'''simple docstring'''





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				( __lowerCamelCase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  ShapEPipeline
     A_										=  ['prompt']
     A_										=  ['prompt']
     A_										=  [
         'num_images_per_prompt',
         'num_inference_steps',
         'generator',
         'latents',
         'guidance_scale',
         'frame_size',
         'output_type',
         'return_dict',
     ]
     A_										=  False



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           return 32



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           return 32



     @property
     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           return self.time_input_dim * 4



     @property
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           return 8



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)
           return tokenizer



     @property
     def 		UpperCamelCase_						(					self						) ->				int:






           """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=37				,				layer_norm_eps=1E-0_5				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=10_00				,				)
           return CLIPTextModelWithProjection(UpperCAmelCase_						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           torch.manual_seed(0						)

           _lowerCamelCase			    =				{
               '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,
           }

           _lowerCamelCase			    =				PriorTransformer(**UpperCAmelCase_						)
           return model



     @property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           torch.manual_seed(0						)

           _lowerCamelCase			    =				{
               '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,
               ),
           }
           _lowerCamelCase			    =				ShapERenderer(**UpperCAmelCase_						)
           return model



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.dummy_prior
           _lowerCamelCase			    =				self.dummy_text_encoder
           _lowerCamelCase			    =				self.dummy_tokenizer
           _lowerCamelCase			    =				self.dummy_renderer

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

           return components



     def 		UpperCamelCase_						(					self				,				A_				,				A_=0						) ->				Optional[Any]:






           """simple docstring"""
           if str(UpperCAmelCase_						).startswith('''mps'''						):
                 _lowerCamelCase			    =				torch.manual_seed(UpperCAmelCase_						)
           else:
                 _lowerCamelCase			    =				torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)
           _lowerCamelCase			    =				{
               'prompt': 'horse',
               'generator': generator,
               'num_inference_steps': 1,
               'frame_size': 32,
               'output_type': 'np',
           }
           return inputs



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				'cpu'

           _lowerCamelCase			    =				self.get_dummy_components()

           _lowerCamelCase			    =				self.pipeline_class(**UpperCAmelCase_						)
           _lowerCamelCase			    =				pipe.to(UpperCAmelCase_						)

           pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

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

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

           _lowerCamelCase			    =				np.array(
               [
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
               ]						)

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



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           # 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						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				torch_device == 'cpu'
           _lowerCamelCase			    =				True

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





     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_dummy_components()
           _lowerCamelCase			    =				self.pipeline_class(**UpperCAmelCase_						)
           _lowerCamelCase			    =				pipe.to(UpperCAmelCase_						)
           pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

           _lowerCamelCase			    =				1
           _lowerCamelCase			    =				2

           _lowerCamelCase			    =				self.get_dummy_inputs(UpperCAmelCase_						)

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

           _lowerCamelCase			    =				pipe(**UpperCAmelCase_				,				num_images_per_prompt=UpperCAmelCase_						)[0]

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





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

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/shap_e/test_shap_e_np_out.npy'''						)
           _lowerCamelCase			    =				ShapEPipeline.from_pretrained('''openai/shap-e'''						)
           _lowerCamelCase			    =				pipe.to(UpperCAmelCase_						)
           pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

           _lowerCamelCase			    =				torch.Generator(device=UpperCAmelCase_						).manual_seed(0						)

           _lowerCamelCase			    =				pipe(
               '''a shark'''				,				generator=UpperCAmelCase_				,				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(UpperCAmelCase_				,				UpperCAmelCase_						)
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

     # Create two fuzzy sets by defining any membership function
     # (trapmf(), gbellmf(), gaussmf(), etc).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

     # Compute the different operations using inbuilt functions.
     snake_case__				= np.ones(75)
     snake_case__				= np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case__				= fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     snake_case__				= fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
     # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
     snake_case__				= young + middle_aged - (young * middle_aged)
     # 6. Algebraic Product = (µA(x) * µB(x))
     snake_case__				= young * middle_aged
     # 7. Bounded Sum = min[1,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

     # max-min composition
     # max-product composition

     # Plot each set A, set B and each operation result using plot() and subplot().
     from matplotlib import pyplot as plt

     plt.figure()

     plt.subplot(4, 3, 1)
     plt.plot(X, young)
     plt.title('Young')
     plt.grid(True)

     plt.subplot(4, 3, 2)
     plt.plot(X, middle_aged)
     plt.title('Middle aged')
     plt.grid(True)

     plt.subplot(4, 3, 3)
     plt.plot(X, union)
     plt.title('union')
     plt.grid(True)

     plt.subplot(4, 3, 4)
     plt.plot(X, intersection)
     plt.title('intersection')
     plt.grid(True)

     plt.subplot(4, 3, 5)
     plt.plot(X, complement_a)
     plt.title('complement_a')
     plt.grid(True)

     plt.subplot(4, 3, 6)
     plt.plot(X, difference)
     plt.title('difference a/b')
     plt.grid(True)

     plt.subplot(4, 3, 7)
     plt.plot(X, alg_sum)
     plt.title('alg_sum')
     plt.grid(True)

     plt.subplot(4, 3, 8)
     plt.plot(X, alg_product)
     plt.title('alg_product')
     plt.grid(True)

     plt.subplot(4, 3, 9)
     plt.plot(X, bdd_sum)
     plt.title('bdd_sum')
     plt.grid(True)

     plt.subplot(4, 3, 10)
     plt.plot(X, bdd_difference)
     plt.title('bdd_difference')
     plt.grid(True)

     plt.subplots_adjust(hspace=0.5)
     plt.show()