





"""simple docstring"""



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     (DDPMParallelScheduler,)

 def 					UpperCamelCase__(    self					,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						int						 =     {
         '''num_train_timesteps''': 1000,
         '''beta_start''': 0.0_0_0_1,
         '''beta_end''': 0.0_2,
         '''beta_schedule''': '''linear''',
         '''variance_type''': '''fixed_small''',
         '''clip_sample''': True,
     }

     config.update(**__lowerCamelCase  )
     return config

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]					,						[0.0_0_2, 0.0_2, 0.2, 2]  ):
         self.check_over_configs(beta_start=__lowerCamelCase					,						beta_end=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self.check_over_configs(thresholding=__lowerCamelCase  )
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(
                 thresholding=__lowerCamelCase					,						prediction_type=__lowerCamelCase					,						sample_max_value=__lowerCamelCase					,						)

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     for t in [0, 500, 999]:
         self.check_over_forward(time_step=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.scheduler_classes[0]
     __A    :						Union[str, Any]						 =     self.get_scheduler_config()
     __A    :						Tuple						 =     scheduler_class(**__lowerCamelCase  )

     assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_0_9_7_9  )  ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.0_2  )  ) < 1e-5

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.scheduler_classes[0]
     __A    :						Union[str, Any]						 =     self.get_scheduler_config()
     __A    :						Union[str, Any]						 =     scheduler_class(**__lowerCamelCase  )

     __A    :						Dict						 =     len(__lowerCamelCase  )

     __A    :						Optional[int]						 =     self.dummy_model()
     __A    :						Tuple						 =     self.dummy_sample_deter
     __A    :						Optional[int]						 =     self.dummy_sample_deter + 0.1
     __A    :						List[str]						 =     self.dummy_sample_deter - 0.1

     __A    :						Optional[Any]						 =     samplea.shape[0]
     __A    :						Optional[int]						 =     torch.stack([samplea, samplea, samplea]					,						dim=0  )
     __A    :						Optional[int]						 =     torch.arange(__lowerCamelCase  )[0:3, None].repeat(1					,						__lowerCamelCase  )

     __A    :						Tuple						 =     model(samples.flatten(0					,						1  )					,						timesteps.flatten(0					,						1  )  )
     __A    :						Optional[Any]						 =     scheduler.batch_step_no_noise(__lowerCamelCase					,						timesteps.flatten(0					,						1  )					,						samples.flatten(0					,						1  )  )

     __A    :						Optional[Any]						 =     torch.sum(torch.abs(__lowerCamelCase  )  )
     __A    :						Optional[int]						 =     torch.mean(torch.abs(__lowerCamelCase  )  )

     assert abs(result_sum.item() - 1_1_5_3.1_8_3_3  ) < 1e-2
     assert abs(result_mean.item() - 0.5_0_0_5  ) < 1e-3

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     self.scheduler_classes[0]
     __A    :						int						 =     self.get_scheduler_config()
     __A    :						int						 =     scheduler_class(**__lowerCamelCase  )

     __A    :						List[str]						 =     len(__lowerCamelCase  )

     __A    :						Tuple						 =     self.dummy_model()
     __A    :						Optional[int]						 =     self.dummy_sample_deter
     __A    :						Union[str, Any]						 =     torch.manual_seed(0  )

     for t in reversed(range(__lowerCamelCase  )  ):
         # 1. predict noise residual
         __A    :						int						 =     model(__lowerCamelCase					,						__lowerCamelCase  )

         # 2. predict previous mean of sample x_t-1
         __A    :						str						 =     scheduler.step(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						generator=__lowerCamelCase  ).prev_sample

         __A    :						Any						 =     pred_prev_sample

     __A    :						int						 =     torch.sum(torch.abs(__lowerCamelCase  )  )
     __A    :						List[str]						 =     torch.mean(torch.abs(__lowerCamelCase  )  )

     assert abs(result_sum.item() - 2_5_8.9_6_0_6  ) < 1e-2
     assert abs(result_mean.item() - 0.3_3_7_2  ) < 1e-3

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Any						 =     self.scheduler_classes[0]
     __A    :						int						 =     self.get_scheduler_config(prediction_type='''v_prediction'''  )
     __A    :						str						 =     scheduler_class(**__lowerCamelCase  )

     __A    :						Any						 =     len(__lowerCamelCase  )

     __A    :						Optional[int]						 =     self.dummy_model()
     __A    :						List[str]						 =     self.dummy_sample_deter
     __A    :						Tuple						 =     torch.manual_seed(0  )

     for t in reversed(range(__lowerCamelCase  )  ):
         # 1. predict noise residual
         __A    :						Tuple						 =     model(__lowerCamelCase					,						__lowerCamelCase  )

         # 2. predict previous mean of sample x_t-1
         __A    :						Union[str, Any]						 =     scheduler.step(__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						generator=__lowerCamelCase  ).prev_sample

         __A    :						Any						 =     pred_prev_sample

     __A    :						List[str]						 =     torch.sum(torch.abs(__lowerCamelCase  )  )
     __A    :						Any						 =     torch.mean(torch.abs(__lowerCamelCase  )  )

     assert abs(result_sum.item() - 2_0_2.0_2_9_6  ) < 1e-2
     assert abs(result_mean.item() - 0.2_6_3_1  ) < 1e-3

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     self.scheduler_classes[0]
     __A    :						List[Any]						 =     self.get_scheduler_config()
     __A    :						List[Any]						 =     scheduler_class(**__lowerCamelCase  )

     __A    :						List[str]						 =     [100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=__lowerCamelCase  )

     __A    :						Union[str, Any]						 =     scheduler.timesteps

     for i, timestep in enumerate(__lowerCamelCase  ):
         if i == len(__lowerCamelCase  ) - 1:
             __A    :						List[Any]						 =     -1
         else:
             __A    :						Optional[int]						 =     timesteps[i + 1]

         __A    :						int						 =     scheduler.previous_timestep(__lowerCamelCase  )
         __A    :						Union[str, Any]						 =     prev_t.item()

         self.assertEqual(__lowerCamelCase					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.scheduler_classes[0]
     __A    :						str						 =     self.get_scheduler_config()
     __A    :						str						 =     scheduler_class(**__lowerCamelCase  )

     __A    :						Dict						 =     [100, 87, 50, 51, 0]

     with self.assertRaises(__lowerCamelCase					,						msg='''`custom_timesteps` must be in descending order.'''  ):
         scheduler.set_timesteps(timesteps=__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     self.scheduler_classes[0]
     __A    :						Any						 =     self.get_scheduler_config()
     __A    :						Any						 =     scheduler_class(**__lowerCamelCase  )

     __A    :						str						 =     [100, 87, 50, 1, 0]
     __A    :						Optional[Any]						 =     len(__lowerCamelCase  )

     with self.assertRaises(__lowerCamelCase					,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''  ):
         scheduler.set_timesteps(num_inference_steps=__lowerCamelCase					,						timesteps=__lowerCamelCase  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.scheduler_classes[0]
     __A    :						str						 =     self.get_scheduler_config()
     __A    :						Union[str, Any]						 =     scheduler_class(**__lowerCamelCase  )

     __A    :						List[Any]						 =     [scheduler.config.num_train_timesteps]

     with self.assertRaises(
         __lowerCamelCase					,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''					,						):
         scheduler.set_timesteps(timesteps=__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()




