



import unittest

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






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]				)       ->							int:

							'''simple docstring'''






							return x + 2



class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  int       )		->     Optional[int]:
											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 __UpperCamelCase						(							self      :  List[str]       )		->     Union[str, Any]:
											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 __UpperCamelCase						(							self      :  Optional[Any]       )		->     Dict:
											A      						=    'x = 3'
											A      						=    {}
											A      						=    evaluate(__UpperCamelCase						, {}						, state=__UpperCamelCase       )
											assert result == 3
											self.assertDictEqual(__UpperCamelCase						, {'x': 3}       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[Any]:
											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 __UpperCamelCase						(							self      :  Any       )		->     str:
											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 __UpperCamelCase						(							self      :  Optional[int]       )		->     Tuple:
											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 __UpperCamelCase						(							self      :  str       )		->     List[Any]:
											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 __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											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 __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											A      						=    'y = x'
											A      						=    {'x': 3}
											A      						=    evaluate(__UpperCamelCase						, {}						, state=__UpperCamelCase       )
											assert result == 3
											self.assertDictEqual(__UpperCamelCase						, {'x': 3, 'y': 3}       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											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 __UpperCamelCase						(							self      :  Tuple       )		->     List[Any]:
											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