


'''simple docstring'''






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


__UpperCAmelCase       							=				logging.get_logger(__name__)

__UpperCAmelCase       							=				{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Union[str, Any]      		= "mobilenet_v1"







							def __init__(						self   ,       lowerCamelCase_=3   ,       lowerCamelCase_=2_24   ,       lowerCamelCase_=1.0   ,       lowerCamelCase_=8   ,       lowerCamelCase_="relu6"   ,       lowerCamelCase_=True   ,       lowerCamelCase_=0.999   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=0.001   ,       **lowerCamelCase_   ,       )			->				List[str]:
														super().__init__(**lowerCamelCase_  )

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

														lowerCAmelCase__			  =      num_channels
														lowerCAmelCase__			  =      image_size
														lowerCAmelCase__			  =      depth_multiplier
														lowerCAmelCase__			  =      min_depth
														lowerCAmelCase__			  =      hidden_act
														lowerCAmelCase__			  =      tf_padding
														lowerCAmelCase__			  =      classifier_dropout_prob
														lowerCAmelCase__			  =      initializer_range
														lowerCAmelCase__			  =      layer_norm_eps




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Optional[int]      		= version.parse("1.11"    )







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Mapping[str, Mapping[int, str]]:
														return OrderedDict([('''pixel_values''', {0: '''batch'''})]  )







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Mapping[str, Mapping[int, str]]:
														if self.task == "image-classification":
																					return OrderedDict([('''logits''', {0: '''batch'''})]  )
														else:
																					return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]  )







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				float:
														return 1e-4



'''simple docstring'''






import gc
import tempfile
import unittest

import numpy as np
import torch

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


__UpperCAmelCase       							=				False




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							pass




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





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )

														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(lowerCamelCase_  )
																					lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained(lowerCamelCase_   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      generator.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       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     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''cyberpunk 2077'''
														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt=lowerCamelCase_   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''   ,       ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]  )

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

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger '''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.text_to_image(
														    prompt=lowerCamelCase_   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]  )

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

														lowerCAmelCase__			  =      pipe.image_variation(lowerCamelCase_   ,       generator=lowerCamelCase_   ,       output_type='''numpy'''  ).images

														lowerCAmelCase__			  =      image[0, 2_53:2_56, 2_53:2_56, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]  )

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