
"""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(       SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	unittest.TestCase      ):
		lowercase__         = CycleDiffusionPipeline
		lowercase__         = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
		    'negative_prompt',
		    'height',
		    'width',
		    'negative_prompt_embeds',
		}
		lowercase__         = PipelineTesterMixin.required_optional_params - {'latents'}
		lowercase__         = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}      )
		lowercase__         = IMAGE_TO_IMAGE_IMAGE_PARAMS
		lowercase__         = IMAGE_TO_IMAGE_IMAGE_PARAMS



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							torch.manual_seed(0)
							_UpperCamelCase									=    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							,						)
							_UpperCamelCase									=    DDIMScheduler(
							    beta_start=0.0_0085							,						beta_end=0.012							,						beta_schedule='''scaled_linear'''							,						num_train_timesteps=10_00							,						clip_sample=__a							,						set_alpha_to_one=__a							,						)
							torch.manual_seed(0)
							_UpperCamelCase									=    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)
							_UpperCamelCase									=    CLIPTextConfig(
							    bos_token_id=0							,						eos_token_id=2							,						hidden_size=32							,						intermediate_size=37							,						layer_norm_eps=1e-05							,						num_attention_heads=4							,						num_hidden_layers=5							,						pad_token_id=1							,						vocab_size=10_00							,						)
							_UpperCamelCase									=    CLIPTextModel(__a)
							_UpperCamelCase									=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

							_UpperCamelCase									=    {
							    '''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)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    floats_tensor((1, 3, 32, 32)							,						rng=random.Random(__a)).to(__a)
							_UpperCamelCase									=    image / 2 + 0.5
							if str(__a).startswith('''mps'''):
												_UpperCamelCase									=    torch.manual_seed(__a)
							else:
												_UpperCamelCase									=    torch.Generator(device=__a).manual_seed(__a)
							_UpperCamelCase									=    {
							    '''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)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

							_UpperCamelCase									=    self.get_dummy_components()
							_UpperCamelCase									=    CycleDiffusionPipeline(**__a)
							_UpperCamelCase									=    pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)

							_UpperCamelCase									=    self.get_dummy_inputs(__a)
							_UpperCamelCase									=    pipe(**__a)
							_UpperCamelCase									=    output.images

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

							assert images.shape == (1, 32, 32, 3)
							_UpperCamelCase									=    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)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_dummy_components()
							for name, module in components.items():
												if hasattr(__a							,						'''half'''):
																	_UpperCamelCase									=    module.half()
							_UpperCamelCase									=    CycleDiffusionPipeline(**__a)
							_UpperCamelCase									=    pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)

							_UpperCamelCase									=    self.get_dummy_inputs(__a)
							_UpperCamelCase									=    pipe(**__a)
							_UpperCamelCase									=    output.images

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

							assert images.shape == (1, 32, 32, 3)
							_UpperCamelCase									=    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)			->			List[Any]:






							'''simple docstring'''
							return super().test_save_load_local()



		@unittest.skip('''non-deterministic pipeline''')
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							return super().test_inference_batch_single_identical()



		@skip_mps
		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							return super().test_dict_tuple_outputs_equivalent()



		@skip_mps
		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							return super().test_save_load_optional_components()




		@skip_mps
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							return super().test_attention_slicing_forward_pass()






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



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/cycle-diffusion/black_colored_car.png''')
							_UpperCamelCase									=    load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''')
							_UpperCamelCase									=    init_image.resize((5_12, 5_12))

							_UpperCamelCase									=    '''CompVis/stable-diffusion-v1-4'''
							_UpperCamelCase									=    DDIMScheduler.from_pretrained(__a							,						subfolder='''scheduler''')
							_UpperCamelCase									=    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()

							_UpperCamelCase									=    '''A black colored car'''
							_UpperCamelCase									=    '''A blue colored car'''

							_UpperCamelCase									=    torch.manual_seed(0)
							_UpperCamelCase									=    pipe(
							    prompt=__a							,						source_prompt=__a							,						image=__a							,						num_inference_steps=1_00							,						eta=0.1							,						strength=0.85							,						guidance_scale=3							,						source_guidance_scale=1							,						generator=__a							,						output_type='''np'''							,						)
							_UpperCamelCase									=    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)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/cycle-diffusion/black_colored_car.png''')
							_UpperCamelCase									=    load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''')
							_UpperCamelCase									=    init_image.resize((5_12, 5_12))

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

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

							_UpperCamelCase									=    '''A black colored car'''
							_UpperCamelCase									=    '''A blue colored car'''

							_UpperCamelCase									=    torch.manual_seed(0)
							_UpperCamelCase									=    pipe(
							    prompt=__a							,						source_prompt=__a							,						image=__a							,						num_inference_steps=1_00							,						eta=0.1							,						strength=0.85							,						guidance_scale=3							,						source_guidance_scale=1							,						generator=__a							,						output_type='''np'''							,						)
							_UpperCamelCase									=    output.images

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












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :     str		    =       {
    "google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
    "google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
    "google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
    "google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= '''mobilenet_v2'''





	def __init__(							self      ,     SCREAMING_SNAKE_CASE=3      ,     SCREAMING_SNAKE_CASE=224      ,     SCREAMING_SNAKE_CASE=1.0      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=8      ,     SCREAMING_SNAKE_CASE=6      ,     SCREAMING_SNAKE_CASE=32      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE="relu6"      ,     SCREAMING_SNAKE_CASE=True      ,     SCREAMING_SNAKE_CASE=0.8      ,     SCREAMING_SNAKE_CASE=0.0_2      ,     SCREAMING_SNAKE_CASE=0.0_0_1      ,     SCREAMING_SNAKE_CASE=255      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					Dict:




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )

				if depth_multiplier <= 0:
							raise ValueError('depth_multiplier must be greater than zero.'      )

				SCREAMING_SNAKE_CASE_								=						num_channels
				SCREAMING_SNAKE_CASE_								=						image_size
				SCREAMING_SNAKE_CASE_								=						depth_multiplier
				SCREAMING_SNAKE_CASE_								=						depth_divisible_by
				SCREAMING_SNAKE_CASE_								=						min_depth
				SCREAMING_SNAKE_CASE_								=						expand_ratio
				SCREAMING_SNAKE_CASE_								=						output_stride
				SCREAMING_SNAKE_CASE_								=						first_layer_is_expansion
				SCREAMING_SNAKE_CASE_								=						finegrained_output
				SCREAMING_SNAKE_CASE_								=						hidden_act
				SCREAMING_SNAKE_CASE_								=						tf_padding
				SCREAMING_SNAKE_CASE_								=						classifier_dropout_prob
				SCREAMING_SNAKE_CASE_								=						initializer_range
				SCREAMING_SNAKE_CASE_								=						layer_norm_eps
				SCREAMING_SNAKE_CASE_								=						semantic_loss_ignore_index




class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= version.parse('''1.11'''     )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				return OrderedDict([('pixel_values', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "image-classification":
							return OrderedDict([('logits', {0: 'batch'})]      )
				else:
							return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )





	@property
	def 						A_(							self      )  ->					float:




				"""simple docstring"""



				return 1e-4
