


from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def 						lowercase_ (				self   ,						__lowercase							)					->    float:
										return 0.0






def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> tuple[int | float, int | float]:
				lowerCAmelCase_ :      int    				=				min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
				lowerCAmelCase_ :      str    				=				max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
				return lowest, highest



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> None:
				lowerCAmelCase_ :      List[Any]    				=				512
				lowerCAmelCase_ :      Any    				=				[1] + [0] * (size - 1)
				lowerCAmelCase_ :      Optional[int]    				=				[filter_type.process(lowerCAmelCase_ ) for item in inputs]

				lowerCAmelCase_ :      int    				=				[0] * (samplerate - size)  # zero-padding
				outputs += filler
				lowerCAmelCase_ :      Tuple    				=				np.abs(np.fft.fft(lowerCAmelCase_ ) )
				lowerCAmelCase_ :      Any    				=				20 * np.logaa(lowerCAmelCase_ )

				# 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
				lowerCAmelCase_ :      Dict    				=				get_bounds(lowerCAmelCase_ ,   lowerCAmelCase_ )
				plt.ylim(max([-80, bounds[0]] ) ,   min([80, bounds[1]] ) )
				plt.ylabel('''Gain (dB)''' )

				plt.plot(lowerCAmelCase_ )
				plt.show()




def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> None:
				lowerCAmelCase_ :      Union[str, Any]    				=				512
				lowerCAmelCase_ :      Optional[Any]    				=				[1] + [0] * (size - 1)
				lowerCAmelCase_ :      Optional[Any]    				=				[filter_type.process(lowerCAmelCase_ ) for item in inputs]

				lowerCAmelCase_ :      Optional[Any]    				=				[0] * (samplerate - size)  # zero-padding
				outputs += filler
				lowerCAmelCase_ :      Dict    				=				np.angle(np.fft.fft(lowerCAmelCase_ ) )

				# 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(lowerCAmelCase_ ,   -2 * pi ) )
				plt.show()



import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_UpperCAmelCase			:  Any     =False







class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def 						lowercase_ (				self							)					->    Optional[int]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						@property
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return 1_2




						@property
						def 						lowercase_ (				self							)					->    Any:
										return 1_2




						@property
						def 						lowercase_ (				self							)					->    Optional[Any]:
										return 3_2




						@property
						def 						lowercase_ (				self							)					->    int:
										torch.manual_seed(0							)
										lowerCAmelCase_ :      Any    				=				VQModel(
										    block_out_channels=[3_2, 6_4]   ,						in_channels=3   ,						out_channels=3   ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   ,						latent_channels=3   ,						num_vq_embeddings=self.num_embed   ,						vq_embed_dim=3   ,						)
										return model




						@property
						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      Optional[Any]    				=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)
										return tokenizer




						@property
						def 						lowercase_ (				self							)					->    int:
										torch.manual_seed(0							)
										lowerCAmelCase_ :      Union[str, Any]    				=				CLIPTextConfig(
										    bos_token_id=0   ,						eos_token_id=2   ,						hidden_size=self.text_embedder_hidden_size   ,						intermediate_size=3_7   ,						layer_norm_eps=1e-05   ,						num_attention_heads=4   ,						num_hidden_layers=5   ,						pad_token_id=1   ,						vocab_size=1_0_0_0   ,						)
										return CLIPTextModel(__lowercase							)




						@property
						def 						lowercase_ (				self							)					->    List[str]:
										torch.manual_seed(0							)

										lowerCAmelCase_ :      Union[str, Any]    				=				1_2
										lowerCAmelCase_ :      int    				=				1_2

										lowerCAmelCase_ :      Union[str, Any]    				=				{
										    '''attention_bias''': True,
										    '''cross_attention_dim''': 3_2,
										    '''attention_head_dim''': height * width,
										    '''num_attention_heads''': 1,
										    '''num_vector_embeds''': self.num_embed,
										    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
										    '''norm_num_groups''': 3_2,
										    '''sample_size''': width,
										    '''activation_fn''': '''geglu-approximate''',
										}

										lowerCAmelCase_ :      List[str]    				=				TransformeraDModel(**__lowercase							)
										return model




						def 						lowercase_ (				self							)					->    str:
										lowerCAmelCase_ :      List[Any]    				=				'''cpu'''

										lowerCAmelCase_ :      Any    				=				self.dummy_vqvae
										lowerCAmelCase_ :      str    				=				self.dummy_text_encoder
										lowerCAmelCase_ :      Union[str, Any]    				=				self.dummy_tokenizer
										lowerCAmelCase_ :      int    				=				self.dummy_transformer
										lowerCAmelCase_ :      List[str]    				=				VQDiffusionScheduler(self.num_embed							)
										lowerCAmelCase_ :      Union[str, Any]    				=				LearnedClassifierFreeSamplingEmbeddings(learnable=__lowercase							)

										lowerCAmelCase_ :      Dict    				=				VQDiffusionPipeline(
										    vqvae=__lowercase   ,						text_encoder=__lowercase   ,						tokenizer=__lowercase   ,						transformer=__lowercase   ,						scheduler=__lowercase   ,						learned_classifier_free_sampling_embeddings=__lowercase   ,						)
										lowerCAmelCase_ :      int    				=				pipe.to(__lowercase							)
										pipe.set_progress_bar_config(disable=__lowercase							)

										lowerCAmelCase_ :      Any    				=				'''teddy bear playing in the pool'''

										lowerCAmelCase_ :      int    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      Tuple    				=				pipe([prompt]   ,						generator=__lowercase   ,						num_inference_steps=2   ,						output_type='''np'''							)
										lowerCAmelCase_ :      Union[str, Any]    				=				output.images

										lowerCAmelCase_ :      List[Any]    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      List[Any]    				=				pipe(
										    [prompt]   ,						generator=__lowercase   ,						output_type='''np'''   ,						return_dict=__lowercase   ,						num_inference_steps=2							)[0]

										lowerCAmelCase_ :      List[str]    				=				image[0, -3:, -3:, -1]
										lowerCAmelCase_ :      str    				=				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 2_4, 2_4, 3)

										lowerCAmelCase_ :      Optional[int]    				=				np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]							)

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




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_ :      Optional[Any]    				=				'''cpu'''

										lowerCAmelCase_ :      str    				=				self.dummy_vqvae
										lowerCAmelCase_ :      Dict    				=				self.dummy_text_encoder
										lowerCAmelCase_ :      List[Any]    				=				self.dummy_tokenizer
										lowerCAmelCase_ :      Union[str, Any]    				=				self.dummy_transformer
										lowerCAmelCase_ :      Tuple    				=				VQDiffusionScheduler(self.num_embed							)
										lowerCAmelCase_ :      str    				=				LearnedClassifierFreeSamplingEmbeddings(
										    learnable=__lowercase   ,						hidden_size=self.text_embedder_hidden_size   ,						length=tokenizer.model_max_length							)

										lowerCAmelCase_ :      List[str]    				=				VQDiffusionPipeline(
										    vqvae=__lowercase   ,						text_encoder=__lowercase   ,						tokenizer=__lowercase   ,						transformer=__lowercase   ,						scheduler=__lowercase   ,						learned_classifier_free_sampling_embeddings=__lowercase   ,						)
										lowerCAmelCase_ :      Union[str, Any]    				=				pipe.to(__lowercase							)
										pipe.set_progress_bar_config(disable=__lowercase							)

										lowerCAmelCase_ :      Any    				=				'''teddy bear playing in the pool'''

										lowerCAmelCase_ :      List[str]    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      Dict    				=				pipe([prompt]   ,						generator=__lowercase   ,						num_inference_steps=2   ,						output_type='''np'''							)
										lowerCAmelCase_ :      str    				=				output.images

										lowerCAmelCase_ :      List[Any]    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      Union[str, Any]    				=				pipe(
										    [prompt]   ,						generator=__lowercase   ,						output_type='''np'''   ,						return_dict=__lowercase   ,						num_inference_steps=2							)[0]

										lowerCAmelCase_ :      List[str]    				=				image[0, -3:, -3:, -1]
										lowerCAmelCase_ :      str    				=				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 2_4, 2_4, 3)

										lowerCAmelCase_ :      Union[str, Any]    				=				np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]							)

										assert np.abs(image_slice.flatten() - expected_slice							).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2







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







						'''simple docstring'''




						def 						lowercase_ (				self							)					->    Optional[int]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Tuple    				=				load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''							)

										lowerCAmelCase_ :      str    				=				VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''							)
										lowerCAmelCase_ :      List[Any]    				=				pipeline.to(__lowercase							)
										pipeline.set_progress_bar_config(disable=__lowercase							)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										lowerCAmelCase_ :      List[Any]    				=				torch.Generator(device=__lowercase							).manual_seed(0							)
										lowerCAmelCase_ :      Optional[int]    				=				pipeline(
										    '''teddy bear playing in the pool'''   ,						num_images_per_prompt=1   ,						generator=__lowercase   ,						output_type='''np'''   ,						)

										lowerCAmelCase_ :      Union[str, Any]    				=				output.images[0]

										assert image.shape == (2_5_6, 2_5_6, 3)
										assert np.abs(expected_image - image							).max() < 2.0