





"""simple docstring"""



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def 		__lowercase     (   snake_case_  :  int						)   ->List[Any]:



    '''simple docstring'''







    __A ,       __A    :						Optional[int]						 =     image.size
    __A ,       __A    :						Tuple						 =     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    __A    :						Optional[Any]						 =     image.resize((w, h)				,resample=PIL_INTERPOLATION['''lanczos''']						)
    __A    :						List[str]						 =     np.array(snake_case_						).astype(np.floataa						) / 255.0
    __A    :						Union[str, Any]						 =     image[None].transpose(0				,3				,1				,2						)
    __A    :						List[str]						 =     torch.from_numpy(snake_case_						)
    return 2.0 * image - 1.0






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__()
     self.register_modules(vqvae=__lowerCamelCase					,						unet=__lowerCamelCase					,						scheduler=__lowerCamelCase  )







 @torch.no_grad()
 def __call__(    self					,						__lowerCamelCase = None					,						__lowerCamelCase = 1					,						__lowerCamelCase = 100					,						__lowerCamelCase = 0.0					,						__lowerCamelCase = None					,						__lowerCamelCase = "pil"					,						__lowerCamelCase = True					,						):



     '''simple docstring'''




     if isinstance(__lowerCamelCase					,						PIL.Image.Image  ):
         __A    :						Tuple						 =     1
     elif isinstance(__lowerCamelCase					,						torch.Tensor  ):
         __A    :						Optional[Any]						 =     image.shape[0]
     else:
         raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase  )}"""  )

     if isinstance(__lowerCamelCase					,						PIL.Image.Image  ):
         __A    :						Dict						 =     preprocess(__lowerCamelCase  )

     __A ,       __A    :						str						 =     image.shape[-2:]

     # in_channels should be 6: 3 for latents, 3 for low resolution image
     __A    :						Tuple						 =     (batch_size, self.unet.config.in_channels // 2, height, width)
     __A    :						List[Any]						 =     next(self.unet.parameters()  ).dtype

     __A    :						Tuple						 =     randn_tensor(__lowerCamelCase					,						generator=__lowerCamelCase					,						device=self.device					,						dtype=__lowerCamelCase  )

     __A    :						Optional[Any]						 =     image.to(device=self.device					,						dtype=__lowerCamelCase  )

     # set timesteps and move to the correct device
     self.scheduler.set_timesteps(__lowerCamelCase					,						device=self.device  )
     __A    :						Union[str, Any]						 =     self.scheduler.timesteps

     # scale the initial noise by the standard deviation required by the scheduler
     __A    :						List[str]						 =     latents * self.scheduler.init_noise_sigma

     # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
     # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
     # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
     # and should be between [0, 1]
     __A    :						Union[str, Any]						 =     '''eta''' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
     __A    :						Union[str, Any]						 =     {}
     if accepts_eta:
         __A    :						Tuple						 =     eta

     for t in self.progress_bar(__lowerCamelCase  ):
         # concat latents and low resolution image in the channel dimension.
         __A    :						List[str]						 =     torch.cat([latents, image]					,						dim=1  )
         __A    :						List[Any]						 =     self.scheduler.scale_model_input(__lowerCamelCase					,						__lowerCamelCase  )
         # predict the noise residual
         __A    :						Optional[int]						 =     self.unet(__lowerCamelCase					,						__lowerCamelCase  ).sample
         # compute the previous noisy sample x_t -> x_t-1
         __A    :						int						 =     self.scheduler.step(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						**__lowerCamelCase  ).prev_sample

     # decode the image latents with the VQVAE
     __A    :						Any						 =     self.vqvae.decode(__lowerCamelCase  ).sample
     __A    :						Any						 =     torch.clamp(__lowerCamelCase					,						-1.0					,						1.0  )
     __A    :						Optional[int]						 =     image / 2 + 0.5
     __A    :						List[Any]						 =     image.cpu().permute(0					,						2					,						3					,						1  ).numpy()

     if output_type == "pil":
         __A    :						Optional[Any]						 =     self.numpy_to_pil(__lowerCamelCase  )

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase  )











"""simple docstring"""



# 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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """microsoft/speecht5_tts"""
 _lowerCamelCase        =     (
     """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."""
 )
 _lowerCamelCase        =     """text_reader"""
 _lowerCamelCase        =     SpeechTaProcessor
 _lowerCamelCase        =     SpeechTaForTextToSpeech
 _lowerCamelCase        =     SpeechTaHifiGan

 _lowerCamelCase        =     ["""text"""]
 _lowerCamelCase        =     ["""audio"""]

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.post_processor is None:
         __A    :						List[str]						 =     '''microsoft/speecht5_hifigan'''
     super().setup()

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



     '''simple docstring'''




     __A    :						int						 =     self.pre_processor(text=__lowerCamelCase					,						return_tensors='''pt'''					,						truncation=__lowerCamelCase  )

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

         __A    :						List[Any]						 =     load_dataset('''Matthijs/cmu-arctic-xvectors'''					,						split='''validation'''  )
         __A    :						int						 =     torch.tensor(embeddings_dataset[7305]['''xvector''']  ).unsqueeze(0  )

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.model.generate_speech(**__lowerCamelCase  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.post_processor(__lowerCamelCase  ).cpu().detach()




