

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   List[Any]     					=				KandinskyVaaControlnetImgaImgPipeline
	_lowercase		:   List[Any]     					=				['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
	_lowercase		:   int     					=				['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
	_lowercase		:   List[str]     					=				[
	    '''generator''',
	    '''height''',
	    '''width''',
	    '''strength''',
	    '''guidance_scale''',
	    '''num_inference_steps''',
	    '''return_dict''',
	    '''guidance_scale''',
	    '''num_images_per_prompt''',
	    '''output_type''',
	    '''return_dict''',
	]
	_lowercase		:   int     					=				False




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return 32




	@property
	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		return 32




	@property
	def        lowerCAmelCase_       (     self						:     str							):
		return self.time_input_dim




	@property
	def        lowerCAmelCase_       (     self						:     Any							):
		return self.time_input_dim * 4




	@property
	def        lowerCAmelCase_       (     self						:     Tuple							):
		return 100




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		torch.manual_seed(0							)

		__A :			List[Any]		    =			{
		    """in_channels""": 8,
		    # Out channels is double in channels because predicts mean and variance
		    """out_channels""": 8,
		    """addition_embed_type""": """image_hint""",
		    """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,
		}

		__A :			Any		    =			UNetaDConditionModel(**__A							)
		return model




	@property
	def        lowerCAmelCase_       (     self						:     str							):
		return {
		    "block_out_channels": [32, 32, 64, 64],
		    "down_block_types": [
		        "DownEncoderBlock2D",
		        "DownEncoderBlock2D",
		        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
		    "vq_embed_dim": 4,
		}




	@property
	def        lowerCAmelCase_       (     self						:     Dict							):
		torch.manual_seed(0							)
		__A :			Union[str, Any]		    =			VQModel(**self.dummy_movq_kwargs							)
		return model




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Any		    =			self.dummy_unet
		__A :			Any		    =			self.dummy_movq

		__A :			Optional[Any]		    =			{
		    """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,
		}

		__A :			Tuple		    =			DDIMScheduler(**__A							)

		__A :			str		    =			{
		    """unet""": unet,
		    """scheduler""": scheduler,
		    """movq""": movq,
		}

		return components




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

		if str(__A							).startswith("""mps"""							):
			__A :			str		    =			torch.manual_seed(__A							)
		else:
			__A :			List[Any]		    =			torch.Generator(device=__A							).manual_seed(__A							)
		__A :			Optional[int]		    =			{
		    """image""": init_image,
		    """image_embeds""": image_embeds,
		    """negative_image_embeds""": negative_image_embeds,
		    """hint""": hint,
		    """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						:     Any							):
		__A :			Any		    =			"""cpu"""

		__A :			str		    =			self.get_dummy_components()

		__A :			Any		    =			self.pipeline_class(**__A							)
		__A :			Union[str, Any]		    =			pipe.to(__A							)

		pipe.set_progress_bar_config(disable=__A							)

		__A :			Optional[int]		    =			pipe(**self.get_dummy_inputs(__A							)							)
		__A :			List[Any]		    =			output.images

		__A :			int		    =			pipe(
		    **self.get_dummy_inputs(__A							)					,      return_dict=__A					,      )[0]

		__A :			Optional[int]		    =			image[0, -3:, -3:, -1]
		__A :			str		    =			image_from_tuple[0, -3:, -3:, -1]

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

		__A :			str		    =			np.array(
		    [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]							)
		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      lowerCamelCase_      (	unittest.TestCase	):




	def        lowerCAmelCase_       (     self						:     Dict							):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			Any		    =			load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""							)

		__A :			List[Any]		    =			load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
		__A :			List[str]		    =			init_image.resize((512, 512)							)

		__A :			Union[str, Any]		    =			load_image(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/kandinskyv22/hint_image_cat.png"""							)
		__A :			Optional[int]		    =			torch.from_numpy(np.array(__A							)							).float() / 2_5_5.0
		__A :			int		    =			hint.permute(2					,      0					,      1							).unsqueeze(0							)

		__A :			List[str]		    =			"""A robot, 4k photo"""

		__A :			int		    =			KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
		    """kandinsky-community/kandinsky-2-2-prior"""					,      torch_dtype=torch.floataa							)
		pipe_prior.to(__A							)

		__A :			List[Any]		    =			KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
		    """kandinsky-community/kandinsky-2-2-controlnet-depth"""					,      torch_dtype=torch.floataa							)
		__A :			str		    =			pipeline.to(__A							)

		pipeline.set_progress_bar_config(disable=__A							)

		__A :			Optional[Any]		    =			torch.Generator(device="""cpu"""							).manual_seed(0							)

		__A    ,				__A :			Tuple		    =			pipe_prior(
		    __A					,      image=__A					,      strength=0.8_5					,      generator=__A					,      negative_prompt=""""""					,      ).to_tuple()

		__A :			Optional[Any]		    =			pipeline(
		    image=__A					,      image_embeds=__A					,      negative_image_embeds=__A					,      hint=__A					,      generator=__A					,      num_inference_steps=100					,      height=512					,      width=512					,      strength=0.5					,      output_type="""np"""					,      )

		__A :			int		    =			output.images[0]

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

		assert_mean_pixel_difference(__A					,      __A							)








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   complex			,a__    :   str = "x"			,a__    :   float = 10**-10			,a__    :   int = 1			,)					->				complex:
	__A :			Tuple		    =			symbols(a__       )
	__A :			List[str]		    =			lambdify(a__			,a__       )
	__A :			Any		    =			lambdify(a__			,diff(a__			,a__       )       )

	__A :			Dict		    =			starting_point

	while True:
		if diff_function(a__       ) != 0:
			__A :			Optional[int]		    =			prev_guess - multiplicity * func(a__       ) / diff_function(
			    a__       )
		else:
			raise ZeroDivisionError("""Could not find root"""       ) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess       ) < precision:
			return next_guess

		__A :			List[Any]		    =			next_guess


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

			# Find root of polynomial
			# Find fourth Root of 5
			print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

			# Find value of e
			print(
			    '''The root of log(y) - 1 = 0 is ''',
			    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
			)

			# Exponential Roots
			print(
			    '''The root of exp(x) - 1 = 0 is''',
			    f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
			)

			# Find root of cos(x)
			print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")





