
import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate


def        __lowerCAmelCase     (     a__    )			->    int:
				return x + 2







class      __A(					unittest.TestCase     ):




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


								'''simple docstring'''




								__a     				=       '''x = 3'''
								__a     				=       {}
								__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								assert result == 3
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3}							)

								__a     				=       '''x = y'''
								__a     				=       {'''y''': 5}
								__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								# evaluate returns the value of the last assignment.
								assert result == 5
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 5, '''y''': 5}							)




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


								'''simple docstring'''




								__a     				=       '''y = add_two(x)'''
								__a     				=       {'''x''': 3}
								__a     				=       evaluate(__UpperCamelCase						,	{'''add_two''': add_two}						,	state=__UpperCamelCase							)
								assert result == 5
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''y''': 5}							)

								# Won't work without the tool
								with CaptureStdout() as out:
												__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								assert result is None
								assert "tried to execute add_two" in out.out




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> Dict:


								'''simple docstring'''




								__a     				=       '''x = 3'''
								__a     				=       {}
								__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								assert result == 3
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3}							)




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


								'''simple docstring'''




								__a     				=       '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
								__a     				=       {'''x''': 3}
								__a     				=       evaluate(__UpperCamelCase						,	{'''add_two''': add_two}						,	state=__UpperCamelCase							)
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''y''': 5}							)
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> str:


								'''simple docstring'''




								__a     				=       '''x = 3\ny = 5'''
								__a     				=       {}
								__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								# evaluate returns the value of the last assignment.
								assert result == 5
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''y''': 5}							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> Tuple:


								'''simple docstring'''




								__a     				=       '''text = f\'This is x: {x}.\''''
								__a     				=       {'''x''': 3}
								__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								# evaluate returns the value of the last assignment.
								assert result == "This is x: 3."
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''text''': '''This is x: 3.'''}							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> List[Any]:


								'''simple docstring'''




								__a     				=       '''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
								__a     				=       {'''x''': 3}
								__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								# evaluate returns the value of the last assignment.
								assert result == 2
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''y''': 2}							)

								__a     				=       {'''x''': 8}
								__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								# evaluate returns the value of the last assignment.
								assert result == 5
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 8, '''y''': 5}							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> List[str]:


								'''simple docstring'''




								__a     				=       '''test_list = [x, add_two(x)]'''
								__a     				=       {'''x''': 3}
								__a     				=       evaluate(__UpperCamelCase						,	{'''add_two''': add_two}						,	state=__UpperCamelCase							)
								self.assertListEqual(__UpperCamelCase						,	[3, 5]							)
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''test_list''': [3, 5]}							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> List[str]:


								'''simple docstring'''




								__a     				=       '''y = x'''
								__a     				=       {'''x''': 3}
								__a     				=       evaluate(__UpperCamelCase						,	{}						,	state=__UpperCamelCase							)
								assert result == 3
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''y''': 3}							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> Tuple:


								'''simple docstring'''




								__a     				=       '''test_list = [x, add_two(x)]\ntest_list[1]'''
								__a     				=       {'''x''': 3}
								__a     				=       evaluate(__UpperCamelCase						,	{'''add_two''': add_two}						,	state=__UpperCamelCase							)
								assert result == 5
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''test_list''': [3, 5]}							)

								__a     				=       '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
								__a     				=       {'''x''': 3}
								__a     				=       evaluate(__UpperCamelCase						,	{'''add_two''': add_two}						,	state=__UpperCamelCase							)
								assert result == 5
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}							)




				def 							SCREAMING_SNAKE_CASE_						(	self							)       -> List[Any]:


								'''simple docstring'''




								__a     				=       '''x = 0\nfor i in range(3):\n    x = i'''
								__a     				=       {}
								__a     				=       evaluate(__UpperCamelCase						,	{'''range''': range}						,	state=__UpperCamelCase							)
								assert result == 2
								self.assertDictEqual(__UpperCamelCase						,	{'''x''': 2, '''i''': 2}							)




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


__snake_case     :str			       =False



class        lowerCAmelCase__					(  unittest.TestCase       ):
				pass





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


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


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

											A      						=    torch.manual_seed(0       )
											A      						=    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       )
																		A      						=    VersatileDiffusionPipeline.from_pretrained(__UpperCamelCase						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    generator.manual_seed(0       )
											A      						=    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 __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

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

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

											assert image.shape == (1, 512, 512, 3)
											A      						=    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

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

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

											assert image.shape == (1, 512, 512, 3)
											A      						=    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

											A      						=    pipe.image_variation(__UpperCamelCase						, generator=__UpperCamelCase						, output_type='numpy'       ).images

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

											assert image.shape == (1, 512, 512, 3)
											A      						=    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