



'''simple docstring'''



from timeit import timeit

lowerCAmelCase	:		Dict				=   {
    '''MALAYALAM''': True,
    '''String''': False,
    '''rotor''': True,
    '''level''': True,
    '''A''': True,
    '''BB''': True,
    '''ABC''': False,
    '''amanaplanacanalpanama''': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  len(lowerCamelCase					) - 1
	while start_i < end_i:
		if s[start_i] == s[end_i]:
			start_i += 1
			end_i -= 1
		else:
			return False
	return True



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	__lowerCAmelCase												=  len(lowerCamelCase					) // 2
	__lowerCAmelCase												=  len(lowerCamelCase					)

	# We need to traverse till half of the length of string
	# as we can get access of the i'th last element from
	# i'th index.
	# eg: [0,1,2,3,4,5] => 4th index can be accessed
	# with the help of 1st index (i==n-i-1)
	# where n is length of string
	return all(s[i] == s[n - i - 1] for i in range(lowerCamelCase					)					)



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	if len(lowerCamelCase					) <= 2:
		return True
	if s[0] == s[len(lowerCamelCase					) - 1]:
		return is_palindrome_recursive(s[1:-1]					)
	else:
		return False



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	return s == s[::-1]



def   __lowerCAmelCase					(							lowerCamelCase   :						str					):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''all({name}(key) is value for key, value in test_data.items())'''
	__lowerCAmelCase												=  f'''from __main__ import test_data, {name}'''
	__lowerCAmelCase												=  50_00_00
	__lowerCAmelCase												=  timeit(stmt=lowerCamelCase				,		setup=lowerCamelCase				,		number=lowerCamelCase					)
	print(f'''{name:<35} finished {number:,} runs in {result:.5f} seconds'''					)


if __name__ == "__main__":
	for key, value in test_data.items():
		assert is_palindrome(key) is is_palindrome_recursive(key)
		assert is_palindrome(key) is is_palindrome_slice(key)
		print(f'{key:21} {value}')
	print('''a man a plan a canal panama''')

	# finished 500,000 runs in 0.46793 seconds
	benchmark_function('''is_palindrome_slice''')
	# finished 500,000 runs in 0.85234 seconds
	benchmark_function('''is_palindrome''')
	# finished 500,000 runs in 1.32028 seconds
	benchmark_function('''is_palindrome_recursive''')
	# finished 500,000 runs in 2.08679 seconds
	benchmark_function('''is_palindrome_traversal''')




'''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, 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__	(		UpperCamelCase__							,       unittest.TestCase ):
					a      :     List[Any]        =							KandinskyImgaImgPipeline
					a      :     Union[str, Any]        =							["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
					a      :     List[Any]        =							[
					    """prompt""",
					    """negative_prompt""",
					    """image_embeds""",
					    """negative_image_embeds""",
					    """image""",
					]
					a      :     Any        =							[
					    """generator""",
					    """height""",
					    """width""",
					    """strength""",
					    """guidance_scale""",
					    """negative_prompt""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					a      :     Union[str, Any]        =							False



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 32



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						return 32



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



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return self.time_input_dim * 4



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 100



					@property
					def        UpperCAmelCase_ (    self     )     ->			Optional[int]:
						__lowerCAmelCase												=  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"     )
						return tokenizer



					@property
					def        UpperCAmelCase_ (    self     )     ->			Union[str, Any]:
						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(UpperCamelCase     )
						__lowerCAmelCase												=  text_encoder.eval()

						return text_encoder



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						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(**UpperCamelCase     )
						return model



					@property
					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						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        UpperCAmelCase_ (    self     )     ->			Dict:
						torch.manual_seed(0     )
						__lowerCAmelCase												=  VQModel(**self.dummy_movq_kwargs     )
						return model



					def        UpperCAmelCase_ (    self     )     ->			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_00_85,
						    "beta_end": 0.0_12,
						    "clip_sample": False,
						    "set_alpha_to_one": False,
						    "steps_offset": 0,
						    "prediction_type": "epsilon",
						    "thresholding": False,
						}

						__lowerCAmelCase												=  DDIMScheduler(**UpperCamelCase     )

						__lowerCAmelCase												=  {
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}

						return components



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

						if str(UpperCamelCase     ).startswith("mps"     ):
							__lowerCAmelCase												=  torch.manual_seed(UpperCamelCase     )
						else:
							__lowerCAmelCase												=  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )
						__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        UpperCAmelCase_ (    self     )     ->			Tuple:
						__lowerCAmelCase												=  "cpu"

						__lowerCAmelCase												=  self.get_dummy_components()

						__lowerCAmelCase												=  self.pipeline_class(**UpperCamelCase     )
						__lowerCAmelCase												=  pipe.to(UpperCamelCase     )

						pipe.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  pipe(**self.get_dummy_inputs(UpperCamelCase     )     )
						__lowerCAmelCase												=  output.images

						__lowerCAmelCase												=  pipe(
						    **self.get_dummy_inputs(UpperCamelCase     )			,				return_dict=UpperCamelCase			,				)[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(
						    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]     )
						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 ):



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



					def        UpperCAmelCase_ (    self     )     ->			List[Any]:
						__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(UpperCamelCase     )

						__lowerCAmelCase												=  KandinskyImgaImgPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-1"			,				torch_dtype=torch.floataa     )
						__lowerCAmelCase												=  pipeline.to(UpperCamelCase     )

						pipeline.set_progress_bar_config(disable=UpperCamelCase     )

						__lowerCAmelCase												=  torch.Generator(device="cpu"     ).manual_seed(0     )
						__lowerCAmelCase							,       __lowerCAmelCase												=  pipe_prior(
						    UpperCamelCase			,				generator=UpperCamelCase			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

						__lowerCAmelCase												=  pipeline(
						    UpperCamelCase			,				image=UpperCamelCase			,				image_embeds=UpperCamelCase			,				negative_image_embeds=UpperCamelCase			,				generator=UpperCamelCase			,				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(UpperCamelCase			,				UpperCamelCase     )