





'''simple docstring'''




import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch
			from torch import nn

			from transformers import (
			    Adafactor,
			    AdamW,
			    get_constant_schedule,
			    get_constant_schedule_with_warmup,
			    get_cosine_schedule_with_warmup,
			    get_cosine_with_hard_restarts_schedule_with_warmup,
			    get_inverse_sqrt_schedule,
			    get_linear_schedule_with_warmup,
			    get_polynomial_decay_schedule_with_warmup,
			)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=10							)   ->Optional[Any]:
	snake_case__	       =   []
	for _ in range(UpperCAmelCase_							):
		lrs.append(scheduler.get_lr()[0]							)
		scheduler.step()
	return lrs




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=10							)   ->Dict:
	snake_case__	       =   []
	for step in range(UpperCAmelCase_							):
		lrs.append(scheduler.get_lr()[0]							)
		scheduler.step()
		if step == num_steps // 2:
			with tempfile.TemporaryDirectory() as tmpdirname:
				snake_case__	       =   os.path.join(UpperCAmelCase_					,				'schedule.bin'							)
				torch.save(scheduler.state_dict()					,				UpperCAmelCase_							)

				snake_case__	       =   torch.load(UpperCAmelCase_							)
				scheduler.load_state_dict(UpperCAmelCase_							)
	return lrs






@require_torch
class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[int]:
		self.assertEqual(len(UpperCamelCase_		)      ,       len(UpperCamelCase_		)		)
		for a, b in zip(UpperCamelCase_      ,       UpperCamelCase_		):
			self.assertAlmostEqual(UpperCamelCase_      ,       UpperCamelCase_      ,       delta=UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   torch.tensor([0.1, -0.2, -0.1]      ,       requires_grad=UpperCamelCase_		)
		snake_case__	       =   torch.tensor([0.4, 0.2, -0.5]		)
		snake_case__	       =   nn.MSELoss()
		# No warmup, constant schedule, no gradient clipping
		snake_case__	       =   AdamW(params=[w]      ,       lr=2E-1      ,       weight_decay=0.0		)
		for _ in range(100		):
			snake_case__	       =   criterion(UpperCamelCase_      ,       UpperCamelCase_		)
			loss.backward()
			optimizer.step()
			w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
			w.grad.zero_()
		self.assertListAlmostEqual(w.tolist()      ,       [0.4, 0.2, -0.5]      ,       tol=1E-2		)





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   torch.tensor([0.1, -0.2, -0.1]      ,       requires_grad=UpperCamelCase_		)
		snake_case__	       =   torch.tensor([0.4, 0.2, -0.5]		)
		snake_case__	       =   nn.MSELoss()
		# No warmup, constant schedule, no gradient clipping
		snake_case__	       =   Adafactor(
		    params=[w]      ,       lr=1E-2      ,       eps=(1E-3_0, 1E-3)      ,       clip_threshold=1.0      ,       decay_rate=-0.8      ,       betaa=UpperCamelCase_      ,       weight_decay=0.0      ,       relative_step=UpperCamelCase_      ,       scale_parameter=UpperCamelCase_      ,       warmup_init=UpperCamelCase_      ,       )
		for _ in range(1000		):
			snake_case__	       =   criterion(UpperCamelCase_      ,       UpperCamelCase_		)
			loss.backward()
			optimizer.step()
			w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
			w.grad.zero_()
		self.assertListAlmostEqual(w.tolist()      ,       [0.4, 0.2, -0.5]      ,       tol=1E-2		)






@require_torch
class 				__snake_case     (					unittest.TestCase							):
	__lowerCAmelCase       						=  nn.Linear(50       ,       50							) if is_torch_available() else None
	__lowerCAmelCase       						=  AdamW(m.parameters()       ,       lr=10.0							) if is_torch_available() else None
	__lowerCAmelCase       						=  10





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=None		)      ->		Union[str, Any]:
		self.assertEqual(len(UpperCamelCase_		)      ,       len(UpperCamelCase_		)		)
		for a, b in zip(UpperCamelCase_      ,       UpperCamelCase_		):
			self.assertAlmostEqual(UpperCamelCase_      ,       UpperCamelCase_      ,       delta=UpperCamelCase_      ,       msg=UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   {'num_warmup_steps': 2, 'num_training_steps': 10}
		# schedulers doct format
		# function: (sched_args_dict, expected_learning_rates)
		snake_case__	       =   {
		    get_constant_schedule: ({}, [1_0.0] * self.num_steps),
		    get_constant_schedule_with_warmup: (
		        {'num_warmup_steps': 4},
		        [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
		    ),
		    get_linear_schedule_with_warmup: (
		        {**common_kwargs},
		        [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
		    ),
		    get_cosine_schedule_with_warmup: (
		        {**common_kwargs},
		        [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
		    ),
		    get_cosine_with_hard_restarts_schedule_with_warmup: (
		        {**common_kwargs, 'num_cycles': 2},
		        [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
		    ),
		    get_polynomial_decay_schedule_with_warmup: (
		        {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
		        [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
		    ),
		    get_inverse_sqrt_schedule: (
		        {'num_warmup_steps': 2},
		        [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
		    ),
		}

		for scheduler_func, data in scheds.items():
			snake_case__       ,  snake_case__	       =   data

			snake_case__	       =   scheduler_func(self.optimizer      ,       **UpperCamelCase_		)
			self.assertEqual(len([scheduler.get_lr()[0]]		)      ,       1		)
			snake_case__	       =   unwrap_schedule(UpperCamelCase_      ,       self.num_steps		)
			self.assertListAlmostEqual(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       tol=1E-2      ,       msg=F'''failed for {scheduler_func} in normal scheduler'''      ,       )

			snake_case__	       =   scheduler_func(self.optimizer      ,       **UpperCamelCase_		)
			if scheduler_func.__name__ != "get_constant_schedule":
				LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase_		)  # wrap to test picklability of the schedule
			snake_case__	       =   unwrap_and_save_reload_schedule(UpperCamelCase_      ,       self.num_steps		)
			self.assertListEqual(UpperCamelCase_      ,       UpperCamelCase_      ,       msg=F'''failed for {scheduler_func} in save and reload'''		)






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_		)      ->		List[str]:
		snake_case__	       =   fn





	def __call__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		Optional[Any]:
		return self.fn(*UpperCamelCase_      ,       **UpperCamelCase_		)





	@classmethod
	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Tuple:
		snake_case__	       =   list(map(self      ,       scheduler.lr_lambdas		)		)










'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		snake_case__	       =   DDIMScheduler.from_config(scheduler.config		)

		self.register_modules(unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(UpperCamelCase_		)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

		# set step values
		self.scheduler.set_timesteps(UpperCamelCase_		)

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).prev_sample

		snake_case__	       =   (image / 2 + 0.5).clamp(0      ,       1		)
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		).numpy()
		if output_type == "pil":
			snake_case__	       =   self.numpy_to_pil(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)



