



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 	a__     (				_lowercase,			unittest.TestCase	):
	__magic_name__		:    Any	    =     KandinskyInpaintPipeline
	__magic_name__		:    List[Any]	    =     ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	__magic_name__		:    int	    =     [
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	__magic_name__		:    List[str]	    =     [
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	__magic_name__		:    Optional[int]	    =     False


	@property
	def 				lowercase__  (self : Tuple      )     ->			Dict:

					"""simple docstring"""

					return 32


	@property
	def 				lowercase__  (self : str      )     ->			Dict:

					"""simple docstring"""

					return 32


	@property
	def 				lowercase__  (self : str      )     ->			Dict:

					"""simple docstring"""

					return self.time_input_dim


	@property
	def 				lowercase__  (self : int      )     ->			Dict:

					"""simple docstring"""

					return self.time_input_dim * 4


	@property
	def 				lowercase__  (self : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					return 100


	@property
	def 				lowercase__  (self : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
					return tokenizer


	@property
	def 				lowercase__  (self : int      )     ->			List[Any]:

					"""simple docstring"""

					torch.manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= MCLIPConfig(
					    numDims=self.cross_attention_dim,					transformerDimensions=self.text_embedder_hidden_size,					hidden_size=self.text_embedder_hidden_size,					intermediate_size=37,					num_attention_heads=4,					num_hidden_layers=5,					vocab_size=1005,					)

					SCREAMING_SNAKE_CASE  :						Tuple      					= MultilingualCLIP(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= text_encoder.eval()

					return text_encoder


	@property
	def 				lowercase__  (self : Optional[int]      )     ->			Tuple:

					"""simple docstring"""

					torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						Dict      					= {
					    '''in_channels''': 9,
					    # Out channels is double in channels because predicts mean and variance
					    '''out_channels''': 8,
					    '''addition_embed_type''': '''text_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''': '''text_image_proj''',
					    '''cross_attention_dim''': self.cross_attention_dim,
					    '''attention_head_dim''': 4,
					    '''resnet_time_scale_shift''': '''scale_shift''',
					    '''class_embed_type''': None,
					}

					SCREAMING_SNAKE_CASE  :						str      					= UNetaDConditionModel(**__UpperCAmelCase      )
					return model


	@property
	def 				lowercase__  (self : Dict      )     ->			Any:

					"""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 				lowercase__  (self : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					torch.manual_seed(0      )
					SCREAMING_SNAKE_CASE  :						Any      					= VQModel(**self.dummy_movq_kwargs      )
					return model


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.dummy_text_encoder
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.dummy_tokenizer
					SCREAMING_SNAKE_CASE  :						str      					= self.dummy_unet
					SCREAMING_SNAKE_CASE  :						int      					= self.dummy_movq

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= DDIMScheduler(
					    num_train_timesteps=1000,					beta_schedule='''linear''',					beta_start=0.0_0085,					beta_end=0.012,					clip_sample=__UpperCAmelCase,					set_alpha_to_one=__UpperCAmelCase,					steps_offset=1,					prediction_type='''epsilon''',					thresholding=__UpperCAmelCase,					)

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= {
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''movq''': movq,
					}

					return components


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict=0      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= floats_tensor((1, self.cross_attention_dim),					rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= floats_tensor((1, self.cross_attention_dim),					rng=random.Random(seed + 1      )      ).to(__UpperCAmelCase      )
					# create init_image
					SCREAMING_SNAKE_CASE  :						Tuple      					= floats_tensor((1, 3, 64, 64),					rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= image.cpu().permute(0,					2,					3,					1      )[0]
					SCREAMING_SNAKE_CASE  :						Any      					= Image.fromarray(np.uinta(__UpperCAmelCase      )      ).convert('''RGB'''      ).resize((256, 256)      )
					# create mask
					SCREAMING_SNAKE_CASE  :						List[str]      					= np.ones((64, 64),					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= 0

					if str(__UpperCAmelCase      ).startswith('''mps'''      ):
									SCREAMING_SNAKE_CASE  :						Any      					= torch.manual_seed(__UpperCAmelCase      )
					else:
									SCREAMING_SNAKE_CASE  :						Dict      					= torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						str      					= {
					    '''prompt''': '''horse''',
					    '''image''': init_image,
					    '''mask_image''': mask,
					    '''image_embeds''': image_embeds,
					    '''negative_image_embeds''': negative_image_embeds,
					    '''generator''': generator,
					    '''height''': 64,
					    '''width''': 64,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 4.0,
					    '''output_type''': '''np''',
					}
					return inputs


	def 				lowercase__  (self : Tuple      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= '''cpu'''

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_dummy_components()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pipeline_class(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pipe.to(__UpperCAmelCase      )

					pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= pipe(**self.get_dummy_inputs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Dict      					= output.images

					SCREAMING_SNAKE_CASE  :						str      					= pipe(
					    **self.get_dummy_inputs(__UpperCAmelCase      ),					return_dict=__UpperCAmelCase,					)[0]

					SCREAMING_SNAKE_CASE  :						Dict      					= image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE  :						str      					= image_from_tuple[0, -3:, -3:, -1]

					print(F'''image.shape {image.shape}'''      )

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

					SCREAMING_SNAKE_CASE  :						Tuple      					= np.array(
					    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]      )

					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()}'''






	def 				lowercase__  (self : Any      )     ->			Any:

					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=3e-3      )



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


	def 				lowercase__  (self : Any      )     ->			Tuple:

					"""simple docstring"""

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






	def 				lowercase__  (self : str      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= np.ones((768, 768),					dtype=np.floataa      )
					SCREAMING_SNAKE_CASE  :						int      					= 0

					SCREAMING_SNAKE_CASE  :						Dict      					= '''a hat'''

					SCREAMING_SNAKE_CASE  :						List[Any]      					= KandinskyPriorPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-1-prior''',					torch_dtype=torch.floataa      )
					pipe_prior.to(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= KandinskyInpaintPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-1-inpaint''',					torch_dtype=torch.floataa      )
					SCREAMING_SNAKE_CASE  :						Dict      					= pipeline.to(__UpperCAmelCase      )
					pipeline.set_progress_bar_config(disable=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.Generator(device='''cpu'''      ).manual_seed(0      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= pipe_prior(
					    __UpperCAmelCase,					generator=__UpperCAmelCase,					num_inference_steps=5,					negative_prompt='''''',					).to_tuple()

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pipeline(
					    __UpperCAmelCase,					image=__UpperCAmelCase,					mask_image=__UpperCAmelCase,					image_embeds=__UpperCAmelCase,					negative_image_embeds=__UpperCAmelCase,					generator=__UpperCAmelCase,					num_inference_steps=100,					height=768,					width=768,					output_type='''np''',					)

					SCREAMING_SNAKE_CASE  :						Tuple      					= output.images[0]

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

					assert_mean_pixel_difference(__UpperCAmelCase,					__UpperCAmelCase      )









'''simple docstring'''

import unittest

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				return x + 2




class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Dict      					= {}
					SCREAMING_SNAKE_CASE  :						Any      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )

					SCREAMING_SNAKE_CASE  :						str      					= '''x = y'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''y''': 5}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 5, '''y''': 5}      )


	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= '''y = add_two(x)'''
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= 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:
									SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result is None
					assert "tried to execute add_two" in out.out


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 3'''
					SCREAMING_SNAKE_CASE  :						Any      					= {}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3}      )


	def 				lowercase__  (self : Optional[int]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[Any]      					= 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 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''x = 3\ny = 5'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 5}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''text = f\'This is x: {x}.\''''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Dict      					= 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 				lowercase__  (self : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					# evaluate returns the value of the last assignment.
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 2}      )

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


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''test_list = [x, add_two(x)]'''
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Tuple      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					self.assertListEqual(__UpperCAmelCase,					[3, 5]      )
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )


	def 				lowercase__  (self : int      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= '''y = x'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						str      					= evaluate(__UpperCAmelCase,					{},					state=__UpperCAmelCase      )
					assert result == 3
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''y''': 3}      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_list = [x, add_two(x)]\ntest_list[1]'''
					SCREAMING_SNAKE_CASE  :						int      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_list''': [3, 5]}      )

					SCREAMING_SNAKE_CASE  :						Dict      					= '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''x''': 3}
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= evaluate(__UpperCAmelCase,					{'''add_two''': add_two},					state=__UpperCAmelCase      )
					assert result == 5
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}      )






	def 				lowercase__  (self : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= '''x = 0\nfor i in range(3):\n    x = i'''
					SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
					SCREAMING_SNAKE_CASE  :						List[str]      					= evaluate(__UpperCAmelCase,					{'''range''': range},					state=__UpperCAmelCase      )
					assert result == 2
					self.assertDictEqual(__UpperCAmelCase,					{'''x''': 2, '''i''': 2}      )




