




"""simple docstring"""




import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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     UpperCamelCase_   (			unittest.TestCase					):

    '''simple docstring'''






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





           '''simple docstring'''

           super().tearDown()
           gc.collect()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->int:





           '''simple docstring'''

           A__			,							A__							=   FlaxControlNetModel.from_pretrained(
               '''lllyasviel/sd-controlnet-canny''' ,			from_pt=UpperCAmelCase__ ,			dtype=jnp.bfloataa)
           A__			,							A__							=   FlaxStableDiffusionControlNetPipeline.from_pretrained(
               '''runwayml/stable-diffusion-v1-5''' ,			controlnet=UpperCAmelCase__ ,			from_pt=UpperCAmelCase__ ,			dtype=jnp.bfloataa)
           A__							=   controlnet_params

           A__							=   '''bird'''
           A__							=   jax.device_count()
           A__							=   pipe.prepare_text_inputs([prompts] * num_samples)

           A__							=   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''')
           A__							=   pipe.prepare_image_inputs([canny_image] * num_samples)

           A__							=   jax.random.PRNGKey(0)
           A__							=   jax.random.split(UpperCAmelCase__ ,			jax.device_count())

           A__							=   replicate(UpperCAmelCase__)
           A__							=   shard(UpperCAmelCase__)
           A__							=   shard(UpperCAmelCase__)

           A__							=   pipe(
               prompt_ids=UpperCAmelCase__ ,			image=UpperCAmelCase__ ,			params=UpperCAmelCase__ ,			prng_seed=UpperCAmelCase__ ,			num_inference_steps=50 ,			jit=UpperCAmelCase__ ,			).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           A__							=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
           A__							=   images[0, 253:256, 253:256, -1]

           A__							=   jnp.asarray(jax.device_get(image_slice.flatten()))
           A__							=   jnp.array(
               [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078])
           print(f"""output_slice: {output_slice}""")
           assert jnp.abs(output_slice - expected_slice).max() < 1e-2





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

           A__			,							A__							=   FlaxControlNetModel.from_pretrained(
               '''lllyasviel/sd-controlnet-openpose''' ,			from_pt=UpperCAmelCase__ ,			dtype=jnp.bfloataa)
           A__			,							A__							=   FlaxStableDiffusionControlNetPipeline.from_pretrained(
               '''runwayml/stable-diffusion-v1-5''' ,			controlnet=UpperCAmelCase__ ,			from_pt=UpperCAmelCase__ ,			dtype=jnp.bfloataa)
           A__							=   controlnet_params

           A__							=   '''Chef in the kitchen'''
           A__							=   jax.device_count()
           A__							=   pipe.prepare_text_inputs([prompts] * num_samples)

           A__							=   load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png''')
           A__							=   pipe.prepare_image_inputs([pose_image] * num_samples)

           A__							=   jax.random.PRNGKey(0)
           A__							=   jax.random.split(UpperCAmelCase__ ,			jax.device_count())

           A__							=   replicate(UpperCAmelCase__)
           A__							=   shard(UpperCAmelCase__)
           A__							=   shard(UpperCAmelCase__)

           A__							=   pipe(
               prompt_ids=UpperCAmelCase__ ,			image=UpperCAmelCase__ ,			params=UpperCAmelCase__ ,			prng_seed=UpperCAmelCase__ ,			num_inference_steps=50 ,			jit=UpperCAmelCase__ ,			).images
           assert images.shape == (jax.device_count(), 1, 768, 512, 3)

           A__							=   images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
           A__							=   images[0, 253:256, 253:256, -1]

           A__							=   jnp.asarray(jax.device_get(image_slice.flatten()))
           A__							=   jnp.array(
               [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]])
           print(f"""output_slice: {output_slice}""")
           assert jnp.abs(output_slice - expected_slice).max() < 1e-2








from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     float)						->float:





           '''simple docstring'''

           return 0.0







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	tuple[int | float, int | float]:



       """simple docstring"""

       A__							=   min([-20, np.min(fft_results[1 : samplerate // 2 - 1]						)]						)
       A__							=   max([20, np.max(fft_results[1 : samplerate // 2 - 1]						)]						)
       return lowest, highest







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.abs(np.fft.fft(lowercase_						)						)
       A__							=   20 * np.logaa(lowercase_						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       # Display within reasonable bounds
       A__							=   get_bounds(lowercase_			,    lowercase_						)
       plt.ylim(max([-80, bounds[0]]						)			,    min([80, bounds[1]]						)						)
       plt.ylabel('''Gain (dB)'''						)

       plt.plot(lowercase_						)
       plt.show()







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	None:



       """simple docstring"""

       A__							=   512
       A__							=   [1] + [0] * (size - 1)
       A__							=   [filter_type.process(lowercase_						) for item in inputs]

       A__							=   [0] * (samplerate - size)  # zero-padding
       outputs += filler
       A__							=   np.angle(np.fft.fft(lowercase_						)						)

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(24			,    samplerate / 2 - 1						)
       plt.xlabel('''Frequency (Hz)'''						)
       plt.xscale('''log'''						)

       plt.ylim(-2 * pi			,    2 * pi						)
       plt.ylabel('''Phase shift (Radians)'''						)
       plt.plot(np.unwrap(lowercase_			,    -2 * pi						)						)
       plt.show()


