






import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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      UpperCamelCase (				snake_case__			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   KandinskyImgaImgPipeline
		snake_case    =   ["prompt", "image_embeds", "negative_image_embeds", "image"]
		snake_case    =   [
		    "prompt",
		    "negative_prompt",
		    "image_embeds",
		    "negative_image_embeds",
		    "image",
		]
		snake_case    =   [
		    "generator",
		    "height",
		    "width",
		    "strength",
		    "guidance_scale",
		    "negative_prompt",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		snake_case    =   False
		@property
		def       A(  self		:       int		)    ->   Tuple:


						'''simple docstring'''
						return 3_2
		@property
		def       A(  self		:       List[Any]		)    ->   Any:


						'''simple docstring'''
						return 3_2
		@property
		def       A(  self		:       int		)    ->   List[str]:


						'''simple docstring'''
						return self.time_input_dim
		@property
		def       A(  self		:       Any		)    ->   Union[str, Any]:


						'''simple docstring'''
						return self.time_input_dim * 4
		@property
		def       A(  self		:       str		)    ->   Union[str, Any]:


						'''simple docstring'''
						return 1_0_0
		@property
		def       A(  self		:       Any		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'		)
						return tokenizer
		@property
		def       A(  self		:       str		)    ->   str:


						'''simple docstring'''
						torch.manual_seed(0		)
						A	 =      MCLIPConfig(
						    numDims=self.cross_attention_dim						,transformerDimensions=self.text_embedder_hidden_size						,hidden_size=self.text_embedder_hidden_size						,intermediate_size=3_7						,num_attention_heads=4						,num_hidden_layers=5						,vocab_size=1_0_0_5						,)

						A	 =      MultilingualCLIP(_SCREAMING_SNAKE_CASE		)
						A	 =      text_encoder.eval()

						return text_encoder
		@property
		def       A(  self		:       Tuple		)    ->   List[Any]:


						'''simple docstring'''
						torch.manual_seed(0		)

						A	 =      {
						    'in_channels': 4,
						    # 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,
						}

						A	 =      UNetaDConditionModel(**_SCREAMING_SNAKE_CASE		)
						return model
		@property
		def       A(  self		:       Optional[Any]		)    ->   Any:


						'''simple docstring'''
						return {
						    "block_out_channels": [3_2, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}
		@property
		def       A(  self		:       Any		)    ->   Optional[int]:


						'''simple docstring'''
						torch.manual_seed(0		)
						A	 =      VQModel(**self.dummy_movq_kwargs		)
						return model
		def       A(  self		:       Tuple		)    ->   int:


						'''simple docstring'''
						A	 =      self.dummy_text_encoder
						A	 =      self.dummy_tokenizer
						A	 =      self.dummy_unet
						A	 =      self.dummy_movq

						A	 =      {
						    'num_train_timesteps': 1_0_0_0,
						    'beta_schedule': 'linear',
						    'beta_start': 0.00085,
						    'beta_end': 0.012,
						    'clip_sample': False,
						    'set_alpha_to_one': False,
						    'steps_offset': 0,
						    'prediction_type': 'epsilon',
						    'thresholding': False,
						}

						A	 =      DDIMScheduler(**_SCREAMING_SNAKE_CASE		)

						A	 =      {
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}

						return components
		def       A(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int=0		)    ->   Tuple:


						'''simple docstring'''
						A	 =      floats_tensor((1, self.cross_attention_dim)						,rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
						A	 =      floats_tensor((1, self.cross_attention_dim)						,rng=random.Random(seed + 1		)		).to(_SCREAMING_SNAKE_CASE		)
						# create init_image
						A	 =      floats_tensor((1, 3, 6_4, 6_4)						,rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
						A	 =      image.cpu().permute(0						,2						,3						,1		)[0]
						A	 =      Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE		)		).convert('RGB'		).resize((2_5_6, 2_5_6)		)

						if str(_SCREAMING_SNAKE_CASE		).startswith('mps'		):
										A	 =      torch.manual_seed(_SCREAMING_SNAKE_CASE		)
						else:
										A	 =      torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
						A	 =      {
						    'prompt': 'horse',
						    'image': init_image,
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'generator': generator,
						    'height': 6_4,
						    'width': 6_4,
						    'num_inference_steps': 1_0,
						    'guidance_scale': 7.0,
						    'strength': 0.2,
						    'output_type': 'np',
						}
						return inputs







		def       A(  self		:       Tuple		)    ->   Optional[Any]:


						'''simple docstring'''
						A	 =      'cpu'

						A	 =      self.get_dummy_components()

						A	 =      self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
						A	 =      pipe.to(_SCREAMING_SNAKE_CASE		)

						pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)		)
						A	 =      output.images

						A	 =      pipe(
						    **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)						,return_dict=_SCREAMING_SNAKE_CASE						,)[0]

						A	 =      image[0, -3:, -3:, -1]
						A	 =      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)

						A	 =      np.array(
						    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]		)
						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      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



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


						'''simple docstring'''
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







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


						'''simple docstring'''
						A	 =      load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinsky/kandinsky_img2img_frog.npy'		)

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

						A	 =      KandinskyPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-1-prior'						,torch_dtype=torch.floataa		)
						pipe_prior.to(_SCREAMING_SNAKE_CASE		)

						A	 =      KandinskyImgaImgPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-1'						,torch_dtype=torch.floataa		)
						A	 =      pipeline.to(_SCREAMING_SNAKE_CASE		)

						pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

						A	 =      torch.Generator(device='cpu'		).manual_seed(0		)
						A ,		A	 =      pipe_prior(
						    _SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE						,num_inference_steps=5						,negative_prompt=''						,).to_tuple()

						A	 =      pipeline(
						    _SCREAMING_SNAKE_CASE						,image=_SCREAMING_SNAKE_CASE						,image_embeds=_SCREAMING_SNAKE_CASE						,negative_image_embeds=_SCREAMING_SNAKE_CASE						,generator=_SCREAMING_SNAKE_CASE						,num_inference_steps=1_0_0						,height=7_6_8						,width=7_6_8						,strength=0.2						,output_type='np'						,)

						A	 =      output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)













from math import factorial
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      real
						if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      [1] * rank
						else:
										A	 =      rank
		def __repr__(  self		:       Any		)    ->   Optional[Any]:


						'''simple docstring'''
						return (
						    f'{self.real}+'
						    f'{"+".join(str(_SCREAMING_SNAKE_CASE		)+"E"+str(n+1		)for n,dual in enumerate(self.duals		)		)}'
						)
		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.duals.copy()
						while cur[-1] == 0:
										cur.pop(-1		)
						return Dual(self.real						,_SCREAMING_SNAKE_CASE		)
		def __add__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										return Dual(self.real + other						,self.duals		)
						A	 =      self.duals.copy()
						A	 =      other.duals.copy()
						if len(_SCREAMING_SNAKE_CASE		) > len(_SCREAMING_SNAKE_CASE		):
										o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						elif len(_SCREAMING_SNAKE_CASE		) < len(_SCREAMING_SNAKE_CASE		):
										s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						A	 =      []
						for i in range(len(_SCREAMING_SNAKE_CASE		)		):
										new_duals.append(s_dual[i] + o_dual[i]		)
						return Dual(self.real + other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __add__
		def __sub__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[Any]:


						'''simple docstring'''
						return self + other * -1
		def __mul__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i * other		)
										return Dual(self.real * other						,_SCREAMING_SNAKE_CASE		)
						A	 =      [0] * (len(self.duals		) + len(other.duals		) + 1)
						for i, item in enumerate(self.duals		):
										for j, jtem in enumerate(other.duals		):
														new_duals[i + j + 1] += item * jtem
						for k in range(len(self.duals		)		):
										new_duals[k] += self.duals[k] * other.real
						for index in range(len(other.duals		)		):
										new_duals[index] += other.duals[index] * self.real
						return Dual(self.real * other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __mul__
		def __truediv__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   List[str]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i / other		)
										return Dual(self.real / other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError
		def __floordiv__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   Dict:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i // other		)
										return Dual(self.real // other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError







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


						'''simple docstring'''
						if n < 0 or isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										raise ValueError('power must be a positive integer'		)
						if n == 0:
										return 1
						if n == 1:
										return self
						A	 =      self
						for _ in range(n - 1		):
										x *= self
						return x







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       int   ):
				if not callable(UpperCAmelCase   ):
								raise ValueError('differentiate() requires a function as input for func'   )
				if not isinstance(UpperCAmelCase,						(float, int)   ):
								raise ValueError('differentiate() requires a float as input for position'   )
				if not isinstance(UpperCAmelCase,						UpperCAmelCase   ):
								raise ValueError('differentiate() requires an int as input for order'   )
				A	 =      Dual(UpperCAmelCase,						1   )
				A	 =      func(UpperCAmelCase   )
				if order == 0:
								return result.real
				return result.duals[order - 1] * factorial(UpperCAmelCase   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







					def      snake_case		(							UpperCAmelCase	:       int   ):
									return y**2 * y**4



					print(differentiate(f, 9, 2))





