from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__	:  str						=  logging.get_logger(__name__)

lowerCAmelCase__	:  List[str]						=  {
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}





class 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  """regnet"""
					__lowerCamelCase			   =  ["""x""", """y"""]




					def __init__(  self       ,			__UpperCamelCase=3       ,			__UpperCamelCase=32       ,			__UpperCamelCase=[128, 192, 512, 1088]       ,			__UpperCamelCase=[2, 6, 12, 2]       ,			__UpperCamelCase=64       ,			__UpperCamelCase="y"       ,			__UpperCamelCase="relu"       ,			**__UpperCamelCase       ,			)	->    Dict:



									'''simple docstring'''







									super().__init__(**__UpperCamelCase						)
									if layer_type not in self.layer_types:
													raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types						)}"""						)
									snake_case__   :      Any   				= num_channels
									snake_case__   :      str   				= embedding_size
									snake_case__   :      Any   				= hidden_sizes
									snake_case__   :      str   				= depths
									snake_case__   :      List[Any]   				= groups_width
									snake_case__   :      int   				= layer_type
									snake_case__   :      Dict   				= hidden_act
									# always downsample in the first stage
									snake_case__   :      Any   				= True



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCAmelCase__	:  Union[str, Any]						=  False





@nightly
@require_torch_gpu
class 					__snake_case    (     unittest.TestCase      ):




					def      __a     (  self						)	->    str:



									'''simple docstring'''







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




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



									'''simple docstring'''







									snake_case__   :      str   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

									snake_case__   :      Dict   				= torch.manual_seed(0						)
									snake_case__   :      Tuple   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(__UpperCamelCase						)
													snake_case__   :      List[Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase						)

									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= generator.manual_seed(0						)
									snake_case__   :      List[str]   				= pipe(
									    prompt='first prompt'       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=2       ,			output_type='numpy'       ,			).images

									assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'						)
									pipe.remove_unused_weights()
									pipe.to(__UpperCamelCase						)
									pipe.set_progress_bar_config(disable=__UpperCamelCase						)

									snake_case__   :      str   				= 'cyberpunk 2077'
									snake_case__   :      Dict   				= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
									snake_case__   :      Union[str, Any]   				= torch.manual_seed(0						)
									snake_case__   :      Union[str, Any]   				= pipe(
									    prompt=__UpperCamelCase       ,			image=__UpperCamelCase       ,			text_to_image_strength=0.7_5       ,			generator=__UpperCamelCase       ,			guidance_scale=7.5       ,			num_inference_steps=50       ,			output_type='numpy'       ,			).images

									snake_case__   :      int   				= image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									snake_case__   :      List[str]   				= np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]						)

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


