
import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'linear'
	a			:Union[str, Any]         =	'cosine'
	a			:List[str]         =	'cosine_with_restarts'
	a			:Dict         =	'polynomial'
	a			:Tuple         =	'constant'
	a			:int         =	'constant_with_warmup'
	a			:Union[str, Any]         =	'piecewise_constant'




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					int = -1					):


				'''simple docstring'''
				return LambdaLR(snake_case__						,   lambda snake_case__					: 1						,   last_epoch=snake_case__					)




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					int						,   snake_case__:					int = -1					):


				'''simple docstring'''

				def lr_lambda(snake_case__:					int					):
								if current_step < num_warmup_steps:
												return float(snake_case__					) / float(max(1.0						,   snake_case__					)					)
								return 1.0

				return LambdaLR(snake_case__						,   snake_case__						,   last_epoch=snake_case__					)




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					str						,   snake_case__:					int = -1					):


				'''simple docstring'''
				lowercase_       =					{}
				lowercase_       =					step_rules.split(''','''					)
				for rule_str in rule_list[:-1]:
								lowercase_  ,	lowercase_       =					rule_str.split(''':'''					)
								lowercase_       =					int(snake_case__					)
								lowercase_       =					float(snake_case__					)
								lowercase_       =					value
				lowercase_       =					float(rule_list[-1]					)

				def create_rules_function(snake_case__:					Optional[int]						,   snake_case__:					int					):
								def rule_func(snake_case__:					int					) -> float:
												lowercase_       =					sorted(rules_dict.keys()					)
												for i, sorted_step in enumerate(snake_case__					):
																if steps < sorted_step:
																				return rules_dict[sorted_steps[i]]
												return last_lr_multiple

								return rule_func

				lowercase_       =					create_rules_function(snake_case__						,   snake_case__					)

				return LambdaLR(snake_case__						,   snake_case__						,   last_epoch=snake_case__					)




def 				a						(							snake_case__:					List[str]						,   snake_case__:					List[Any]						,   snake_case__:					Dict						,   snake_case__:					int=-1					):


				'''simple docstring'''

				def lr_lambda(snake_case__:					int					):
								if current_step < num_warmup_steps:
												return float(snake_case__					) / float(max(1						,   snake_case__					)					)
								return max(
								    0.0						,   float(num_training_steps - current_step					) / float(max(1						,   num_training_steps - num_warmup_steps					)					)					)

				return LambdaLR(snake_case__						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					float = 0.5						,   snake_case__:					int = -1					):


				'''simple docstring'''

				def lr_lambda(snake_case__:					List[Any]					):
								if current_step < num_warmup_steps:
												return float(snake_case__					) / float(max(1						,   snake_case__					)					)
								lowercase_       =					float(current_step - num_warmup_steps					) / float(max(1						,   num_training_steps - num_warmup_steps					)					)
								return max(0.0						,   0.5 * (1.0 + math.cos(math.pi * float(snake_case__					) * 2.0 * progress					))					)

				return LambdaLR(snake_case__						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					Optimizer						,   snake_case__:					int						,   snake_case__:					int						,   snake_case__:					int = 1						,   snake_case__:					int = -1					):


				'''simple docstring'''

				def lr_lambda(snake_case__:					Any					):
								if current_step < num_warmup_steps:
												return float(snake_case__					) / float(max(1						,   snake_case__					)					)
								lowercase_       =					float(current_step - num_warmup_steps					) / float(max(1						,   num_training_steps - num_warmup_steps					)					)
								if progress >= 1.0:
												return 0.0
								return max(0.0						,   0.5 * (1.0 + math.cos(math.pi * ((float(snake_case__					) * progress) % 1.0)					))					)

				return LambdaLR(snake_case__						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					Dict						,   snake_case__:					Dict						,   snake_case__:					List[str]						,   snake_case__:					Union[str, Any]=1e-7						,   snake_case__:					Tuple=1.0						,   snake_case__:					Optional[Any]=-1					):


				'''simple docstring'''
				lowercase_       =					optimizer.defaults['''lr''']
				if not (lr_init > lr_end):
								raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''					)

				def lr_lambda(snake_case__:					int					):
								if current_step < num_warmup_steps:
												return float(snake_case__					) / float(max(1						,   snake_case__					)					)
								elif current_step > num_training_steps:
												return lr_end / lr_init  # as LambdaLR multiplies by lr_init
								else:
												lowercase_       =					lr_init - lr_end
												lowercase_       =					num_training_steps - num_warmup_steps
												lowercase_       =					1 - (current_step - num_warmup_steps) / decay_steps
												lowercase_       =					lr_range * pct_remaining**power + lr_end
												return decay / lr_init  # as LambdaLR multiplies by lr_init

				return LambdaLR(snake_case__						,   snake_case__						,   snake_case__					)


__a  			=					{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}




def 				a						(							snake_case__:					Union[str, SchedulerType]						,   snake_case__:					Optimizer						,   snake_case__:					Optional[str] = None						,   snake_case__:					Optional[int] = None						,   snake_case__:					Optional[int] = None						,   snake_case__:					int = 1						,   snake_case__:					float = 1.0						,   snake_case__:					int = -1						,   ):


				'''simple docstring'''
				lowercase_       =					SchedulerType(snake_case__					)
				lowercase_       =					TYPE_TO_SCHEDULER_FUNCTION[name]
				if name == SchedulerType.CONSTANT:
								return schedule_func(snake_case__						,   last_epoch=snake_case__					)

				if name == SchedulerType.PIECEWISE_CONSTANT:
								return schedule_func(snake_case__						,   step_rules=snake_case__						,   last_epoch=snake_case__					)

				# All other schedulers require `num_warmup_steps`
				if num_warmup_steps is None:
								raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''					)

				if name == SchedulerType.CONSTANT_WITH_WARMUP:
								return schedule_func(snake_case__						,   num_warmup_steps=snake_case__						,   last_epoch=snake_case__					)

				# All other schedulers require `num_training_steps`
				if num_training_steps is None:
								raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''					)

				if name == SchedulerType.COSINE_WITH_RESTARTS:
								return schedule_func(
								    snake_case__						,   num_warmup_steps=snake_case__						,   num_training_steps=snake_case__						,   num_cycles=snake_case__						,   last_epoch=snake_case__						,   )

				if name == SchedulerType.POLYNOMIAL:
								return schedule_func(
								    snake_case__						,   num_warmup_steps=snake_case__						,   num_training_steps=snake_case__						,   power=snake_case__						,   last_epoch=snake_case__						,   )

				return schedule_func(
				    snake_case__						,   num_warmup_steps=snake_case__						,   num_training_steps=snake_case__						,   last_epoch=snake_case__					)








import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		1
           _lowercase       :							Any      		=		3
           _lowercase       :							Tuple      		=		(32, 32)

           _lowercase       :							Tuple      		=		floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0)).to(lowerCamelCase)
           return image
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		UNetaDConditionModel(
               block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=4,      out_channels=4,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),      cross_attention_dim=32,      )
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							str      		=		AutoencoderKL(
               block_out_channels=[32, 64],      in_channels=3,      out_channels=3,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],      latent_channels=4,      )
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		RobertaSeriesConfig(
               hidden_size=32,      project_dim=32,      intermediate_size=37,      layer_norm_eps=1E-05,      num_attention_heads=4,      num_hidden_layers=5,      pad_token_id=1,      vocab_size=50_06,      )
           return RobertaSeriesModelWithTransformation(lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           def extract(*lowerCamelCase,      **lowerCamelCase):






               class _lowerCamelCase:
                      def __init__(					self)       ->   Optional[Any]:




                          """simple docstring"""

                          _lowercase       :							Optional[int]      		=		torch.ones([0])




                      def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   int:




                          """simple docstring"""

                          self.pixel_values.to(lowerCamelCase)
                          return self


               return Out()

           return extract
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Any      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Union[str, Any]      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.dummy_vae
           _lowercase       :							List[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Any      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Tuple      		=		77

           _lowercase       :							int      		=		self.dummy_image.to(lowerCamelCase)
           _lowercase       :							int      		=		init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           _lowercase       :							Union[str, Any]      		=		AltDiffusionImgaImgPipeline(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      vae=lowerCamelCase,      text_encoder=lowerCamelCase,      tokenizer=lowerCamelCase,      safety_checker=lowerCamelCase,      feature_extractor=self.dummy_extractor,      )
           _lowercase       :							List[Any]      		=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,      do_normalize=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		alt_pipe.to(lowerCamelCase)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		'A painting of a squirrel eating a burger'
           _lowercase       :							Dict      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Any      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      )

           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Optional[Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      return_dict=lowerCamelCase,      )[0]

           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							Dict      		=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           _lowercase       :							int      		=		np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3
       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Tuple      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							str      		=		self.dummy_vae
           _lowercase       :							Optional[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Optional[Any]      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Optional[Any]      		=		77

           _lowercase       :							str      		=		self.dummy_image.to(lowerCamelCase)

           # put models in fp16
           _lowercase       :							List[str]      		=		unet.half()
           _lowercase       :							List[Any]      		=		vae.half()
           _lowercase       :							Any      		=		bert.half()

           # make sure here that pndm scheduler skips prk
           _lowercase       :							Union[str, Any]      		=		AltDiffusionImgaImgPipeline(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      vae=lowerCamelCase,      text_encoder=lowerCamelCase,      tokenizer=lowerCamelCase,      safety_checker=lowerCamelCase,      feature_extractor=self.dummy_extractor,      )
           _lowercase       :							List[str]      		=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,      do_normalize=lowerCamelCase)
           _lowercase       :							Any      		=		alt_pipe.to(lowerCamelCase)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							int      		=		'A painting of a squirrel eating a burger'
           _lowercase       :							Optional[Any]      		=		torch.manual_seed(0)
           _lowercase       :							Union[str, Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      ).images

           assert image.shape == (1, 32, 32, 3)




       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           # resize to resolution that is divisible by 8 but not 16 or 32
           _lowercase       :							str      		=		init_image.resize((7_60, 5_04))

           _lowercase       :							Optional[int]      		=		'BAAI/AltDiffusion'
           _lowercase       :							str      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							List[str]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							Any      		=		torch.manual_seed(0)
           _lowercase       :							Dict      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							List[str]      		=		output.images[0]

           _lowercase       :							Tuple      		=		image[2_55:2_58, 3_83:3_86, -1]

           assert image.shape == (5_04, 7_60, 3)
           _lowercase       :							Optional[Any]      		=		np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							str      		=		init_image.resize((7_68, 5_12))
           _lowercase       :							Any      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy')

           _lowercase       :							str      		=		'BAAI/AltDiffusion'
           _lowercase       :							Optional[Any]      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							int      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							int      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Union[str, Any]      		=		output.images[0]

           assert image.shape == (5_12, 7_68, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image).max() < 1E-2


