






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






class 				a_						(			SCREAMING_SNAKE_CASE__				):

  def __init__(      self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE			,		)  ->      str:



        """simple docstring"""






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


  @torch.no_grad()
  def __call__(      self			,		_SCREAMING_SNAKE_CASE = None			,		_SCREAMING_SNAKE_CASE = 1			,		_SCREAMING_SNAKE_CASE = 100			,		_SCREAMING_SNAKE_CASE = 0.0			,		_SCREAMING_SNAKE_CASE = None			,		_SCREAMING_SNAKE_CASE = "pil"			,		_SCREAMING_SNAKE_CASE = True			,		)  ->      Dict:



        """simple docstring"""






        if isinstance(__lowerCamelCase			,		PIL.Image.Image      ):
              UpperCamelCase				  =					1
        elif isinstance(__lowerCamelCase			,		torch.Tensor      ):
              UpperCamelCase				  =					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      ):
              UpperCamelCase				  =					preprocess(__lowerCamelCase      )

        UpperCamelCase				  =					image.shape[-2:]

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

        UpperCamelCase				  =					randn_tensor(__lowerCamelCase			,		generator=__lowerCamelCase			,		device=self.device			,		dtype=__lowerCamelCase      )

        UpperCamelCase				  =					image.to(device=self.device			,		dtype=__lowerCamelCase      )

        # set timesteps and move to the correct device
        self.scheduler.set_timesteps(__lowerCamelCase			,		device=self.device      )
        UpperCamelCase				  =					self.scheduler.timesteps

        # scale the initial noise by the standard deviation required by the scheduler
        UpperCamelCase				  =					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]
        UpperCamelCase				  =					'''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
        UpperCamelCase				  =					{}
        if accepts_eta:
              UpperCamelCase				  =					eta

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

        # decode the image latents with the VQVAE
        UpperCamelCase				  =					self.vqvae.decode(__lowerCamelCase      ).sample
        UpperCamelCase				  =					torch.clamp(__lowerCamelCase			,		-1.0			,		1.0      )
        UpperCamelCase				  =					image / 2 + 0.5
        UpperCamelCase				  =					image.cpu().permute(0			,		2			,		3			,		1      ).numpy()

        if output_type == "pil":
              UpperCamelCase				  =					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()




