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


_lowerCamelCase					:   Union[str, Any]							=		False


class 						lowercase    (     unittest.TestCase     ):
							pass



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




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Union[str, Any]:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					str      )					->				Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,							torch_dtype=torch.floataa      )
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

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

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe.dual_guided(
												    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      )
																	SCREAMING_SNAKE_CASE    			=       VersatileDiffusionPipeline.from_pretrained(_UpperCamelCase	,							torch_dtype=torch.floataa      )
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       generator.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe.dual_guided(
												    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 	__snake_case(						self   :					Union[str, Any]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,							torch_dtype=torch.floataa      )
												pipe.to(_UpperCamelCase      )
												pipe.set_progress_bar_config(disable=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       "cyberpunk 2077"
												SCREAMING_SNAKE_CASE    			=       load_image(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )
												SCREAMING_SNAKE_CASE    			=       pipe.dual_guided(
												    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

												SCREAMING_SNAKE_CASE    			=       image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE    			=       np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]      )

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

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

												SCREAMING_SNAKE_CASE    			=       image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE    			=       np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]      )

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

												SCREAMING_SNAKE_CASE    			=       pipe.image_variation(_UpperCamelCase	,							generator=_UpperCamelCase	,							output_type="numpy"      ).images

												SCREAMING_SNAKE_CASE    			=       image[0, 253:256, 253:256, -1]

												assert image.shape == (1, 512, 512, 3)
												SCREAMING_SNAKE_CASE    			=       np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]      )

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





# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


_lowerCamelCase					:   List[Any]							=		'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
							import torch

							print('''Torch version:''', torch.__version__)
							print('''Cuda available:''', torch.cuda.is_available())
							print('''Cuda version:''', torch.version.cuda)
							print('''CuDNN version:''', torch.backends.cudnn.version())
							print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
							print('''Torch version:''', None)

try:
							import transformers

							print('''transformers version:''', transformers.__version__)
except ImportError:
							print('''transformers version:''', None)




