'''simple docstring'''


import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 						A   (   __lowercase       ,	unittest.TestCase   ):
							a_        =      VideoToVideoSDPipeline
							a_        =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({'''video'''}   ) - {'''image''', '''width''', '''height'''}
							a_        =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''video'''}   ) - {'''image'''}
							a_        =      PipelineTesterMixin.required_optional_params - {'''latents'''}
							a_        =      False

							# No `output_type`.
							a_        =      frozenset(
							    [
							        '''num_inference_steps''',
							        '''generator''',
							        '''latents''',
							        '''return_dict''',
							        '''callback''',
							        '''callback_steps''',
							    ]   )





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Optional[Any]:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4, 6_4, 6_4)     ,		layers_per_block=2     ,		sample_size=3_2     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')     ,		up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')     ,		cross_attention_dim=3_2     ,		attention_head_dim=4     ,		)
								__UpperCAmelCase							=			DDIMScheduler(
								    beta_start=0.0_0_0_8_5     ,		beta_end=0.0_1_2     ,		beta_schedule='''scaled_linear'''     ,		clip_sample=_A     ,		set_alpha_to_one=_A     ,		)
								torch.manual_seed(0	)
								__UpperCAmelCase							=			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	)
								__UpperCAmelCase							=			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     ,		)
								__UpperCAmelCase							=			CLIPTextModel(_A	)
								__UpperCAmelCase							=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

								__UpperCAmelCase							=			{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								}
								return components





							def       snake_case__						(      self				:							List[str]     ,		__a				:							Any     ,		__a				:							str=0	)							->	Optional[Any]:
								# 3 frames
								__UpperCAmelCase							=			floats_tensor((1, 3, 3, 3_2, 3_2)     ,		rng=random.Random(_A	)	).to(_A	)

								if str(_A	).startswith('''mps'''	):
									__UpperCAmelCase							=			torch.manual_seed(_A	)
								else:
									__UpperCAmelCase							=			torch.Generator(device=_A	).manual_seed(_A	)
								__UpperCAmelCase							=			{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''video''': video,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''pt''',
								}
								return inputs





							def       snake_case__						(      self				:							List[Any]	)							->	str:
								__UpperCAmelCase							=			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__UpperCAmelCase							=			self.get_dummy_components()
								__UpperCAmelCase							=			VideoToVideoSDPipeline(**_A	)
								__UpperCAmelCase							=			sd_pipe.to(_A	)
								sd_pipe.set_progress_bar_config(disable=_A	)

								__UpperCAmelCase							=			self.get_dummy_inputs(_A	)
								__UpperCAmelCase							=			'''np'''
								__UpperCAmelCase							=			sd_pipe(**_A	).frames
								__UpperCAmelCase							=			frames[0][-3:, -3:, -1]

								assert frames[0].shape == (3_2, 3_2, 3)
								__UpperCAmelCase							=			np.array([1_0_6, 1_1_7, 1_1_3, 1_7_4, 1_3_7, 1_1_2, 1_4_8, 1_5_1, 1_3_1]	)

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





							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()     ,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''     ,		)
							def       snake_case__						(      self				:							Optional[Any]	)							->	Union[str, Any]:
								self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_A     ,		expected_max_diff=5e-3	)





							@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''	)
							def       snake_case__						(      self				:							List[Any]	)							->	Tuple:
								pass





							@unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.'''	)
							def       snake_case__						(      self				:							int	)							->	str:
								pass





							@unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.'''	)
							def       snake_case__						(      self				:							List[str]	)							->	str:
								pass






							def       snake_case__						(      self				:							List[str]	)							->	List[Any]:
								return super().test_progress_bar()






@slow
@skip_mps
class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							List[str]	)							->	str:
								__UpperCAmelCase							=			VideoToVideoSDPipeline.from_pretrained('''cerspense/zeroscope_v2_XL'''     ,		torch_dtype=torch.floataa	)
								pipe.enable_model_cpu_offload()

								# 10 frames
								__UpperCAmelCase							=			torch.Generator(device='''cpu'''	).manual_seed(0	)
								__UpperCAmelCase							=			torch.randn((1, 1_0, 3, 1_0_2_4, 5_7_6)     ,		generator=_A	)
								__UpperCAmelCase							=			video.to('''cuda'''	)

								__UpperCAmelCase							=			'''Spiderman is surfing'''

								__UpperCAmelCase							=			pipe(_A     ,		video=_A     ,		generator=_A     ,		num_inference_steps=3     ,		output_type='''pt'''	).frames

								__UpperCAmelCase							=			np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]	)
								assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array	).sum() < 1e-2



'''simple docstring'''


from __future__ import annotations

import math





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			u
	for i in range(1    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			temp * (u - i)
	return temp





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			int(input('''enter the numbers of values: '''       )       )
	__UpperCAmelCase							=			[]
	for _ in range(UpperCamelCase__       ):
		y.append([]       )
	for i in range(UpperCamelCase__       ):
		for j in range(UpperCamelCase__       ):
			y[i].append(UpperCamelCase__       )
			__UpperCAmelCase							=			0

	print('''enter the values of parameters in a list: '''       )
	__UpperCAmelCase							=			list(map(UpperCamelCase__    ,					input().split()       )       )

	print('''enter the values of corresponding parameters: '''       )
	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			float(input()       )

	__UpperCAmelCase							=			int(input('''enter the value to interpolate: '''       )       )
	__UpperCAmelCase							=			(value - x[0]) / (x[1] - x[0])

	# for calculating forward difference table

	for i in range(1    ,					UpperCamelCase__       ):
		for j in range(n - i       ):
			__UpperCAmelCase							=			y[j + 1][i - 1] - y[j][i - 1]

	__UpperCAmelCase							=			y[0][0]
	for i in range(1    ,					UpperCamelCase__       ):
		summ += (ucal(UpperCamelCase__    ,					UpperCamelCase__       ) * y[0][i]) / math.factorial(UpperCamelCase__       )

	print(f"""the value at {value} is {summ}"""       )


if __name__ == "__main__":
					main()
