






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						def  UpperCamelCase__ (		self				):






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






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  1
												lowerCAmelCase__        =  3
												lowerCAmelCase__        =  (32, 32)

												lowerCAmelCase__        =  floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0				)				).to(_UpperCamelCase				)
												return image






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  UNetaDConditionModel(
												    block_out_channels=(32, 32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=7       ,  out_channels=4       ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')       ,  up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')       ,  cross_attention_dim=32       ,  attention_head_dim=8       ,  use_linear_projection=_UpperCamelCase       ,  only_cross_attention=(True, True, False)       ,  num_class_embeds=1_00       ,  )
												return model






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  AutoencoderKL(
												    block_out_channels=[32, 32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']       ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']       ,  latent_channels=4       ,  )
												return model






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.manual_seed(0				)
												lowerCAmelCase__        =  CLIPTextConfig(
												    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=10_00       ,  hidden_act='gelu'       ,  projection_dim=5_12       ,  )
												return CLIPTextModel(_UpperCamelCase				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'  # ensure determinism for the device-dependent torch.Generator
												lowerCAmelCase__        =  self.dummy_cond_unet_upscale
												lowerCAmelCase__        =  DDPMScheduler()
												lowerCAmelCase__        =  DDIMScheduler(prediction_type='v_prediction'				)
												lowerCAmelCase__        =  self.dummy_vae
												lowerCAmelCase__        =  self.dummy_text_encoder
												lowerCAmelCase__        =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

												lowerCAmelCase__        =  self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
												lowerCAmelCase__        =  Image.fromarray(np.uinta(_UpperCamelCase				)				).convert('RGB'				).resize((64, 64)				)

												# make sure here that pndm scheduler skips prk
												lowerCAmelCase__        =  StableDiffusionUpscalePipeline(
												    unet=_UpperCamelCase       ,  low_res_scheduler=_UpperCamelCase       ,  scheduler=_UpperCamelCase       ,  vae=_UpperCamelCase       ,  text_encoder=_UpperCamelCase       ,  tokenizer=_UpperCamelCase       ,  max_noise_level=3_50       ,  )
												lowerCAmelCase__        =  sd_pipe.to(_UpperCamelCase				)
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  'A painting of a squirrel eating a burger'
												lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(0				)
												lowerCAmelCase__        =  sd_pipe(
												    [prompt]       ,  image=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )

												lowerCAmelCase__        =  output.images

												lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(0				)
												lowerCAmelCase__        =  sd_pipe(
												    [prompt]       ,  image=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  return_dict=_UpperCamelCase       ,  )[0]

												lowerCAmelCase__        =  image[0, -3:, -3:, -1]
												lowerCAmelCase__        =  image_from_tuple[0, -3:, -3:, -1]

												lowerCAmelCase__        =  low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)
												lowerCAmelCase__        =  np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]				)

												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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  'cpu'  # ensure determinism for the device-dependent torch.Generator
												lowerCAmelCase__        =  self.dummy_cond_unet_upscale
												lowerCAmelCase__        =  DDPMScheduler()
												lowerCAmelCase__        =  DDIMScheduler(prediction_type='v_prediction'				)
												lowerCAmelCase__        =  self.dummy_vae
												lowerCAmelCase__        =  self.dummy_text_encoder
												lowerCAmelCase__        =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

												lowerCAmelCase__        =  self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
												lowerCAmelCase__        =  Image.fromarray(np.uinta(_UpperCamelCase				)				).convert('RGB'				).resize((64, 64)				)

												# make sure here that pndm scheduler skips prk
												lowerCAmelCase__        =  StableDiffusionUpscalePipeline(
												    unet=_UpperCamelCase       ,  low_res_scheduler=_UpperCamelCase       ,  scheduler=_UpperCamelCase       ,  vae=_UpperCamelCase       ,  text_encoder=_UpperCamelCase       ,  tokenizer=_UpperCamelCase       ,  max_noise_level=3_50       ,  )
												lowerCAmelCase__        =  sd_pipe.to(_UpperCamelCase				)
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  'A painting of a squirrel eating a burger'
												lowerCAmelCase__        =  sd_pipe(
												    2 * [prompt]       ,  image=2 * [low_res_image]       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												lowerCAmelCase__        =  output.images
												assert image.shape[0] == 2

												lowerCAmelCase__        =  torch.Generator(device=_UpperCamelCase				).manual_seed(0				)
												lowerCAmelCase__        =  sd_pipe(
												    [prompt]       ,  image=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_images_per_prompt=2       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												lowerCAmelCase__        =  output.images
												assert image.shape[0] == 2







						@unittest.skipIf(torch_device != 'cuda'       ,  'This test requires a GPU'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.dummy_cond_unet_upscale
												lowerCAmelCase__        =  DDPMScheduler()
												lowerCAmelCase__        =  DDIMScheduler(prediction_type='v_prediction'				)
												lowerCAmelCase__        =  self.dummy_vae
												lowerCAmelCase__        =  self.dummy_text_encoder
												lowerCAmelCase__        =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

												lowerCAmelCase__        =  self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1				)[0]
												lowerCAmelCase__        =  Image.fromarray(np.uinta(_UpperCamelCase				)				).convert('RGB'				).resize((64, 64)				)

												# put models in fp16, except vae as it overflows in fp16
												lowerCAmelCase__        =  unet.half()
												lowerCAmelCase__        =  text_encoder.half()

												# make sure here that pndm scheduler skips prk
												lowerCAmelCase__        =  StableDiffusionUpscalePipeline(
												    unet=_UpperCamelCase       ,  low_res_scheduler=_UpperCamelCase       ,  scheduler=_UpperCamelCase       ,  vae=_UpperCamelCase       ,  text_encoder=_UpperCamelCase       ,  tokenizer=_UpperCamelCase       ,  max_noise_level=3_50       ,  )
												lowerCAmelCase__        =  sd_pipe.to(_UpperCamelCase				)
												sd_pipe.set_progress_bar_config(disable=_UpperCamelCase				)

												lowerCAmelCase__        =  'A painting of a squirrel eating a burger'
												lowerCAmelCase__        =  torch.manual_seed(0				)
												lowerCAmelCase__        =  sd_pipe(
												    [prompt]       ,  image=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=2       ,  output_type='np'       ,  ).images

												lowerCAmelCase__        =  low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)




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






						def  UpperCamelCase__ (		self				):






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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png'				)
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat.npy'				)

												lowerCAmelCase__        =  'stabilityai/stable-diffusion-x4-upscaler'
												lowerCAmelCase__        =  StableDiffusionUpscalePipeline.from_pretrained(_UpperCamelCase				)
												pipe.to(_UpperCamelCase				)
												pipe.set_progress_bar_config(disable=_UpperCamelCase				)
												pipe.enable_attention_slicing()

												lowerCAmelCase__        =  'a cat sitting on a park bench'

												lowerCAmelCase__        =  torch.manual_seed(0				)
												lowerCAmelCase__        =  pipe(
												    prompt=_UpperCamelCase       ,  image=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  output_type='np'       ,  )
												lowerCAmelCase__        =  output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 1E-3






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png'				)
												lowerCAmelCase__        =  load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat_fp16.npy'				)

												lowerCAmelCase__        =  'stabilityai/stable-diffusion-x4-upscaler'
												lowerCAmelCase__        =  StableDiffusionUpscalePipeline.from_pretrained(
												    _UpperCamelCase       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(_UpperCamelCase				)
												pipe.set_progress_bar_config(disable=_UpperCamelCase				)
												pipe.enable_attention_slicing()

												lowerCAmelCase__        =  'a cat sitting on a park bench'

												lowerCAmelCase__        =  torch.manual_seed(0				)
												lowerCAmelCase__        =  pipe(
												    prompt=_UpperCamelCase       ,  image=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  output_type='np'       ,  )
												lowerCAmelCase__        =  output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image				).max() < 5E-1







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowerCAmelCase__        =  load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png'				)

												lowerCAmelCase__        =  'stabilityai/stable-diffusion-x4-upscaler'
												lowerCAmelCase__        =  StableDiffusionUpscalePipeline.from_pretrained(
												    _UpperCamelCase       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(_UpperCamelCase				)
												pipe.set_progress_bar_config(disable=_UpperCamelCase				)
												pipe.enable_attention_slicing(1				)
												pipe.enable_sequential_cpu_offload()

												lowerCAmelCase__        =  'a cat sitting on a park bench'

												lowerCAmelCase__        =  torch.manual_seed(0				)
												lowerCAmelCase__        =  pipe(
												    prompt=_UpperCamelCase       ,  image=_UpperCamelCase       ,  generator=_UpperCamelCase       ,  num_inference_steps=5       ,  output_type='np'       ,  )

												lowerCAmelCase__        =  torch.cuda.max_memory_allocated()
												# make sure that less than 2.9 GB is allocated
												assert mem_bytes < 2.9 * 10**9













# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''microsoft/speecht5_tts'''
						_SCREAMING_SNAKE_CASE		:    Any						=   (
						    '''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
						    '''text to read (in English) and returns a waveform object containing the sound.'''
						)
						_SCREAMING_SNAKE_CASE		:    int						=   '''text_reader'''
						_SCREAMING_SNAKE_CASE		:    List[str]						=   SpeechTaProcessor
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaForTextToSpeech
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   SpeechTaHifiGan

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   ['''text''']
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''audio''']






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if self.post_processor is None:
																		lowerCAmelCase__        =  'microsoft/speecht5_hifigan'
												super().setup()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=None				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.pre_processor(text=_UpperCamelCase       ,  return_tensors='pt'       ,  truncation=_UpperCamelCase				)

												if speaker_embeddings is None:
																		if not is_datasets_available():
																								raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'				)

																		lowerCAmelCase__        =  load_dataset('Matthijs/cmu-arctic-xvectors'       ,  split='validation'				)
																		lowerCAmelCase__        =  torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

												return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.model.generate_speech(**_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.post_processor(_UpperCamelCase				).cpu().detach()





