

"""simple docstring"""




import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard





@slow
@require_flax
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     int:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   FlaxStableDiffusionPipeline.from_pretrained(
						    """stabilityai/stable-diffusion-2"""     ,					revision="""bf16"""     ,					dtype=jnp.bfloataa     ,					)

						SCREAMING_SNAKE_CASE__										=   """A painting of a squirrel eating a burger"""
						SCREAMING_SNAKE_CASE__										=   jax.device_count()
						SCREAMING_SNAKE_CASE__										=   num_samples * [prompt]
						SCREAMING_SNAKE_CASE__										=   sd_pipe.prepare_inputs(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   replicate(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   shard(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   jax.random.PRNGKey(0							)
						SCREAMING_SNAKE_CASE__										=   jax.random.split(__UpperCAmelCase     ,					jax.device_count()							)

						SCREAMING_SNAKE_CASE__										=   sd_pipe(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					num_inference_steps=2_5     ,					jit=__UpperCAmelCase							)[0]
						assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

						SCREAMING_SNAKE_CASE__										=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
						SCREAMING_SNAKE_CASE__										=   images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						SCREAMING_SNAKE_CASE__										=   jnp.asarray(jax.device_get(image_slice.flatten()							)							)
						SCREAMING_SNAKE_CASE__										=   jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
						print(F"""output_slice: {output_slice}"""							)
						assert jnp.abs(output_slice - expected_slice							).max() < 1e-2





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   """stabilityai/stable-diffusion-2"""
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   FlaxDPMSolverMultistepScheduler.from_pretrained(__UpperCAmelCase     ,					subfolder="""scheduler"""							)
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   FlaxStableDiffusionPipeline.from_pretrained(
						    __UpperCAmelCase     ,					scheduler=__UpperCAmelCase     ,					revision="""bf16"""     ,					dtype=jnp.bfloataa     ,					)
						SCREAMING_SNAKE_CASE__										=   scheduler_params

						SCREAMING_SNAKE_CASE__										=   """A painting of a squirrel eating a burger"""
						SCREAMING_SNAKE_CASE__										=   jax.device_count()
						SCREAMING_SNAKE_CASE__										=   num_samples * [prompt]
						SCREAMING_SNAKE_CASE__										=   sd_pipe.prepare_inputs(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   replicate(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   shard(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   jax.random.PRNGKey(0							)
						SCREAMING_SNAKE_CASE__										=   jax.random.split(__UpperCAmelCase     ,					jax.device_count()							)

						SCREAMING_SNAKE_CASE__										=   sd_pipe(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					num_inference_steps=2_5     ,					jit=__UpperCAmelCase							)[0]
						assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

						SCREAMING_SNAKE_CASE__										=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
						SCREAMING_SNAKE_CASE__										=   images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						SCREAMING_SNAKE_CASE__										=   jnp.asarray(jax.device_get(image_slice.flatten()							)							)
						SCREAMING_SNAKE_CASE__										=   jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
						print(F"""output_slice: {output_slice}"""							)
						assert jnp.abs(output_slice - expected_slice							).max() < 1e-2





"""simple docstring"""




import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
A_				:      int           =			pd.read_csv(
    "https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"
    "position_salaries.csv"
)
A_				:      int           =			dataset.iloc[:, 1:2].values
A_				:      Optional[Any]           =			dataset.iloc[:, 2].values


A_       ,						A_       ,						A_       ,						A_				:      str           =			train_test_split(X, y, test_size=0.2, random_state=0)


A_				:      Optional[Any]           =			PolynomialFeatures(degree=4)
A_				:      Tuple           =			poly_reg.fit_transform(X)
A_				:      str           =			LinearRegression()
pol_reg.fit(X_poly, y)

def 						A							(  ):




			'''simple docstring'''


			plt.scatter(snake_case__		,					snake_case__		,					color="""red"""     )
			plt.plot(snake_case__		,					pol_reg.predict(poly_reg.fit_transform(snake_case__     )     )		,					color="""blue"""     )
			plt.title("""Truth or Bluff (Linear Regression)"""     )
			plt.xlabel("""Position level"""     )
			plt.ylabel("""Salary"""     )
			plt.show()


if __name__ == "__main__":
						viz_polymonial()

						# Predicting a new result with Polymonial Regression
						pol_reg.predict(poly_reg.fit_transform([[5.5]]))
						# output should be 132148.43750003


