


'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   CycleDiffusionPipeline
	A_           =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    "negative_prompt",
	    "height",
	    "width",
	    "negative_prompt_embeds",
	}
	A_           =   PipelineTesterMixin.required_optional_params - {"latents"}
	A_           =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
	A_           =   IMAGE_TO_IMAGE_IMAGE_PARAMS
	A_           =   IMAGE_TO_IMAGE_IMAGE_PARAMS




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a      :	str    =  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						,    )
						__a      :	Dict    =  DDIMScheduler(
						    beta_start=0.00085						,    beta_end=0.012						,    beta_schedule='scaled_linear'						,    num_train_timesteps=1000						,    clip_sample=__a						,    set_alpha_to_one=__a						,    )
						torch.manual_seed(0							)
						__a      :	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						,    )
						torch.manual_seed(0							)
						__a      :	Tuple    =  CLIPTextConfig(
						    bos_token_id=0						,    eos_token_id=2						,    hidden_size=32						,    intermediate_size=37						,    layer_norm_eps=1E-0_5						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1000						,    )
						__a      :	int    =  CLIPTextModel(__a							)
						__a      :	Union[str, Any]    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a      :	Optional[Any]    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components




	def 				__UpperCAmelCase							(       self						,    __a						,    __a=0							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  floats_tensor((1, 3, 32, 32)						,    rng=random.Random(__a							)							).to(__a							)
						__a      :	Optional[int]    =  image / 2 + 0.5
						if str(__a							).startswith('mps'							):
											__a      :	Dict    =  torch.manual_seed(__a							)
						else:
											__a      :	Dict    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a      :	Optional[Any]    =  {
						    'prompt': 'An astronaut riding an elephant',
						    'source_prompt': 'An astronaut riding a horse',
						    'image': image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'eta': 0.1,
						    'strength': 0.8,
						    'guidance_scale': 3,
						    'source_guidance_scale': 1,
						    'output_type': 'numpy',
						}
						return inputs




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  'cpu'  # ensure determinism for the device-dependent torch.Generator

						__a      :	Tuple    =  self.get_dummy_components()
						__a      :	int    =  CycleDiffusionPipeline(**__a							)
						__a      :	str    =  pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)

						__a      :	int    =  self.get_dummy_inputs(__a							)
						__a      :	Any    =  pipe(**__a							)
						__a      :	List[str]    =  output.images

						__a      :	List[str]    =  images[0, -3:, -3:, -1]

						assert images.shape == (1, 32, 32, 3)
						__a      :	Optional[Any]    =  np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]							)

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




	@unittest.skipIf(torch_device != 'cuda'						,    'This test requires a GPU'							)
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.get_dummy_components()
						for name, module in components.items():
											if hasattr(__a						,    'half'							):
																__a      :	Dict    =  module.half()
						__a      :	Optional[int]    =  CycleDiffusionPipeline(**__a							)
						__a      :	str    =  pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)

						__a      :	Dict    =  self.get_dummy_inputs(__a							)
						__a      :	Any    =  pipe(**__a							)
						__a      :	int    =  output.images

						__a      :	Tuple    =  images[0, -3:, -3:, -1]

						assert images.shape == (1, 32, 32, 3)
						__a      :	Any    =  np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]							)

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




	@skip_mps
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_save_load_local()




	@unittest.skip('non-deterministic pipeline'							)
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_inference_batch_single_identical()




	@skip_mps
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_dict_tuple_outputs_equivalent()




	@skip_mps
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_save_load_optional_components()





	@skip_mps
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return super().test_attention_slicing_forward_pass()





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/cycle-diffusion/black_colored_car.png'							)
						__a      :	Any    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'							)
						__a      :	Optional[int]    =  init_image.resize((512, 512)							)

						__a      :	List[Any]    =  'CompVis/stable-diffusion-v1-4'
						__a      :	List[str]    =  DDIMScheduler.from_pretrained(__a						,    subfolder='scheduler'							)
						__a      :	Optional[Any]    =  CycleDiffusionPipeline.from_pretrained(
						    __a						,    scheduler=__a						,    safety_checker=__a						,    torch_dtype=torch.floataa						,    revision='fp16'							)

						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a      :	Union[str, Any]    =  'A black colored car'
						__a      :	int    =  'A blue colored car'

						__a      :	int    =  torch.manual_seed(0							)
						__a      :	List[Any]    =  pipe(
						    prompt=__a						,    source_prompt=__a						,    image=__a						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__a						,    output_type='np'						,    )
						__a      :	List[Any]    =  output.images

						# the values aren't exactly equal, but the images look the same visually
						assert np.abs(image - expected_image							).max() < 5E-1





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/cycle-diffusion/black_colored_car.png'							)
						__a      :	Tuple    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'							)
						__a      :	Optional[Any]    =  init_image.resize((512, 512)							)

						__a      :	int    =  'CompVis/stable-diffusion-v1-4'
						__a      :	Tuple    =  DDIMScheduler.from_pretrained(__a						,    subfolder='scheduler'							)
						__a      :	Tuple    =  CycleDiffusionPipeline.from_pretrained(__a						,    scheduler=__a						,    safety_checker=__a							)

						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a      :	Union[str, Any]    =  'A black colored car'
						__a      :	Optional[Any]    =  'A blue colored car'

						__a      :	Optional[int]    =  torch.manual_seed(0							)
						__a      :	Optional[Any]    =  pipe(
						    prompt=__a						,    source_prompt=__a						,    image=__a						,    num_inference_steps=100						,    eta=0.1						,    strength=0.85						,    guidance_scale=3						,    source_guidance_scale=1						,    generator=__a						,    output_type='np'						,    )
						__a      :	Tuple    =  output.images

						assert np.abs(image - expected_image							).max() < 2E-2







'''simple docstring'''



import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
	from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					if is_torch_version('<'						,				'2.0.0'       ) or not hasattr(_SCREAMING_SNAKE_CASE						,				'_dynamo'       ):
										return False
					return isinstance(_SCREAMING_SNAKE_CASE						,				torch._dynamo.eval_frame.OptimizedModule       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       bool = True       ):
					__a      :	int    =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

					__a      :	Any    =  is_compiled_module(_SCREAMING_SNAKE_CASE       )
					if is_compiled:
										__a      :	List[Any]    =  model
										__a      :	Union[str, Any]    =  model._orig_mod

					if is_deepspeed_available():
										options += (DeepSpeedEngine,)

					while isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										__a      :	Union[str, Any]    =  model.module

					if not keep_fpaa_wrapper:
										__a      :	Optional[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				'forward'       )
										__a      :	str    =  model.__dict__.pop('_original_forward'						,				_SCREAMING_SNAKE_CASE       )
										if original_forward is not None:
															while hasattr(_SCREAMING_SNAKE_CASE						,				'__wrapped__'       ):
																				__a      :	Any    =  forward.__wrapped__
																				if forward == original_forward:
																									break
															__a      :	str    =  forward
										if getattr(_SCREAMING_SNAKE_CASE						,				'_converted_to_transformer_engine'						,				_SCREAMING_SNAKE_CASE       ):
															convert_model(_SCREAMING_SNAKE_CASE						,				to_transformer_engine=_SCREAMING_SNAKE_CASE       )

					if is_compiled:
										__a      :	List[str]    =  model
										__a      :	Optional[int]    =  compiled_model

					return model






def   lowerCamelCase							():
					PartialState().wait_for_everyone()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					if PartialState().distributed_type == DistributedType.TPU:
										xm.save(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					elif PartialState().local_process_index == 0:
										torch.save(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )






@contextmanager
def   lowerCamelCase							(**_SCREAMING_SNAKE_CASE				:       Tuple       ):
					for key, value in kwargs.items():
										__a      :	Optional[int]    =  str(_SCREAMING_SNAKE_CASE       )

					yield

					for key in kwargs:
										if key.upper() in os.environ:
															del os.environ[key.upper()]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Dict       ):
					if not hasattr(_SCREAMING_SNAKE_CASE						,				'__qualname__'       ) and not hasattr(_SCREAMING_SNAKE_CASE						,				'__name__'       ):
										__a      :	List[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				'__class__'						,				_SCREAMING_SNAKE_CASE       )
					if hasattr(_SCREAMING_SNAKE_CASE						,				'__qualname__'       ):
										return obj.__qualname__
					if hasattr(_SCREAMING_SNAKE_CASE						,				'__name__'       ):
										return obj.__name__
					return str(_SCREAMING_SNAKE_CASE       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					for key, value in source.items():
										if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
															__a      :	int    =  destination.setdefault(_SCREAMING_SNAKE_CASE						,				{}       )
															merge_dicts(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										else:
															__a      :	Tuple    =  value

					return destination






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int = None       ):
					if port is None:
										__a      :	List[str]    =  29_500
					with socket.socket(socket.AF_INET						,				socket.SOCK_STREAM       ) as s:
										return s.connect_ex(('localhost', port)       ) == 0



