
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 					lowercase__(							__lowerCAmelCase					,		__lowerCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Union[str, Any]       	=      CycleDiffusionPipeline
	a     :Dict       	=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    '''negative_prompt''',
	    '''height''',
	    '''width''',
	    '''negative_prompt_embeds''',
	}
	a     :Union[str, Any]       	=      PipelineTesterMixin.required_optional_params - {'''latents'''}
	a     :List[Any]       	=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}				)
	a     :Optional[Any]       	=      IMAGE_TO_IMAGE_IMAGE_PARAMS
	a     :Tuple       	=      IMAGE_TO_IMAGE_IMAGE_PARAMS
	def 		_lowercase    (			self    :     Optional[int]			)			->	Union[str, Any]:
					torch.manual_seed(0			)
					lowercase_	    =   UNetaDConditionModel(
					    block_out_channels=(3_2, 6_4)			,				layers_per_block=2			,				sample_size=3_2			,				in_channels=4			,				out_channels=4			,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,				cross_attention_dim=3_2			,				)
					lowercase_	    =   DDIMScheduler(
					    beta_start=0.0_00_85			,				beta_end=0.0_12			,				beta_schedule='''scaled_linear'''			,				num_train_timesteps=1_0_0_0			,				clip_sample=__SCREAMING_SNAKE_CASE			,				set_alpha_to_one=__SCREAMING_SNAKE_CASE			,				)
					torch.manual_seed(0			)
					lowercase_	    =   AutoencoderKL(
					    block_out_channels=[3_2, 6_4]			,				in_channels=3			,				out_channels=3			,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']			,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,				latent_channels=4			,				)
					torch.manual_seed(0			)
					lowercase_	    =   CLIPTextConfig(
					    bos_token_id=0			,				eos_token_id=2			,				hidden_size=3_2			,				intermediate_size=3_7			,				layer_norm_eps=1e-05			,				num_attention_heads=4			,				num_hidden_layers=5			,				pad_token_id=1			,				vocab_size=1_0_0_0			,				)
					lowercase_	    =   CLIPTextModel(__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

					lowercase_	    =   {
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''vae''': vae,
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''safety_checker''': None,
					    '''feature_extractor''': None,
					}
					return components
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=0			)			->	Optional[Any]:
					lowercase_	    =   floats_tensor((1, 3, 3_2, 3_2)			,				rng=random.Random(__SCREAMING_SNAKE_CASE			)			).to(__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   image / 2 + 0.5
					if str(__SCREAMING_SNAKE_CASE			).startswith('''mps'''			):
									lowercase_	    =   torch.manual_seed(__SCREAMING_SNAKE_CASE			)
					else:
									lowercase_	    =   torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   {
					    '''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 		_lowercase    (			self    :     Optional[Any]			)			->	Dict:
					lowercase_	    =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator

					lowercase_	    =   self.get_dummy_components()
					lowercase_	    =   CycleDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   pipe.to(__SCREAMING_SNAKE_CASE			)
					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

					lowercase_	    =   self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   pipe(**__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   output.images

					lowercase_	    =   images[0, -3:, -3:, -1]

					assert images.shape == (1, 3_2, 3_2, 3)
					lowercase_	    =   np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
	@unittest.skipIf(torch_device != '''cuda'''			,				'''This test requires a GPU'''			)
	def 		_lowercase    (			self    :     Any			)			->	Optional[Any]:
					lowercase_	    =   self.get_dummy_components()
					for name, module in components.items():
									if hasattr(__SCREAMING_SNAKE_CASE			,				'''half'''			):
													lowercase_	    =   module.half()
					lowercase_	    =   CycleDiffusionPipeline(**__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   pipe.to(__SCREAMING_SNAKE_CASE			)
					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

					lowercase_	    =   self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   pipe(**__SCREAMING_SNAKE_CASE			)
					lowercase_	    =   output.images

					lowercase_	    =   images[0, -3:, -3:, -1]

					assert images.shape == (1, 3_2, 3_2, 3)
					lowercase_	    =   np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]			)

					assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
	@skip_mps
	def 		_lowercase    (			self    :     Optional[Any]			)			->	List[str]:
					return super().test_save_load_local()
	@unittest.skip('''non-deterministic pipeline'''			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Optional[Any]:
					return super().test_inference_batch_single_identical()
	@skip_mps
	def 		_lowercase    (			self    :     Optional[Any]			)			->	List[str]:
					return super().test_dict_tuple_outputs_equivalent()
	@skip_mps
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Any:
					return super().test_save_load_optional_components()




	@skip_mps
	def 		_lowercase    (			self    :     str			)			->	Tuple:
					return super().test_attention_slicing_forward_pass()

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



	"""simple docstring"""




	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
	def 		_lowercase    (			self    :     List[str]			)			->	Optional[int]:
					lowercase_	    =   load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/cycle-diffusion/black_colored_car.png'''			)
					lowercase_	    =   load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
					lowercase_	    =   init_image.resize((5_1_2, 5_1_2)			)

					lowercase_	    =   '''CompVis/stable-diffusion-v1-4'''
					lowercase_	    =   DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE			,				subfolder='''scheduler'''			)
					lowercase_	    =   CycleDiffusionPipeline.from_pretrained(
					    __SCREAMING_SNAKE_CASE			,				scheduler=__SCREAMING_SNAKE_CASE			,				safety_checker=__SCREAMING_SNAKE_CASE			,				torch_dtype=torch.floataa			,				revision='''fp16'''			)

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

					lowercase_	    =   '''A black colored car'''
					lowercase_	    =   '''A blue colored car'''

					lowercase_	    =   torch.manual_seed(0			)
					lowercase_	    =   pipe(
					    prompt=__SCREAMING_SNAKE_CASE			,				source_prompt=__SCREAMING_SNAKE_CASE			,				image=__SCREAMING_SNAKE_CASE			,				num_inference_steps=1_0_0			,				eta=0.1			,				strength=0.85			,				guidance_scale=3			,				source_guidance_scale=1			,				generator=__SCREAMING_SNAKE_CASE			,				output_type='''np'''			,				)
					lowercase_	    =   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 		_lowercase    (			self    :     int			)			->	Any:
					lowercase_	    =   load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/cycle-diffusion/black_colored_car.png'''			)
					lowercase_	    =   load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
					lowercase_	    =   init_image.resize((5_1_2, 5_1_2)			)

					lowercase_	    =   '''CompVis/stable-diffusion-v1-4'''
					lowercase_	    =   DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE			,				subfolder='''scheduler'''			)
					lowercase_	    =   CycleDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE			,				scheduler=__SCREAMING_SNAKE_CASE			,				safety_checker=__SCREAMING_SNAKE_CASE			)

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

					lowercase_	    =   '''A black colored car'''
					lowercase_	    =   '''A blue colored car'''

					lowercase_	    =   torch.manual_seed(0			)
					lowercase_	    =   pipe(
					    prompt=__SCREAMING_SNAKE_CASE			,				source_prompt=__SCREAMING_SNAKE_CASE			,				image=__SCREAMING_SNAKE_CASE			,				num_inference_steps=1_0_0			,				eta=0.1			,				strength=0.85			,				guidance_scale=3			,				source_guidance_scale=1			,				generator=__SCREAMING_SNAKE_CASE			,				output_type='''np'''			,				)
					lowercase_	    =   output.images

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










'''simple docstring'''



import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version







def       _UpperCamelCase			(*_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[Union[Dict, Any]] = None ,						_lowerCamelCase				:  List[Any]=True ,						_lowerCamelCase				:  str=2						)-> str:




     '''simple docstring'''





     from .. import __version__

     __snake_case            =	take_from
     __snake_case            =	()
     if not isinstance(args[0] ,						_lowerCamelCase						):
          __snake_case            =	(args,)

     for attribute, version_name, message in args:
          if version.parse(version.parse(_lowerCamelCase						).base_version						) >= version.parse(_lowerCamelCase						):
               raise ValueError(
                   f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
                   f''' version {__version__} is >= {version_name}'''						)

          __snake_case            =	None
          if isinstance(_lowerCamelCase ,						_lowerCamelCase						) and attribute in deprecated_kwargs:
               values += (deprecated_kwargs.pop(_lowerCamelCase						),)
               __snake_case            =	f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
          elif hasattr(_lowerCamelCase ,						_lowerCamelCase						):
               values += (getattr(_lowerCamelCase ,						_lowerCamelCase						),)
               __snake_case            =	f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
          elif deprecated_kwargs is None:
               __snake_case            =	f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

          if warning is not None:
               __snake_case            =	warning + ''' ''' if standard_warn else ''''''
               warnings.warn(warning + message ,						_lowerCamelCase ,						stacklevel=_lowerCamelCase						)

     if isinstance(_lowerCamelCase ,						_lowerCamelCase						) and len(_lowerCamelCase						) > 0:
          __snake_case            =	inspect.getouterframes(inspect.currentframe()						)[1]
          __snake_case            =	call_frame.filename
          __snake_case            =	call_frame.lineno
          __snake_case            =	call_frame.function
          __snake_case       ,     __snake_case            =	next(iter(deprecated_kwargs.items()						)						)
          raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''						)

     if len(_lowerCamelCase						) == 0:
          return
     elif len(_lowerCamelCase						) == 1:
          return values[0]
     return values






