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    lowercase_  (  lowerCAmelCase__      ,     unittest.TestCase						):



	"""simple docstring"""





	UpperCAmelCase_  :					Tuple   =				KandinskyImgaImgPipeline
	UpperCAmelCase_  :					Dict   =				["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
	UpperCAmelCase_  :					Optional[Any]   =				[
	    """prompt""",
	    """negative_prompt""",
	    """image_embeds""",
	    """negative_image_embeds""",
	    """image""",
	]
	UpperCAmelCase_  :					Union[str, Any]   =				[
	    """generator""",
	    """height""",
	    """width""",
	    """strength""",
	    """guidance_scale""",
	    """negative_prompt""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	UpperCAmelCase_  :					Optional[int]   =				False


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								return 32


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Optional[Any]:
								return 32


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Dict:
								return self.time_input_dim


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


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Optional[int]:
								return 100


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->int:
								lowerCAmelCase			=		XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''				)
								return tokenizer


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->int:
								torch.manual_seed(0				)
								lowerCAmelCase			=		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		,			)

								lowerCAmelCase			=		MultilingualCLIP(lowercase_				)
								lowerCAmelCase			=		text_encoder.eval()

								return text_encoder


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Dict:
								torch.manual_seed(0				)

								lowerCAmelCase			=		{
								    '''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,
								}

								lowerCAmelCase			=		UNetaDConditionModel(**lowercase_				)
								return model


	@property
	def 			SCREAMING_SNAKE_CASE_				(							self				)						->str:
								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 			SCREAMING_SNAKE_CASE_				(							self				)						->List[str]:
								torch.manual_seed(0				)
								lowerCAmelCase			=		VQModel(**self.dummy_movq_kwargs				)
								return model


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								lowerCAmelCase			=		self.dummy_text_encoder
								lowerCAmelCase			=		self.dummy_tokenizer
								lowerCAmelCase			=		self.dummy_unet
								lowerCAmelCase			=		self.dummy_movq

								lowerCAmelCase			=		{
								    '''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			=		DDIMScheduler(**lowercase_				)

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

								return components


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE=0				)						->Optional[Any]:
								lowerCAmelCase			=		floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(lowercase_				)				).to(lowercase_				)
								lowerCAmelCase			=		floats_tensor((1, self.cross_attention_dim)		,			rng=random.Random(seed + 1				)				).to(lowercase_				)
								# create init_image
								lowerCAmelCase			=		floats_tensor((1, 3, 64, 64)		,			rng=random.Random(lowercase_				)				).to(lowercase_				)
								lowerCAmelCase			=		image.cpu().permute(0		,			2		,			3		,			1				)[0]
								lowerCAmelCase			=		Image.fromarray(np.uinta(lowercase_				)				).convert('''RGB'''				).resize((256, 256)				)

								if str(lowercase_				).startswith('''mps'''				):
															lowerCAmelCase			=		torch.manual_seed(lowercase_				)
								else:
															lowerCAmelCase			=		torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
								lowerCAmelCase			=		{
								    '''prompt''': '''horse''',
								    '''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 			SCREAMING_SNAKE_CASE_				(							self				)						->int:
								lowerCAmelCase			=		'''cpu'''

								lowerCAmelCase			=		self.get_dummy_components()

								lowerCAmelCase			=		self.pipeline_class(**lowercase_				)
								lowerCAmelCase			=		pipe.to(lowercase_				)

								pipe.set_progress_bar_config(disable=lowercase_				)

								lowerCAmelCase			=		pipe(**self.get_dummy_inputs(lowercase_				)				)
								lowerCAmelCase			=		output.images

								lowerCAmelCase			=		pipe(
								    **self.get_dummy_inputs(lowercase_				)		,			return_dict=lowercase_		,			)[0]

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

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

								lowerCAmelCase			=		np.array(
								    [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_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 			SCREAMING_SNAKE_CASE_				(							self				)						->Optional[int]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Tuple:
								lowerCAmelCase			=		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinsky/kandinsky_img2img_frog.npy'''				)

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

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

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

								pipeline.set_progress_bar_config(disable=lowercase_				)

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

								lowerCAmelCase			=		pipeline(
								    lowercase_		,			image=lowercase_		,			image_embeds=lowercase_		,			negative_image_embeds=lowercase_		,			generator=lowercase_		,			num_inference_steps=100		,			height=768		,			width=768		,			strength=0.2		,			output_type='''np'''		,			)

								lowerCAmelCase			=		output.images[0]

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

								assert_mean_pixel_difference(lowercase_		,			lowercase_				)








"""simple docstring"""

from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def __lowercase			(							_a		,			_a		,			_a = "x"		,			_a = 10**-10		,			_a = 1		,			):
    snake_case_ :     Any				=	symbols(_a    )
    snake_case_ :     int				=	lambdify(_a		,			_a    )
    snake_case_ :     Optional[Any]				=	lambdify(_a		,			diff(_a		,			_a    )    )

    snake_case_ :     Optional[Any]				=	starting_point

    while True:
        if diff_function(_a    ) != 0:
            snake_case_ :     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

        snake_case_ :     int				=	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)}')





