

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class       snake_case_  (						__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							UnCLIPImageVariationPipeline
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							IMAGE_VARIATION_BATCH_PARAMS

			SCREAMING_SNAKE_CASE	:  List[Any]		     =							[
			    "generator",
			    "return_dict",
			    "decoder_num_inference_steps",
			    "super_res_num_inference_steps",
			]
			SCREAMING_SNAKE_CASE	:  List[str]		     =							False







			@property
			def 							snake_case__(	self					:							Optional[Any]				) ->int:
									return 3_2







			@property
			def 							snake_case__(	self					:							List[Any]				) ->List[str]:
									return 3_2







			@property
			def 							snake_case__(	self					:							Optional[Any]				) ->int:
									return self.time_input_dim







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







			@property
			def 							snake_case__(	self					:							Any				) ->Tuple:
									return 1_0_0







			@property
			def 							snake_case__(	self					:							List[Any]				) ->int:
									snake_case_  =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
									return tokenizer







			@property
			def 							snake_case__(	self					:							List[str]				) ->List[Any]:
									torch.manual_seed(0				)
									snake_case_  =      CLIPTextConfig(
									    bos_token_id=0				,  eos_token_id=2				,  hidden_size=self.text_embedder_hidden_size				,  projection_dim=self.text_embedder_hidden_size				,  intermediate_size=3_7				,  layer_norm_eps=1e-05				,  num_attention_heads=4				,  num_hidden_layers=5				,  pad_token_id=1				,  vocab_size=1_0_0_0				,  )
									return CLIPTextModelWithProjection(_UpperCamelCase				)







			@property
			def 							snake_case__(	self					:							int				) ->Optional[Any]:
									torch.manual_seed(0				)
									snake_case_  =      CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size				,  projection_dim=self.text_embedder_hidden_size				,  num_hidden_layers=5				,  num_attention_heads=4				,  image_size=3_2				,  intermediate_size=3_7				,  patch_size=1				,  )
									return CLIPVisionModelWithProjection(_UpperCamelCase				)







			@property
			def 							snake_case__(	self					:							Optional[Any]				) ->str:
									torch.manual_seed(0				)

									snake_case_  =      {
									    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
									    '''time_embed_dim''': self.time_embed_dim,
									    '''cross_attention_dim''': self.cross_attention_dim,
									}

									snake_case_  =      UnCLIPTextProjModel(**_UpperCamelCase				)
									return model







			@property
			def 							snake_case__(	self					:							Union[str, Any]				) ->Union[str, Any]:
									torch.manual_seed(0				)

									snake_case_  =      {
									    '''sample_size''': 3_2,
									    # RGB in channels
									    '''in_channels''': 3,
									    # Out channels is double in channels because predicts mean and variance
									    '''out_channels''': 6,
									    '''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,
									    '''cross_attention_dim''': self.cross_attention_dim,
									    '''attention_head_dim''': 4,
									    '''resnet_time_scale_shift''': '''scale_shift''',
									    '''class_embed_type''': '''identity''',
									}

									snake_case_  =      UNetaDConditionModel(**_UpperCamelCase				)
									return model







			@property
			def 							snake_case__(	self					:							List[Any]				) ->Any:
									return {
									    "sample_size": 6_4,
									    "layers_per_block": 1,
									    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
									    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "in_channels": 6,
									    "out_channels": 3,
									}







			@property
			def 							snake_case__(	self					:							str				) ->Union[str, Any]:
									torch.manual_seed(0				)

									snake_case_  =      UNetaDModel(**self.dummy_super_res_kwargs				)
									return model







			@property
			def 							snake_case__(	self					:							Dict				) ->str:
									# seeded differently to get different unet than `self.dummy_super_res_first`
									torch.manual_seed(1				)

									snake_case_  =      UNetaDModel(**self.dummy_super_res_kwargs				)
									return model







			def 							snake_case__(	self					:							List[Any]				) ->Optional[int]:
									snake_case_  =      self.dummy_decoder
									snake_case_  =      self.dummy_text_proj
									snake_case_  =      self.dummy_text_encoder
									snake_case_  =      self.dummy_tokenizer
									snake_case_  =      self.dummy_super_res_first
									snake_case_  =      self.dummy_super_res_last

									snake_case_  =      UnCLIPScheduler(
									    variance_type='''learned_range'''				,  prediction_type='''epsilon'''				,  num_train_timesteps=1_0_0_0				,  )

									snake_case_  =      UnCLIPScheduler(
									    variance_type='''fixed_small_log'''				,  prediction_type='''epsilon'''				,  num_train_timesteps=1_0_0_0				,  )

									snake_case_  =      CLIPImageProcessor(crop_size=3_2				,  size=3_2				)

									snake_case_  =      self.dummy_image_encoder

									return {
									    "decoder": decoder,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "text_proj": text_proj,
									    "feature_extractor": feature_extractor,
									    "image_encoder": image_encoder,
									    "super_res_first": super_res_first,
									    "super_res_last": super_res_last,
									    "decoder_scheduler": decoder_scheduler,
									    "super_res_scheduler": super_res_scheduler,
									}







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							str=0				,  _UpperCamelCase					:							Any=True				) ->Dict:
									snake_case_  =      floats_tensor((1, 3, 3_2, 3_2)				,  rng=random.Random(_UpperCamelCase				)				).to(_UpperCamelCase				)
									if str(_UpperCamelCase				).startswith('''mps'''				):
															snake_case_  =      torch.manual_seed(_UpperCamelCase				)
									else:
															snake_case_  =      torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)

									if pil_image:
															snake_case_  =      input_image * 0.5 + 0.5
															snake_case_  =      input_image.clamp(0				,  1				)
															snake_case_  =      input_image.cpu().permute(0				,  2				,  3				,  1				).float().numpy()
															snake_case_  =      DiffusionPipeline.numpy_to_pil(_UpperCamelCase				)[0]

									return {
									    "image": input_image,
									    "generator": generator,
									    "decoder_num_inference_steps": 2,
									    "super_res_num_inference_steps": 2,
									    "output_type": "np",
									}







			def 							snake_case__(	self					:							List[str]				) ->List[Any]:
									snake_case_  =      '''cpu'''

									snake_case_  =      self.get_dummy_components()

									snake_case_  =      self.pipeline_class(**_UpperCamelCase				)
									snake_case_  =      pipe.to(_UpperCamelCase				)

									pipe.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      self.get_dummy_inputs(_UpperCamelCase				,  pil_image=_UpperCamelCase				)

									snake_case_  =      pipe(**_UpperCamelCase				)
									snake_case_  =      output.images

									snake_case_  =      self.get_dummy_inputs(_UpperCamelCase				,  pil_image=_UpperCamelCase				)

									snake_case_  =      pipe(
									    **_UpperCamelCase				,  return_dict=_UpperCamelCase				,  )[0]

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

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

									snake_case_  =      np.array(
									    [
									        0.9997,
									        0.0002,
									        0.9997,
									        0.9997,
									        0.9969,
									        0.0023,
									        0.9997,
									        0.9969,
									        0.9970,
									    ]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2







			def 							snake_case__(	self					:							Tuple				) ->int:
									snake_case_  =      '''cpu'''

									snake_case_  =      self.get_dummy_components()

									snake_case_  =      self.pipeline_class(**_UpperCamelCase				)
									snake_case_  =      pipe.to(_UpperCamelCase				)

									pipe.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      self.get_dummy_inputs(_UpperCamelCase				,  pil_image=_UpperCamelCase				)

									snake_case_  =      pipe(**_UpperCamelCase				)
									snake_case_  =      output.images

									snake_case_  =      self.get_dummy_inputs(_UpperCamelCase				,  pil_image=_UpperCamelCase				)

									snake_case_  =      pipe(
									    **_UpperCamelCase				,  return_dict=_UpperCamelCase				,  )[0]

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

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

									snake_case_  =      np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2







			def 							snake_case__(	self					:							Any				) ->List[str]:
									snake_case_  =      '''cpu'''

									snake_case_  =      self.get_dummy_components()

									snake_case_  =      self.pipeline_class(**_UpperCamelCase				)
									snake_case_  =      pipe.to(_UpperCamelCase				)

									pipe.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      self.get_dummy_inputs(_UpperCamelCase				,  pil_image=_UpperCamelCase				)
									snake_case_  =      [
									    pipeline_inputs['''image'''],
									    pipeline_inputs['''image'''],
									]

									snake_case_  =      pipe(**_UpperCamelCase				)
									snake_case_  =      output.images

									snake_case_  =      self.get_dummy_inputs(_UpperCamelCase				,  pil_image=_UpperCamelCase				)
									snake_case_  =      [
									    tuple_pipeline_inputs['''image'''],
									    tuple_pipeline_inputs['''image'''],
									]

									snake_case_  =      pipe(
									    **_UpperCamelCase				,  return_dict=_UpperCamelCase				,  )[0]

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

									assert image.shape == (2, 6_4, 6_4, 3)

									snake_case_  =      np.array(
									    [
									        0.9997,
									        0.9989,
									        0.0008,
									        0.0021,
									        0.9960,
									        0.0018,
									        0.0014,
									        0.0002,
									        0.9933,
									    ]				)

									assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2







			def 							snake_case__(	self					:							Optional[Any]				) ->Any:
									snake_case_  =      torch.device('''cpu'''				)





									class       snake_case_  :







												'''simple docstring'''

												SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							1






									snake_case_  =      self.get_dummy_components()

									snake_case_  =      self.pipeline_class(**_UpperCamelCase				)
									snake_case_  =      pipe.to(_UpperCamelCase				)

									pipe.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      torch.Generator(device=_UpperCamelCase				).manual_seed(0				)
									snake_case_  =      pipe.decoder.dtype
									snake_case_  =      1

									snake_case_  =      (
									    batch_size,
									    pipe.decoder.config.in_channels,
									    pipe.decoder.config.sample_size,
									    pipe.decoder.config.sample_size,
									)
									snake_case_  =      pipe.prepare_latents(
									    _UpperCamelCase				,  dtype=_UpperCamelCase				,  device=_UpperCamelCase				,  generator=_UpperCamelCase				,  latents=_UpperCamelCase				,  scheduler=DummyScheduler()				)

									snake_case_  =      (
									    batch_size,
									    pipe.super_res_first.config.in_channels // 2,
									    pipe.super_res_first.config.sample_size,
									    pipe.super_res_first.config.sample_size,
									)
									snake_case_  =      pipe.prepare_latents(
									    _UpperCamelCase				,  dtype=_UpperCamelCase				,  device=_UpperCamelCase				,  generator=_UpperCamelCase				,  latents=_UpperCamelCase				,  scheduler=DummyScheduler()				)

									snake_case_  =      self.get_dummy_inputs(_UpperCamelCase				,  pil_image=_UpperCamelCase				)

									snake_case_  =      pipe(
									    **_UpperCamelCase				,  decoder_latents=_UpperCamelCase				,  super_res_latents=_UpperCamelCase				).images

									snake_case_  =      self.get_dummy_inputs(_UpperCamelCase				,  pil_image=_UpperCamelCase				)
									# Don't pass image, instead pass embedding
									snake_case_  =      pipeline_inputs.pop('''image'''				)
									snake_case_  =      pipe.image_encoder(_UpperCamelCase				).image_embeds

									snake_case_  =      pipe(
									    **_UpperCamelCase				,  decoder_latents=_UpperCamelCase				,  super_res_latents=_UpperCamelCase				,  image_embeddings=_UpperCamelCase				,  ).images

									# make sure passing text embeddings manually is identical
									assert np.abs(img_out_a - img_out_a				).max() < 1e-4







			@skip_mps
			def 							snake_case__(	self					:							Optional[Any]				) ->Any:
									snake_case_  =      torch_device == '''cpu'''

									# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
									snake_case_  =      1e-2

									self._test_attention_slicing_forward_pass(
									    test_max_difference=_UpperCamelCase				,  expected_max_diff=_UpperCamelCase				)







			@skip_mps
			def 							snake_case__(	self					:							Optional[int]				) ->Optional[Any]:
									snake_case_  =      torch_device == '''cpu'''
									snake_case_  =      True
									snake_case_  =      [
									    '''decoder_num_inference_steps''',
									    '''super_res_num_inference_steps''',
									]

									self._test_inference_batch_single_identical(
									    test_max_difference=_UpperCamelCase				,  relax_max_difference=_UpperCamelCase				,  additional_params_copy_to_batched_inputs=_UpperCamelCase				,  )







			def 							snake_case__(	self					:							List[Any]				) ->List[str]:
									snake_case_  =      [
									    '''decoder_num_inference_steps''',
									    '''super_res_num_inference_steps''',
									]

									if torch_device == "mps":
															# TODO: MPS errors with larger batch sizes
															snake_case_  =      [2, 3]
															self._test_inference_batch_consistent(
															    batch_sizes=_UpperCamelCase				,  additional_params_copy_to_batched_inputs=_UpperCamelCase				,  )
									else:
															self._test_inference_batch_consistent(
															    additional_params_copy_to_batched_inputs=_UpperCamelCase				)







			@skip_mps
			def 							snake_case__(	self					:							Dict				) ->Optional[int]:
									return super().test_dict_tuple_outputs_equivalent()







			@skip_mps
			def 							snake_case__(	self					:							Tuple				) ->List[Any]:
									return super().test_save_load_local()







			@skip_mps
			def 							snake_case__(	self					:							List[Any]				) ->Optional[Any]:
									return super().test_save_load_optional_components()






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







			'''simple docstring'''

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







			def 							snake_case__(	self					:							Any				) ->Tuple:
									snake_case_  =      load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''				)
									snake_case_  =      load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''				)

									snake_case_  =      UnCLIPImageVariationPipeline.from_pretrained(
									    '''kakaobrain/karlo-v1-alpha-image-variations'''				,  torch_dtype=torch.floataa				)
									snake_case_  =      pipeline.to(_UpperCamelCase				)
									pipeline.set_progress_bar_config(disable=_UpperCamelCase				)

									snake_case_  =      torch.Generator(device='''cpu'''				).manual_seed(0				)
									snake_case_  =      pipeline(
									    _UpperCamelCase				,  generator=_UpperCamelCase				,  output_type='''np'''				,  )

									snake_case_  =      output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)

									assert_mean_pixel_difference(_UpperCamelCase				,  _UpperCamelCase				,  1_5				)





from __future__ import annotations

import math


def 					_a							(					lowerCAmelCase )->	bool:

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5    ,						int(math.sqrt(lowerCAmelCase ) + 1 )    ,						6 ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True


SCREAMING_SNAKE_CASE:     List[str]									= [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]


def 					_a							(					lowerCAmelCase )->	list[int]:
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
				raise ValueError('n must be an integer' )
		if n <= 0:
				raise ValueError('n must be >= 0' )

		SCREAMING_SNAKE_CASE_					      =							[]
		for num in range(len(lowerCAmelCase ) ):
				SCREAMING_SNAKE_CASE_					      =							0
				while 2 * i * i <= odd_composites[num]:
						SCREAMING_SNAKE_CASE_					      =							odd_composites[num] - 2 * i * i
						if is_prime(lowerCAmelCase ):
								break
						i += 1
				else:
						list_nums.append(odd_composites[num] )
						if len(lowerCAmelCase ) == n:
								return list_nums

		return []


def 					_a							(					)->	int:
		return compute_nums(1 )[0]


if __name__ == "__main__":
						print(f"""{solution() = }""")