



import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()



class 			__lowerCAmelCase  (						__lowercase	,						unittest.TestCase ):
 lowerCAmelCase__							       =	LDMTextToImagePipeline
 lowerCAmelCase__							       =	TEXT_TO_IMAGE_PARAMS - {
     "negative_prompt",
     "negative_prompt_embeds",
     "cross_attention_kwargs",
     "prompt_embeds",
 }
 lowerCAmelCase__							       =	PipelineTesterMixin.required_optional_params - {
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowerCAmelCase__							       =	TEXT_TO_IMAGE_BATCH_PARAMS
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    torch.manual_seed(0					)
    __lowerCamelCase    				=     UNetaDConditionModel(
        block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=4						,					out_channels=4						,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,					cross_attention_dim=32						,					)
    __lowerCamelCase    				=     DDIMScheduler(
        beta_start=0.00_085						,					beta_end=0.012						,					beta_schedule='''scaled_linear'''						,					clip_sample=_a						,					set_alpha_to_one=_a						,					)
    torch.manual_seed(0					)
    __lowerCamelCase    				=     AutoencoderKL(
        block_out_channels=(32, 64)						,					in_channels=3						,					out_channels=3						,					down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')						,					up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')						,					latent_channels=4						,					)
    torch.manual_seed(0					)
    __lowerCamelCase    				=     CLIPTextConfig(
        bos_token_id=0						,					eos_token_id=2						,					hidden_size=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						,					)
    __lowerCamelCase    				=     CLIPTextModel(_a					)
    __lowerCamelCase    				=     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

    __lowerCamelCase    				=     {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vqvae''': vae,
        '''bert''': text_encoder,
        '''tokenizer''': tokenizer,
    }
    return components




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=0					):



    '''simple docstring'''
    if str(_a					).startswith('''mps'''					):
       __lowerCamelCase    				=     torch.manual_seed(_a					)
    else:
       __lowerCamelCase    				=     torch.Generator(device=_a					).manual_seed(_a					)
    __lowerCamelCase    				=     {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''cpu'''  # ensure determinism for the device-dependent torch.Generator

    __lowerCamelCase    				=     self.get_dummy_components()
    __lowerCamelCase    				=     LDMTextToImagePipeline(**_a					)
    pipe.to(_a					)
    pipe.set_progress_bar_config(disable=_a					)

    __lowerCamelCase    				=     self.get_dummy_inputs(_a					)
    __lowerCamelCase    				=     pipe(**_a					).images
    __lowerCamelCase    				=     image[0, -3:, -3:, -1]

    assert image.shape == (1, 16, 16, 3)
    __lowerCamelCase    				=     np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014]					)

    assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-3



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




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=torch.floataa						,					__UpperCAmelCase=0					):



    '''simple docstring'''
    __lowerCamelCase    				=     torch.manual_seed(_a					)
    __lowerCamelCase    				=     np.random.RandomState(_a					).standard_normal((1, 4, 32, 32)					)
    __lowerCamelCase    				=     torch.from_numpy(_a					).to(device=_a						,					dtype=_a					)
    __lowerCamelCase    				=     {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''latents''': latents,
        '''generator''': generator,
        '''num_inference_steps''': 3,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''					).to(_a					)
    pipe.set_progress_bar_config(disable=_a					)

    __lowerCamelCase    				=     self.get_inputs(_a					)
    __lowerCamelCase    				=     pipe(**_a					).images
    __lowerCamelCase    				=     image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 256, 256, 3)
    __lowerCamelCase    				=     np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878]					)
    __lowerCamelCase    				=     np.abs(expected_slice - image_slice					).max()
    assert max_diff < 1E-3



@nightly
@require_torch_gpu
class 			__lowerCAmelCase  (						unittest.TestCase ):




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase=torch.floataa						,					__UpperCAmelCase=0					):



    '''simple docstring'''
    __lowerCamelCase    				=     torch.manual_seed(_a					)
    __lowerCamelCase    				=     np.random.RandomState(_a					).standard_normal((1, 4, 32, 32)					)
    __lowerCamelCase    				=     torch.from_numpy(_a					).to(device=_a						,					dtype=_a					)
    __lowerCamelCase    				=     {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''latents''': latents,
        '''generator''': generator,
        '''num_inference_steps''': 50,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''					).to(_a					)
    pipe.set_progress_bar_config(disable=_a					)

    __lowerCamelCase    				=     self.get_inputs(_a					)
    __lowerCamelCase    				=     pipe(**_a					).images[0]

    __lowerCamelCase    				=     load_numpy(
        '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''					)
    __lowerCamelCase    				=     np.abs(expected_image - image					).max()
    assert max_diff < 1E-3









def   __UpperCAmelCase					(      __a :     str      )     ->	list:

					"""simple docstring"""

					if n_term == "":
										return []
					_a      :       list   =						[]
					for temp in range(int(__a      )      ):
										series.append(F"""1/{temp + 1}""" if series else '''1'''      )
					return series


if __name__ == "__main__":
			a__												=						input('''Enter the last number (nth term) of the Harmonic Series''')
			print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
			print(harmonic_series(nth_term))



