




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class      a__       (   snake_case__					,							unittest.TestCase ):
						_a				:       Union[str, Any]   =       UnCLIPPipeline
						_a				:       Dict   =       TEXT_TO_IMAGE_PARAMS - {
						    """negative_prompt""",
						    """height""",
						    """width""",
						    """negative_prompt_embeds""",
						    """guidance_scale""",
						    """prompt_embeds""",
						    """cross_attention_kwargs""",
						}
						_a				:       str   =       TEXT_TO_IMAGE_BATCH_PARAMS
						_a				:       Tuple   =       [
						    """generator""",
						    """return_dict""",
						    """prior_num_inference_steps""",
						    """decoder_num_inference_steps""",
						    """super_res_num_inference_steps""",
						]
						_a				:       Optional[int]   =       False






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return 3_2






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return 3_2






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return self.time_input_dim






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return self.time_input_dim * 4






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return 1_0_0






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )
											return tokenizer






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											torch.manual_seed(0     )
											__lowerCAmelCase							  =			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-0_5						,     num_attention_heads=4						,     num_hidden_layers=5						,     pad_token_id=1						,     vocab_size=1_0_0_0						,     )
											return CLIPTextModelWithProjection(_A     )






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											torch.manual_seed(0     )

											__lowerCAmelCase							  =			{
											    "num_attention_heads": 2,
											    "attention_head_dim": 1_2,
											    "embedding_dim": self.text_embedder_hidden_size,
											    "num_layers": 1,
											}

											__lowerCAmelCase							  =			PriorTransformer(**_A     )
											return model






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											torch.manual_seed(0     )

											__lowerCAmelCase							  =			{
											    "clip_embeddings_dim": self.text_embedder_hidden_size,
											    "time_embed_dim": self.time_embed_dim,
											    "cross_attention_dim": self.cross_attention_dim,
											}

											__lowerCAmelCase							  =			UnCLIPTextProjModel(**_A     )
											return model






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											torch.manual_seed(0     )

											__lowerCAmelCase							  =			{
											    "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",
											}

											__lowerCAmelCase							  =			UNetaDConditionModel(**_A     )
											return model






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											torch.manual_seed(0     )

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






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											torch.manual_seed(1     )

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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.dummy_prior
											__lowerCAmelCase							  =			self.dummy_decoder
											__lowerCAmelCase							  =			self.dummy_text_proj
											__lowerCAmelCase							  =			self.dummy_text_encoder
											__lowerCAmelCase							  =			self.dummy_tokenizer
											__lowerCAmelCase							  =			self.dummy_super_res_first
											__lowerCAmelCase							  =			self.dummy_super_res_last

											__lowerCAmelCase							  =			UnCLIPScheduler(
											    variance_type="fixed_small_log"						,     prediction_type="sample"						,     num_train_timesteps=1_0_0_0						,     clip_sample_range=5.0						,     )

											__lowerCAmelCase							  =			UnCLIPScheduler(
											    variance_type="learned_range"						,     prediction_type="epsilon"						,     num_train_timesteps=1_0_0_0						,     )

											__lowerCAmelCase							  =			UnCLIPScheduler(
											    variance_type="fixed_small_log"						,     prediction_type="epsilon"						,     num_train_timesteps=1_0_0_0						,     )

											__lowerCAmelCase							  =			{
											    "prior": prior,
											    "decoder": decoder,
											    "text_proj": text_proj,
											    "text_encoder": text_encoder,
											    "tokenizer": tokenizer,
											    "super_res_first": super_res_first,
											    "super_res_last": super_res_last,
											    "prior_scheduler": prior_scheduler,
											    "decoder_scheduler": decoder_scheduler,
											    "super_res_scheduler": super_res_scheduler,
											}

											return components






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A=0     ):




											"""simple docstring"""



											if str(_A     ).startswith("mps"     ):
																__lowerCAmelCase							  =			torch.manual_seed(_A     )
											else:
																__lowerCAmelCase							  =			torch.Generator(device=_A     ).manual_seed(_A     )
											__lowerCAmelCase							  =			{
											    "prompt": "horse",
											    "generator": generator,
											    "prior_num_inference_steps": 2,
											    "decoder_num_inference_steps": 2,
											    "super_res_num_inference_steps": 2,
											    "output_type": "numpy",
											}
											return inputs






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			"cpu"

											__lowerCAmelCase							  =			self.get_dummy_components()

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

											pipe.set_progress_bar_config(disable=_A     )

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

											__lowerCAmelCase							  =			pipe(
											    **self.get_dummy_inputs(_A     )						,     return_dict=_A						,     )[0]

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

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

											__lowerCAmelCase							  =			np.array(
											    [
											        0.99_97,
											        0.99_88,
											        0.00_28,
											        0.99_97,
											        0.99_84,
											        0.99_65,
											        0.00_29,
											        0.99_86,
											        0.00_25,
											    ]     )

											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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			torch.device("cpu"     )
											class      a__       :
																	_a				:       int   =       1






											__lowerCAmelCase							  =			self.get_dummy_components()

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

											__lowerCAmelCase							  =			components["prior"]
											__lowerCAmelCase							  =			components["decoder"]
											__lowerCAmelCase							  =			components["super_res_first"]
											__lowerCAmelCase							  =			components["tokenizer"]
											__lowerCAmelCase							  =			components["text_encoder"]

											__lowerCAmelCase							  =			torch.Generator(device=_A     ).manual_seed(0     )
											__lowerCAmelCase							  =			prior.dtype
											__lowerCAmelCase							  =			1

											__lowerCAmelCase							  =			(batch_size, prior.config.embedding_dim)
											__lowerCAmelCase							  =			pipe.prepare_latents(
											    _A						,     dtype=_A						,     device=_A						,     generator=_A						,     latents=_A						,     scheduler=DummyScheduler()     )
											__lowerCAmelCase							  =			(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
											__lowerCAmelCase							  =			pipe.prepare_latents(
											    _A						,     dtype=_A						,     device=_A						,     generator=_A						,     latents=_A						,     scheduler=DummyScheduler()     )

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

											pipe.set_progress_bar_config(disable=_A     )

											__lowerCAmelCase							  =			"this is a prompt example"

											__lowerCAmelCase							  =			torch.Generator(device=_A     ).manual_seed(0     )
											__lowerCAmelCase							  =			pipe(
											    [prompt]						,     generator=_A						,     prior_num_inference_steps=2						,     decoder_num_inference_steps=2						,     super_res_num_inference_steps=2						,     prior_latents=_A						,     decoder_latents=_A						,     super_res_latents=_A						,     output_type="np"						,     )
											__lowerCAmelCase							  =			output.images

											__lowerCAmelCase							  =			tokenizer(
											    _A						,     padding="max_length"						,     max_length=tokenizer.model_max_length						,     return_tensors="pt"						,     )
											__lowerCAmelCase							  =			text_encoder(text_inputs.input_ids     )
											__lowerCAmelCase							  =			text_inputs.attention_mask

											__lowerCAmelCase							  =			torch.Generator(device=_A     ).manual_seed(0     )
											__lowerCAmelCase							  =			pipe(
											    generator=_A						,     prior_num_inference_steps=2						,     decoder_num_inference_steps=2						,     super_res_num_inference_steps=2						,     prior_latents=_A						,     decoder_latents=_A						,     super_res_latents=_A						,     text_model_output=_A						,     text_attention_mask=_A						,     output_type="np"						,     )[0]

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






						@skip_mps
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			torch_device == "cpu"

											self._test_attention_slicing_forward_pass(test_max_difference=_A						,     expected_max_diff=0.01     )






						@skip_mps
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			torch_device == "cpu"
											__lowerCAmelCase							  =			True
											__lowerCAmelCase							  =			[
											    "prior_num_inference_steps",
											    "decoder_num_inference_steps",
											    "super_res_num_inference_steps",
											]

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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			[
											    "prior_num_inference_steps",
											    "decoder_num_inference_steps",
											    "super_res_num_inference_steps",
											]

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






						@skip_mps
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return super().test_save_load_local()






						@skip_mps
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return super().test_save_load_optional_components()






@nightly
class      a__       (   unittest.TestCase ):






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    "/unclip/karlo_v1_alpha_horse_cpu.npy"     )

											__lowerCAmelCase							  =			UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"     )
											pipeline.set_progress_bar_config(disable=_A     )

											__lowerCAmelCase							  =			torch.manual_seed(0     )
											__lowerCAmelCase							  =			pipeline(
											    "horse"						,     num_images_per_prompt=1						,     generator=_A						,     output_type="np"						,     )

											__lowerCAmelCase							  =			output.images[0]

											assert image.shape == (2_5_6, 2_5_6, 3)
											assert np.abs(expected_image - image     ).max() < 1E-1






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






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			load_numpy(
											    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
											    "/unclip/karlo_v1_alpha_horse_fp16.npy"     )

											__lowerCAmelCase							  =			UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"						,     torch_dtype=torch.floataa     )
											__lowerCAmelCase							  =			pipeline.to(_A     )
											pipeline.set_progress_bar_config(disable=_A     )

											__lowerCAmelCase							  =			torch.Generator(device="cpu"     ).manual_seed(0     )
											__lowerCAmelCase							  =			pipeline(
											    "horse"						,     generator=_A						,     output_type="np"						,     )

											__lowerCAmelCase							  =			output.images[0]

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

											assert_mean_pixel_difference(_A						,     _A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											__lowerCAmelCase							  =			UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"						,     torch_dtype=torch.floataa     )
											__lowerCAmelCase							  =			pipe.to(_A     )
											pipe.set_progress_bar_config(disable=_A     )
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											__lowerCAmelCase							  =			pipe(
											    "horse"						,     num_images_per_prompt=1						,     prior_num_inference_steps=2						,     decoder_num_inference_steps=2						,     super_res_num_inference_steps=2						,     output_type="np"						,     )

											__lowerCAmelCase							  =			torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 1_0**9










def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int	):
					if a < b:
										return gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					if a % b == 0:
										return b
					return gcd(SCREAMING_SNAKE_CASE_ , a % b	)







def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int	):
					if y == 0:
										return 1
					__lowerCAmelCase							  =			power(SCREAMING_SNAKE_CASE_ , y // 2 , SCREAMING_SNAKE_CASE_	) % mod
					__lowerCAmelCase							  =			(temp * temp) % mod
					if y % 2 == 1:
										__lowerCAmelCase							  =			(temp * x) % mod
					return temp







def       _a (		SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			2
					while b < n:
										if gcd(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	) == 1 and power(SCREAMING_SNAKE_CASE_ , n - 1 , SCREAMING_SNAKE_CASE_	) != 1:
															return False
										b += 1
					return True


if __name__ == "__main__":
					UpperCamelCase__            =     int(input("""Enter number: """).strip())
					if is_carmichael_number(number):
										print(f'''{number} is a Carmichael Number.''')
					else:
										print(f'''{number} is not a Carmichael Number.''')




