


import unittest

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

def    lowercase_	(	_A    :					str		):

      """simple docstring"""




      return x + 2







class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						"x = 3"
             lowerCamelCase__	:       Optional[int]					=						{}
             lowerCamelCase__	:       Tuple					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             assert result == 3
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3} )

             lowerCamelCase__	:       Dict					=						"x = y"
             lowerCamelCase__	:       List[Any]					=						{"y": 5}
             lowerCamelCase__	:       List[Any]					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             # evaluate returns the value of the last assignment.
             assert result == 5
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 5, "y": 5} )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						"y = add_two(x)"
             lowerCamelCase__	:       Dict					=						{"x": 3}
             lowerCamelCase__	:       Optional[int]					=						evaluate(__lowerCamelCase      ,   {"add_two": add_two}      ,   state=__lowerCamelCase )
             assert result == 5
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "y": 5} )

             # Won't work without the tool
             with CaptureStdout() as out:
                   lowerCamelCase__	:       Union[str, Any]					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             assert result is None
             assert "tried to execute add_two" in out.out





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						"x = 3"
             lowerCamelCase__	:       Optional[Any]					=						{}
             lowerCamelCase__	:       Tuple					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             assert result == 3
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3} )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						"test_dict = {'x': x, 'y': add_two(x)}"
             lowerCamelCase__	:       List[Any]					=						{"x": 3}
             lowerCamelCase__	:       Any					=						evaluate(__lowerCamelCase      ,   {"add_two": add_two}      ,   state=__lowerCamelCase )
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "y": 5} )
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "test_dict": {"x": 3, "y": 5}} )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						"x = 3\ny = 5"
             lowerCamelCase__	:       Optional[int]					=						{}
             lowerCamelCase__	:       Any					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             # evaluate returns the value of the last assignment.
             assert result == 5
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "y": 5} )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						"text = f'This is x: {x}.'"
             lowerCamelCase__	:       Union[str, Any]					=						{"x": 3}
             lowerCamelCase__	:       Optional[int]					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             # evaluate returns the value of the last assignment.
             assert result == "This is x: 3."
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "text": "This is x: 3."} )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						"if x <= 3:\n    y = 2\nelse:\n    y = 5"
             lowerCamelCase__	:       Any					=						{"x": 3}
             lowerCamelCase__	:       Union[str, Any]					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             # evaluate returns the value of the last assignment.
             assert result == 2
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "y": 2} )

             lowerCamelCase__	:       Union[str, Any]					=						{"x": 8}
             lowerCamelCase__	:       Any					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             # evaluate returns the value of the last assignment.
             assert result == 5
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 8, "y": 5} )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						"test_list = [x, add_two(x)]"
             lowerCamelCase__	:       Dict					=						{"x": 3}
             lowerCamelCase__	:       List[str]					=						evaluate(__lowerCamelCase      ,   {"add_two": add_two}      ,   state=__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   [3, 5] )
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "test_list": [3, 5]} )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						"y = x"
             lowerCamelCase__	:       Any					=						{"x": 3}
             lowerCamelCase__	:       Optional[int]					=						evaluate(__lowerCamelCase      ,   {}      ,   state=__lowerCamelCase )
             assert result == 3
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "y": 3} )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						"test_list = [x, add_two(x)]\ntest_list[1]"
             lowerCamelCase__	:       int					=						{"x": 3}
             lowerCamelCase__	:       Union[str, Any]					=						evaluate(__lowerCamelCase      ,   {"add_two": add_two}      ,   state=__lowerCamelCase )
             assert result == 5
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "test_list": [3, 5]} )

             lowerCamelCase__	:       List[str]					=						"test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
             lowerCamelCase__	:       Optional[Any]					=						{"x": 3}
             lowerCamelCase__	:       Optional[int]					=						evaluate(__lowerCamelCase      ,   {"add_two": add_two}      ,   state=__lowerCamelCase )
             assert result == 5
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 3, "test_dict": {"x": 3, "y": 5}} )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						"x = 0\nfor i in range(3):\n    x = i"
             lowerCamelCase__	:       Dict					=						{}
             lowerCamelCase__	:       int					=						evaluate(__lowerCamelCase      ,   {"range": range}      ,   state=__lowerCamelCase )
             assert result == 2
             self.assertDictEqual(__lowerCamelCase      ,   {"x": 2, "i": 2} )






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    KandinskyVaaImgaImgPipeline
       A__      		=    ["image_embeds", "negative_image_embeds", "image"]
       A__      		=    [
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       A__      		=    [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       A__      		=    False





       @property
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return 32





       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return self.time_input_dim





       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return self.time_input_dim * 4





       @property
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             return 100





       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             torch.manual_seed(0 )

             lowerCamelCase__	:       Optional[Any]					=						{
                 "in_channels": 4,
                 # Out channels is double in channels because predicts mean and variance
                 "out_channels": 8,
                 "addition_embed_type": "image",
                 "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
                 "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
                 "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
                 "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                 "layers_per_block": 1,
                 "encoder_hid_dim": self.text_embedder_hidden_size,
                 "encoder_hid_dim_type": "image_proj",
                 "cross_attention_dim": self.cross_attention_dim,
                 "attention_head_dim": 4,
                 "resnet_time_scale_shift": "scale_shift",
                 "class_embed_type": None,
             }

             lowerCamelCase__	:       Tuple					=						UNetaDConditionModel(**__lowerCamelCase )
             return model





       @property
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             return {
                 "block_out_channels": [32, 64],
                 "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                 "in_channels": 3,
                 "latent_channels": 4,
                 "layers_per_block": 1,
                 "norm_num_groups": 8,
                 "norm_type": "spatial",
                 "num_vq_embeddings": 12,
                 "out_channels": 3,
                 "up_block_types": [
                     "AttnUpDecoderBlock2D",
                     "UpDecoderBlock2D",
                 ],
                 "vq_embed_dim": 4,
             }





       @property
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             torch.manual_seed(0 )
             lowerCamelCase__	:       int					=						VQModel(**self.dummy_movq_kwargs )
             return model





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.dummy_unet
             lowerCamelCase__	:       Optional[Any]					=						self.dummy_movq

             lowerCamelCase__	:       Optional[int]					=						{
                 "num_train_timesteps": 1000,
                 "beta_schedule": "linear",
                 "beta_start": 0.0_0_0_8_5,
                 "beta_end": 0.0_1_2,
                 "clip_sample": False,
                 "set_alpha_to_one": False,
                 "steps_offset": 0,
                 "prediction_type": "epsilon",
                 "thresholding": False,
             }

             lowerCamelCase__	:       List[Any]					=						DDIMScheduler(**__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						{
                 "unet": unet,
                 "scheduler": scheduler,
                 "movq": movq,
             }

             return components





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    int=0 ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(seed + 1 ) ).to(
                 __lowerCamelCase )
             # create init_image
             lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						image.cpu().permute(0      ,   2      ,   3      ,   1 )[0]
             lowerCamelCase__	:       Optional[int]					=						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" ).resize((256, 256) )

             if str(__lowerCamelCase ).startswith("mps" ):
                   lowerCamelCase__	:       Optional[int]					=						torch.manual_seed(__lowerCamelCase )
             else:
                   lowerCamelCase__	:       List[str]					=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						{
                 "image": init_image,
                 "image_embeds": image_embeds,
                 "negative_image_embeds": negative_image_embeds,
                 "generator": generator,
                 "height": 64,
                 "width": 64,
                 "num_inference_steps": 10,
                 "guidance_scale": 7.0,
                 "strength": 0.2,
                 "output_type": "np",
             }
             return inputs





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						"cpu"

             lowerCamelCase__	:       str					=						self.get_dummy_components()

             lowerCamelCase__	:       Optional[int]					=						self.pipeline_class(**__lowerCamelCase )
             lowerCamelCase__	:       List[str]					=						pipe.to(__lowerCamelCase )

             pipe.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
             lowerCamelCase__	:       List[str]					=						output.images

             lowerCamelCase__	:       Optional[Any]					=						pipe(
                 **self.get_dummy_inputs(__lowerCamelCase )      ,   return_dict=__lowerCamelCase      ,   )[0]

             lowerCamelCase__	:       int					=						image[0, -3:, -3:, -1]
             lowerCamelCase__	:       int					=						image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)

             lowerCamelCase__	:       str					=						np.array(
                 [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
             assert (
                 np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
             ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
             assert (
                 np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
             ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinskyv22/kandinskyv22_img2img_frog.npy" )

             lowerCamelCase__	:       Tuple					=						load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
             lowerCamelCase__	:       Any					=						"A red cartoon frog, 4k"

             lowerCamelCase__	:       str					=						KandinskyVaaPriorPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-prior"      ,   torch_dtype=torch.floataa )
             pipe_prior.to(__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						KandinskyVaaImgaImgPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-decoder"      ,   torch_dtype=torch.floataa )
             lowerCamelCase__	:       str					=						pipeline.to(__lowerCamelCase )

             pipeline.set_progress_bar_config(disable=__lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						torch.Generator(device="cpu" ).manual_seed(0 )
             lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						pipe_prior(
                 __lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=5      ,   negative_prompt=""      ,   ).to_tuple()

             lowerCamelCase__	:       Optional[Any]					=						pipeline(
                 image=__lowerCamelCase      ,   image_embeds=__lowerCamelCase      ,   negative_image_embeds=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=100      ,   height=768      ,   width=768      ,   strength=0.2      ,   output_type="np"      ,   )

             lowerCamelCase__	:       Union[str, Any]					=						output.images[0]

             assert image.shape == (768, 768, 3)

             assert_mean_pixel_difference(__lowerCamelCase      ,   __lowerCamelCase )


