
import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	ShapEPipeline
	snake_case_          =	["prompt"]
	snake_case_          =	["prompt"]
	snake_case_          =	[
	    "num_images_per_prompt",
	    "num_inference_steps",
	    "generator",
	    "latents",
	    "guidance_scale",
	    "frame_size",
	    "output_type",
	    "return_dict",
	]
	snake_case_          =	False






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					return 32






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return 32






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					return self.time_input_dim * 4






	@property
	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					return 8






	@property
	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
					return tokenizer






	@property
	def 							UpperCamelCase_    (			self   :					int					):
					torch.manual_seed(0					)
					__A       =					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=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,)
					return CLIPTextModelWithProjection(A					)






	@property
	def 							UpperCamelCase_    (			self   :					List[Any]					):
					torch.manual_seed(0					)

					__A       =					{
					    "num_attention_heads": 2,
					    "attention_head_dim": 16,
					    "embedding_dim": self.time_input_dim,
					    "num_embeddings": 32,
					    "embedding_proj_dim": self.text_embedder_hidden_size,
					    "time_embed_dim": self.time_embed_dim,
					    "num_layers": 1,
					    "clip_embed_dim": self.time_input_dim * 2,
					    "additional_embeddings": 0,
					    "time_embed_act_fn": "gelu",
					    "norm_in_type": "layer",
					    "encoder_hid_proj_type": None,
					    "added_emb_type": None,
					}

					__A       =					PriorTransformer(**A					)
					return model






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

					__A       =					{
					    "param_shapes": (
					        (self.renderer_dim, 93),
					        (self.renderer_dim, 8),
					        (self.renderer_dim, 8),
					        (self.renderer_dim, 8),
					    ),
					    "d_latent": self.time_input_dim,
					    "d_hidden": self.renderer_dim,
					    "n_output": 12,
					    "background": (
					        0.1,
					        0.1,
					        0.1,
					    ),
					}
					__A       =					ShapERenderer(**A					)
					return model






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					self.dummy_prior
					__A       =					self.dummy_text_encoder
					__A       =					self.dummy_tokenizer
					__A       =					self.dummy_renderer

					__A       =					HeunDiscreteScheduler(
					    beta_schedule="exp"				,num_train_timesteps=10_24				,prediction_type="sample"				,use_karras_sigmas=A				,clip_sample=A				,clip_sample_range=1.0				,)
					__A       =					{
					    "prior": prior,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "renderer": renderer,
					    "scheduler": scheduler,
					}

					return components






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					Optional[Any]				,A   :					Any=0					):
					if str(A					).startswith("mps"					):
									__A       =					torch.manual_seed(A					)
					else:
									__A       =					torch.Generator(device=A					).manual_seed(A					)
					__A       =					{
					    "prompt": "horse",
					    "generator": generator,
					    "num_inference_steps": 1,
					    "frame_size": 32,
					    "output_type": "np",
					}
					return inputs






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					"cpu"

					__A       =					self.get_dummy_components()

					__A       =					self.pipeline_class(**A					)
					__A       =					pipe.to(A					)

					pipe.set_progress_bar_config(disable=A					)

					__A       =					pipe(**self.get_dummy_inputs(A					)					)
					__A       =					output.images[0]
					__A       =					image[0, -3:, -3:, -1]

					assert image.shape == (20, 32, 32, 3)

					__A       =					np.array(
					    [
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					    ]					)

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






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
					self._test_inference_batch_consistent(batch_sizes=[1, 2]					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					torch_device == "cpu"
					__A       =					True

					self._test_inference_batch_single_identical(
					    batch_size=2				,test_max_difference=A				,relax_max_difference=A				,)







	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					__A       =					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					1
					__A       =					2

					__A       =					self.get_dummy_inputs(A					)

					for key in inputs.keys():
									if key in self.batch_params:
													__A       =					batch_size * [inputs[key]]

					__A       =					pipe(**A				,num_images_per_prompt=A					)[0]

					assert images.shape[0] == batch_size * num_images_per_prompt





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




	'''simple docstring'''





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







	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/shap_e/test_shap_e_np_out.npy"					)
					__A       =					ShapEPipeline.from_pretrained("openai/shap-e"					)
					__A       =					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					torch.Generator(device=A					).manual_seed(0					)

					__A       =					pipe(
					    "a shark"				,generator=A				,guidance_scale=15.0				,num_inference_steps=64				,frame_size=64				,output_type="np"				,).images[0]

					assert images.shape == (20, 64, 64, 3)

					assert_mean_pixel_difference(A				,A					)







'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				int(input("Enter the power for  P-Series"))
       print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
       print(p_series(nth_term, power))


