
'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Dict										=  KandinskyVaaControlnetImgaImgPipeline
						_lowercase						: int										=  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
						_lowercase						: Tuple										=  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
						_lowercase						: Dict										=  [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''strength''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_lowercase						: str										=  False




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 32




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 32




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.time_input_dim




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.time_input_dim * 4




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 100




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )

												_lowerCAmelCase    		=					{
												    """in_channels""": 8,
												    # Out channels is double in channels because predicts mean and variance
												    """out_channels""": 8,
												    """addition_embed_type""": """image_hint""",
												    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
												    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
												    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
												    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
												    """layers_per_block""": 1,
												    """encoder_hid_dim""": self.text_embedder_hidden_size,
												    """encoder_hid_dim_type""": """image_proj""",
												    """cross_attention_dim""": self.cross_attention_dim,
												    """attention_head_dim""": 4,
												    """resnet_time_scale_shift""": """scale_shift""",
												    """class_embed_type""": None,
												}

												_lowerCAmelCase    		=					UNetaDConditionModel(**_lowercase )
												return model




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return {
												    "block_out_channels": [32, 32, 64, 64],
												    "down_block_types": [
												        "DownEncoderBlock2D",
												        "DownEncoderBlock2D",
												        "DownEncoderBlock2D",
												        "AttnDownEncoderBlock2D",
												    ],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 12,
												    "out_channels": 3,
												    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
												    "vq_embed_dim": 4,
												}




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )
												_lowerCAmelCase    		=					VQModel(**self.dummy_movq_kwargs )
												return model




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.dummy_unet
												_lowerCAmelCase    		=					self.dummy_movq

												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_schedule""": """linear""",
												    """beta_start""": 0.0_0085,
												    """beta_end""": 0.012,
												    """clip_sample""": False,
												    """set_alpha_to_one""": False,
												    """steps_offset""": 0,
												    """prediction_type""": """epsilon""",
												    """thresholding""": False,
												}

												_lowerCAmelCase    		=					DDIMScheduler(**_lowercase )

												_lowerCAmelCase    		=					{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """movq""": movq,
												}

												return components




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=0 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1 ) ).to(
												    _lowercase )
												# create init_image
												_lowerCAmelCase    		=					floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												_lowerCAmelCase    		=					Image.fromarray(np.uinta(_lowercase ) ).convert("""RGB""" ).resize((256, 256) )
												# create hint
												_lowerCAmelCase    		=					floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )

												if str(_lowercase ).startswith("""mps""" ):
																		_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												else:
																		_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(_lowercase )
												_lowerCAmelCase    		=					{
												    """image""": init_image,
												    """image_embeds""": image_embeds,
												    """negative_image_embeds""": negative_image_embeds,
												    """hint""": hint,
												    """generator""": generator,
												    """height""": 64,
												    """width""": 64,
												    """num_inference_steps""": 10,
												    """guidance_scale""": 7.0,
												    """strength""": 0.2,
												    """output_type""": """np""",
												}
												return inputs




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""cpu"""

												_lowerCAmelCase    		=					self.get_dummy_components()

												_lowerCAmelCase    		=					self.pipeline_class(**_lowercase )
												_lowerCAmelCase    		=					pipe.to(_lowercase )

												pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					pipe(**self.get_dummy_inputs(_lowercase ) )
												_lowerCAmelCase    		=					output.images

												_lowerCAmelCase    		=					pipe(
												    **self.get_dummy_inputs(_lowercase )       ,  return_dict=_lowercase       ,  )[0]

												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)

												_lowerCAmelCase    		=					np.array(
												    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736] )
												assert (
												    np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
												), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
												), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'






@slow
@require_torch_gpu
class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""" )

												_lowerCAmelCase    		=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
												_lowerCAmelCase    		=					init_image.resize((512, 512) )

												_lowerCAmelCase    		=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/kandinskyv22/hint_image_cat.png""" )
												_lowerCAmelCase    		=					torch.from_numpy(np.array(_lowercase ) ).float() / 255.0
												_lowerCAmelCase    		=					hint.permute(2       ,  0       ,  1 ).unsqueeze(0 )

												_lowerCAmelCase    		=					"""A robot, 4k photo"""

												_lowerCAmelCase    		=					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-prior"""       ,  torch_dtype=torch.floataa )
												pipe_prior.to(_lowercase )

												_lowerCAmelCase    		=					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-controlnet-depth"""       ,  torch_dtype=torch.floataa )
												_lowerCAmelCase    		=					pipeline.to(_lowercase )

												pipeline.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					torch.Generator(device="""cpu""" ).manual_seed(0 )

												_lowerCAmelCase		,       _lowerCAmelCase    		=					pipe_prior(
												    _lowercase       ,  image=_lowercase       ,  strength=0.85       ,  generator=_lowercase       ,  negative_prompt=""""""       ,  ).to_tuple()

												_lowerCAmelCase    		=					pipeline(
												    image=_lowercase       ,  image_embeds=_lowercase       ,  negative_image_embeds=_lowercase       ,  hint=_lowercase       ,  generator=_lowercase       ,  num_inference_steps=100       ,  height=512       ,  width=512       ,  strength=0.5       ,  output_type="""np"""       ,  )

												_lowerCAmelCase    		=					output.images[0]

												assert image.shape == (512, 512, 3)

												assert_mean_pixel_difference(_lowercase       ,  _lowercase )







import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class 				a			:






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										if isinstance(_lowerCAmelCase			, _lowerCAmelCase					):
																# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																# modified it, it will not be accepted here again, since `auto` values would have been overridden
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   deepcopy(_lowerCAmelCase					)
										elif os.path.exists(_lowerCAmelCase					):
																with io.open(_lowerCAmelCase			, '''r'''			, encoding='''utf-8'''					) as f:
																						__SCREAMING_SNAKE_CASE:				int           =   json.load(_lowerCAmelCase					)
										else:
																try:
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   baseaa.urlsafe_baadecode(_lowerCAmelCase					).decode('''utf-8'''					)
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   json.loads(_lowerCAmelCase					)
																except (UnicodeDecodeError, AttributeError, ValueError):
																						raise ValueError(
																						    f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""					)

										__SCREAMING_SNAKE_CASE:				str           =   config

										self.set_stage_and_offload()






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.get_value('''zero_optimization.stage'''			, -1					)

										# offload
										__SCREAMING_SNAKE_CASE:				Dict           =   False
										if self.is_zeroa() or self.is_zeroa():
																__SCREAMING_SNAKE_CASE:				str           =   set(['''cpu''', '''nvme''']					)
																__SCREAMING_SNAKE_CASE:				Tuple           =   set(
																    [
																        self.get_value('''zero_optimization.offload_optimizer.device'''					),
																        self.get_value('''zero_optimization.offload_param.device'''					),
																    ]					)
																if len(offload_devices & offload_devices_valid					) > 0:
																						__SCREAMING_SNAKE_CASE:				Dict           =   True






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   self.config

										# find the config node of interest if it exists
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   ds_key_long.split('''.'''					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   nodes.pop()
										for node in nodes:
																__SCREAMING_SNAKE_CASE:				int           =   config.get(_lowerCAmelCase					)
																if config is None:
																						return None, ds_key

										return config, ds_key






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   self.find_config_node(_lowerCAmelCase					)
										if config is None:
																return default
										return config.get(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=False					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.config

										# find the config node of interest if it exists
										__SCREAMING_SNAKE_CASE:				str           =   ds_key_long.split('''.'''					)
										for node in nodes:
																__SCREAMING_SNAKE_CASE:				Tuple           =   config
																__SCREAMING_SNAKE_CASE:				List[str]           =   config.get(_lowerCAmelCase					)
																if config is None:
																						if must_exist:
																												raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""					)
																						else:
																												return

        # if found remove it
										if parent_config is not None:
																parent_config.pop(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_value(_lowerCAmelCase					)
										return False if value is None else bool(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.get_value(_lowerCAmelCase					)
										return False if value is None else not bool(_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._stage == 2






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._stage == 3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._offload





class 				a			:






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   engine






				def 			snake_case_    (  self			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										self.engine.backward(_lowerCAmelCase			, **_lowerCAmelCase					)

										# Deepspeed's `engine.step` performs the following operations:
										# - gradient accumulation check
										# - gradient clipping
										# - optimizer step
										# - zero grad
										# - checking overflow
										# - lr_scheduler step (only if engine.lr_scheduler is not None)
										self.engine.step()
										# and this plugin overrides the above calls with no-ops when Accelerate runs under
										# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
										# training loop that works transparently under many training regimes.





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, device_placement=_lowerCAmelCase			, scaler=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   hasattr(self.optimizer			, '''overflow'''					)






				def 			snake_case_    (  self			, _lowerCAmelCase=None					):

										"""simple docstring"""




										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.__has_overflow__:
																return self.optimizer.overflow
										return False





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=0.001			, _lowerCAmelCase=0			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   params
										__SCREAMING_SNAKE_CASE:				int           =   lr
										__SCREAMING_SNAKE_CASE:				List[str]           =   weight_decay
										__SCREAMING_SNAKE_CASE:				List[Any]           =   kwargs





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   optimizer
										__SCREAMING_SNAKE_CASE:				int           =   total_num_steps
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   warmup_num_steps
										__SCREAMING_SNAKE_CASE:				str           =   kwargs



