



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

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


_lowercase		:				List[Any]      		=False

@skip_mps
class        snake_case__    (__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					StableDiffusionAttendAndExcitePipeline
						__lowerCAmelCase					:Any      =					False
						__lowerCAmelCase					:Optional[int]      =					TEXT_TO_IMAGE_PARAMS
						__lowerCAmelCase					:List[str]      =					TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"}		)
						__lowerCAmelCase					:str      =					TEXT_TO_IMAGE_IMAGE_PARAMS
						__lowerCAmelCase					:Dict      =					TEXT_TO_IMAGE_IMAGE_PARAMS

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls  ) ->				Dict:
									"""simple docstring"""




									super().setUpClass()
									torch.use_deterministic_algorithms(__lowercase  )

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls  ) ->				str:
									"""simple docstring"""




									super().tearDownClass()
									torch.use_deterministic_algorithms(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									torch.manual_seed(0  )
									a__       :						Optional[int]							=  UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4) ,       layers_per_block=1 ,       sample_size=3_2 ,       in_channels=4 ,       out_channels=4 ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,       cross_attention_dim=3_2 ,       attention_head_dim=(2, 4) ,       use_linear_projection=__lowercase ,       )
									a__       :						Optional[int]							=  DDIMScheduler(
									    beta_start=0.0_0_0_8_5 ,       beta_end=0.0_1_2 ,       beta_schedule="""scaled_linear""" ,       clip_sample=__lowercase ,       set_alpha_to_one=__lowercase ,       )
									torch.manual_seed(0  )
									a__       :						List[str]							=  AutoencoderKL(
									    block_out_channels=[3_2, 6_4] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,       latent_channels=4 ,       sample_size=1_2_8 ,       )
									torch.manual_seed(0  )
									a__       :						Any							=  CLIPTextConfig(
									    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=3_2 ,       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 ,       hidden_act="""gelu""" ,       projection_dim=5_1_2 ,       )
									a__       :						int							=  CLIPTextModel(__lowercase  )
									a__       :						Tuple							=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

									a__       :						Any							=  {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}

									return components

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=0  ) ->				int:
									"""simple docstring"""




									if str(__lowercase  ).startswith("""mps"""  ):
												a__       :						Dict							=  torch.manual_seed(__lowercase  )
									else:
												a__       :						Any							=  torch.Generator(device=__lowercase  ).manual_seed(__lowercase  )
									a__       :						Dict							=  {
									    """prompt""": """a cat and a frog""",
									    """token_indices""": [2, 5],
									    """generator""": generator,
									    """num_inference_steps""": 1,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									    """max_iter_to_alter""": 2,
									    """thresholds""": {0: 0.7},
									}
									return inputs

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						int							=  """cpu"""

									a__       :						int							=  self.get_dummy_components()
									a__       :						int							=  self.pipeline_class(**__lowercase  )
									pipe.to(__lowercase  )
									pipe.set_progress_bar_config(disable=__lowercase  )

									a__       :						Dict							=  self.get_dummy_inputs(__lowercase  )
									a__       :						Optional[int]							=  pipe(**__lowercase  ).images
									a__       :						List[Any]							=  image[0, -3:, -3:, -1]

									self.assertEqual(image.shape ,       (1, 6_4, 6_4, 3)  )
									a__       :						Optional[Any]							=  np.array(
									    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]  )
									a__       :						str							=  np.abs(image_slice.flatten() - expected_slice  ).max()
									self.assertLessEqual(__lowercase ,       1E-3  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									super().test_cpu_offload_forward_pass(expected_max_diff=5E-4  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									self._test_inference_batch_consistent(batch_sizes=[1, 2]  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									self._test_inference_batch_single_identical(batch_size=2 ,       expected_max_diff=7E-4  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									super().test_save_load_local(expected_max_difference=5E-4  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									super().test_save_load_optional_components(expected_max_difference=4E-4  )




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







						"""simple docstring"""




						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls  ) ->				Optional[int]:
									"""simple docstring"""




									super().setUpClass()
									torch.use_deterministic_algorithms(__lowercase  )

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(       cls  ) ->				Tuple:
									"""simple docstring"""




									super().tearDownClass()
									torch.use_deterministic_algorithms(__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Dict							=  torch.manual_seed(5_1  )

									a__       :						List[str]							=  StableDiffusionAttendAndExcitePipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4""" ,       safety_checker=__lowercase ,       torch_dtype=torch.floataa  )
									pipe.to("""cuda"""  )

									a__       :						Optional[Any]							=  """a painting of an elephant with glasses"""
									a__       :						int							=  [5, 7]

									a__       :						Dict							=  pipe(
									    prompt=__lowercase ,       token_indices=__lowercase ,       guidance_scale=7.5 ,       generator=__lowercase ,       num_inference_steps=5 ,       max_iter_to_alter=5 ,       output_type="""numpy""" ,       ).images[0]

									a__       :						Optional[Any]							=  load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""  )
									assert np.abs((expected_image - image).max()  ) < 5E-1









from __future__ import annotations

import math

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       int)							->	float:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  u
			for i in range(1						,       _lowercase):
						a__       :						Optional[int]							=  temp * (u - i)
			return temp

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						Tuple							=  int(input("""enter the numbers of values: """))
			a__       :						list[list[float]]							=  []
			for _ in range(_lowercase):
						y.append([])
			for i in range(_lowercase):
						for j in range(_lowercase):
									y[i].append(_lowercase)
									a__       :						Optional[Any]							=  0

			print("""enter the values of parameters in a list: """)
			a__       :						List[Any]							=  list(map(_lowercase						,       input().split()))

			print("""enter the values of corresponding parameters: """)
			for i in range(_lowercase):
						a__       :						Optional[Any]							=  float(input())

			a__       :						Tuple							=  int(input("""enter the value to interpolate: """))
			a__       :						int							=  (value - x[0]) / (x[1] - x[0])

			# for calculating forward difference table

			for i in range(1						,       _lowercase):
						for j in range(n - i):
									a__       :						int							=  y[j + 1][i - 1] - y[j][i - 1]

			a__       :						Optional[int]							=  y[0][0]
			for i in range(1						,       _lowercase):
						summ += (ucal(_lowercase						,       _lowercase) * y[0][i]) / math.factorial(_lowercase)

			print(F'''the value at {value} is {summ}''')


if __name__ == "__main__":
		main()




