






'''simple docstring'''

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()






class 							__magic_name__     (	UpperCAmelCase_,						UpperCAmelCase_,						UpperCAmelCase_,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Any        =  StableUnCLIPPipeline
	SCREAMING_SNAKE_CASE_					:      int        =  TEXT_TO_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  TEXT_TO_IMAGE_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_					:      List[Any]        =  TEXT_TO_IMAGE_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  TEXT_TO_IMAGE_IMAGE_PARAMS

	# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
	SCREAMING_SNAKE_CASE_					:      List[str]        =  False
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   Optional[Any]        =			32
							lowercase_      :   Any        =			embedder_hidden_size

							# prior components

							torch.manual_seed(0       )
							lowercase_      :   Dict        =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

							torch.manual_seed(0       )
							lowercase_      :   List[str]        =			CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=_lowercase  ,      projection_dim=_lowercase  ,      intermediate_size=37  ,      layer_norm_eps=1E-0_5  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1000  ,      )       )

							torch.manual_seed(0       )
							lowercase_      :   Union[str, Any]        =			PriorTransformer(
							    num_attention_heads=2  ,      attention_head_dim=12  ,      embedding_dim=_lowercase  ,      num_layers=1  ,      )

							torch.manual_seed(0       )
							lowercase_      :   Optional[int]        =			DDPMScheduler(
							    variance_type='fixed_small_log'  ,      prediction_type='sample'  ,      num_train_timesteps=1000  ,      clip_sample=_lowercase  ,      clip_sample_range=5.0  ,      beta_schedule='squaredcos_cap_v2'  ,      )

							# regular denoising components

							torch.manual_seed(0       )
							lowercase_      :   Tuple        =			StableUnCLIPImageNormalizer(embedding_dim=_lowercase       )
							lowercase_      :   Dict        =			DDPMScheduler(beta_schedule='squaredcos_cap_v2'       )

							torch.manual_seed(0       )
							lowercase_      :   Union[str, Any]        =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

							torch.manual_seed(0       )
							lowercase_      :   Union[str, Any]        =			CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=_lowercase  ,      projection_dim=32  ,      intermediate_size=37  ,      layer_norm_eps=1E-0_5  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1000  ,      )       )

							torch.manual_seed(0       )
							lowercase_      :   Optional[Any]        =			UNetaDConditionModel(
							    sample_size=32  ,      in_channels=4  ,      out_channels=4  ,      down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')  ,      up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')  ,      block_out_channels=(32, 64)  ,      attention_head_dim=(2, 4)  ,      class_embed_type='projection'  ,      projection_class_embeddings_input_dim=embedder_projection_dim * 2  ,      cross_attention_dim=_lowercase  ,      layers_per_block=1  ,      upcast_attention=_lowercase  ,      use_linear_projection=_lowercase  ,      )

							torch.manual_seed(0       )
							lowercase_      :   Optional[int]        =			DDIMScheduler(
							    beta_schedule='scaled_linear'  ,      beta_start=0.0_00_85  ,      beta_end=0.0_12  ,      prediction_type='v_prediction'  ,      set_alpha_to_one=_lowercase  ,      steps_offset=1  ,      )

							torch.manual_seed(0       )
							lowercase_      :   str        =			AutoencoderKL()

							lowercase_      :   Optional[Any]        =			{
							    # prior components
							    'prior_tokenizer': prior_tokenizer,
							    'prior_text_encoder': prior_text_encoder,
							    'prior': prior,
							    'prior_scheduler': prior_scheduler,
							    # image noising components
							    'image_normalizer': image_normalizer,
							    'image_noising_scheduler': image_noising_scheduler,
							    # regular denoising components
							    'tokenizer': tokenizer,
							    'text_encoder': text_encoder,
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							}

							return components
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=0       )						->  List[str]:
							if str(_lowercase       ).startswith('mps'       ):
													lowercase_      :   Tuple        =			torch.manual_seed(_lowercase       )
							else:
													lowercase_      :   List[str]        =			torch.Generator(device=_lowercase       ).manual_seed(_lowercase       )
							lowercase_      :   Tuple        =			{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'prior_num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_      :   Any        =			torch_device == 'cpu'

							self._test_attention_slicing_forward_pass(test_max_difference=_lowercase       )







	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   Tuple        =			torch_device in ['cpu', 'mps']

							self._test_inference_batch_single_identical(test_max_difference=_lowercase       )






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







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   Any        =			load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'       )

							lowercase_      :   Optional[Any]        =			StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'  ,      torch_dtype=torch.floataa       )
							pipe.to(_lowercase       )
							pipe.set_progress_bar_config(disable=_lowercase       )
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowercase_      :   List[str]        =			torch.Generator(device='cpu'       ).manual_seed(0       )
							lowercase_      :   Optional[int]        =			pipe('anime turle'  ,      generator=_lowercase  ,      output_type='np'       )

							lowercase_      :   List[str]        =			output.images[0]

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

							assert_mean_pixel_difference(_lowercase  ,      _lowercase       )







	def        lowerCamelCase__		(							self       )						->  int:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowercase_      :   List[Any]        =			StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'  ,      torch_dtype=torch.floataa       )
							lowercase_      :   List[Any]        =			pipe.to(_lowercase       )
							pipe.set_progress_bar_config(disable=_lowercase       )
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowercase_      :   Optional[Any]        =			pipe(
							    'anime turtle'  ,      prior_num_inference_steps=2  ,      num_inference_steps=2  ,      output_type='np'  ,      )

							lowercase_      :   Optional[int]        =			torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9








'''simple docstring'''

def 	_UpperCAmelCase    (  a		:	str							)			->			str:





						"""simple docstring"""
						lowercase_      :   Dict        =			0

						# if input_string is "aba" than new_input_string become "a|b|a"
						lowercase_      :   Dict        =			''
						lowercase_      :   Any        =			''

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(a							) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						lowercase_  ,      lowercase_      :   Dict        =			0, 0

						# length[i] shows the length of palindromic substring with center i
						lowercase_      :   List[Any]        =			[1 for i in range(len(a							)							)]

						# for each character in new_string find corresponding palindromic string
						lowercase_      :   Dict        =			0
						for j in range(len(a							)							):
												lowercase_      :   Tuple        =			1 if j > r else min(length[l + r - j] // 2						,			r - j + 1							)
												while (
												    j - k >= 0
												    and j + k < len(a							)
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												lowercase_      :   int        =			2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		lowercase_      :   Tuple        =			j - k + 1  # noqa: E741
																		lowercase_      :   Tuple        =			j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		lowercase_      :   Tuple        =			length[j]
																		lowercase_      :   List[Any]        =			j

    # create that string
						lowercase_      :   str        =			new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


if __name__ == "__main__":
						import doctest

						doctest.testmod()
