






'''simple docstring'''



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=(DDIMParallelScheduler,)
      __a  					=(("eta", 0.0), ("num_inference_steps", 50))




      def  __UpperCamelCase							(  self ,   **lowerCamelCase      )							->int:



         '''simple docstring'''
         __a				     =    {
             'num_train_timesteps': 1000,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'clip_sample': True,
         }

         config.update(**lowerCamelCase      )
         return config




      def  __UpperCamelCase							(  self ,   **lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    self.scheduler_classes[0]
         __a				     =    self.get_scheduler_config(**lowerCamelCase      )
         __a				     =    scheduler_class(**lowerCamelCase      )

         __a     ,			__a				     =    10, 0.0

         __a				     =    self.dummy_model()
         __a				     =    self.dummy_sample_deter

         scheduler.set_timesteps(lowerCamelCase      )

         for t in scheduler.timesteps:
            __a				     =    model(lowerCamelCase ,   lowerCamelCase      )
            __a				     =    scheduler.step(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      ).prev_sample

         return sample




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=lowerCamelCase      )

         __a				     =    self.scheduler_classes[0]
         __a				     =    self.get_scheduler_config(steps_offset=1      )
         __a				     =    scheduler_class(**lowerCamelCase      )
         scheduler.set_timesteps(5      )
         assert torch.equal(scheduler.timesteps ,   torch.LongTensor([801, 601, 401, 201, 1]      )      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,   [0.002, 0.02, 0.2, 2]      ):
            self.check_over_configs(beta_start=lowerCamelCase ,   beta_end=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=lowerCamelCase      )




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



         '''simple docstring'''
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=lowerCamelCase      )




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



         '''simple docstring'''
         for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=lowerCamelCase      )




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



         '''simple docstring'''
         for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         self.check_over_configs(thresholding=lowerCamelCase      )
         for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(
                   thresholding=lowerCamelCase ,   prediction_type=lowerCamelCase ,   sample_max_value=lowerCamelCase ,   )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         for t in [1, 10, 49]:
            self.check_over_forward(time_step=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         for t, num_inference_steps in zip([1, 10, 50] ,   [10, 50, 500]      ):
            self.check_over_forward(time_step=lowerCamelCase ,   num_inference_steps=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         for t, eta in zip([1, 10, 49] ,   [0.0, 0.5, 1.0]      ):
            self.check_over_forward(time_step=lowerCamelCase ,   eta=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    self.scheduler_classes[0]
         __a				     =    self.get_scheduler_config()
         __a				     =    scheduler_class(**lowerCamelCase      )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ,   0      ) - 0.0      )      ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420 ,   400      ) - 0.14771      )      ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980 ,   960      ) - 0.32460      )      ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0 ,   0      ) - 0.0      )      ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ,   486      ) - 0.00979      )      ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ,   998      ) - 0.02      )      ) < 1e-5




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    self.scheduler_classes[0]
         __a				     =    self.get_scheduler_config()
         __a				     =    scheduler_class(**lowerCamelCase      )

         __a     ,			__a				     =    10, 0.0
         scheduler.set_timesteps(lowerCamelCase      )

         __a				     =    self.dummy_model()
         __a				     =    self.dummy_sample_deter
         __a				     =    self.dummy_sample_deter + 0.1
         __a				     =    self.dummy_sample_deter - 0.1

         __a				     =    samplea.shape[0]
         __a				     =    torch.stack([samplea, samplea, samplea] ,   dim=0      )
         __a				     =    torch.arange(lowerCamelCase      )[0:3, None].repeat(1 ,   lowerCamelCase      )

         __a				     =    model(samples.flatten(0 ,   1      ) ,   timesteps.flatten(0 ,   1      )      )
         __a				     =    scheduler.batch_step_no_noise(lowerCamelCase ,   timesteps.flatten(0 ,   1      ) ,   samples.flatten(0 ,   1      ) ,   lowerCamelCase      )

         __a				     =    torch.sum(torch.abs(lowerCamelCase      )      )
         __a				     =    torch.mean(torch.abs(lowerCamelCase      )      )

         assert abs(result_sum.item() - 1147.7904      ) < 1e-2
         assert abs(result_mean.item() - 0.4982      ) < 1e-3




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         __a				     =    self.full_loop()

         __a				     =    torch.sum(torch.abs(lowerCamelCase      )      )
         __a				     =    torch.mean(torch.abs(lowerCamelCase      )      )

         assert abs(result_sum.item() - 172.0067      ) < 1e-2
         assert abs(result_mean.item() - 0.223967      ) < 1e-3




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    self.full_loop(prediction_type='v_prediction'      )

         __a				     =    torch.sum(torch.abs(lowerCamelCase      )      )
         __a				     =    torch.mean(torch.abs(lowerCamelCase      )      )

         assert abs(result_sum.item() - 52.5302      ) < 1e-2
         assert abs(result_mean.item() - 0.0684      ) < 1e-3




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         # We specify different beta, so that the first alpha is 0.99
         __a				     =    self.full_loop(set_alpha_to_one=lowerCamelCase ,   beta_start=0.01      )
         __a				     =    torch.sum(torch.abs(lowerCamelCase      )      )
         __a				     =    torch.mean(torch.abs(lowerCamelCase      )      )

         assert abs(result_sum.item() - 149.8295      ) < 1e-2
         assert abs(result_mean.item() - 0.1951      ) < 1e-3






      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         # We specify different beta, so that the first alpha is 0.99
         __a				     =    self.full_loop(set_alpha_to_one=lowerCamelCase ,   beta_start=0.01      )
         __a				     =    torch.sum(torch.abs(lowerCamelCase      )      )
         __a				     =    torch.mean(torch.abs(lowerCamelCase      )      )

         assert abs(result_sum.item() - 149.0784      ) < 1e-2
         assert abs(result_mean.item() - 0.1941      ) < 1e-3







'''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 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="microsoft/speecht5_tts"
      __a  					=(
          "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."
      )
      __a  					="text_reader"
      __a  					=SpeechTaProcessor
      __a  					=SpeechTaForTextToSpeech
      __a  					=SpeechTaHifiGan

      __a  					=["text"]
      __a  					=["audio"]




      def  __UpperCamelCase							(  self      )							->str:



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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=None      )							->str:



         '''simple docstring'''
         __a				     =    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				     =    load_dataset('Matthijs/cmu-arctic-xvectors' ,   split='validation'      )
            __a				     =    torch.tensor(embeddings_dataset[7305]['xvector']      ).unsqueeze(0      )

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




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.model.generate_speech(**lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.post_processor(lowerCamelCase      ).cpu().detach()