str, Any]       =			torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
						_lowerCamelCase       :				int       =			torch.tensor([10] * noise.shape[0])
						with torch.no_grad():
									_lowerCamelCase       :				int       =			model(noise, time_step).sample

						assert torch.allclose(
						    logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
						)
						print(F'{mod.modelId} has passed successfully!!!')