
"""simple docstring"""




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch


@require_torch
class       __magic_name__							:
       @property
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return self.get_dummy_input()
       @property
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         if self.block_type == "down":
           return (4, 32, 16, 16)
         elif self.block_type == "mid":
           return (4, 32, 32, 32)
         elif self.block_type == "up":
           return (4, 32, 64, 64)

         raise ValueError(f'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''							)
       def 				lowercase_				(  self			,					A_=True			,					A_=False			,					A_=False			,					A_=False			,					)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      int   			=  4
         _lowercase:      Optional[int]   			=  32
         _lowercase:      Any   			=  (32, 32)

         _lowercase:      int   			=  torch.manual_seed(0							)
         _lowercase:      List[Any]   			=  torch.device(A_							)
         _lowercase:      Tuple   			=  (batch_size, num_channels) + sizes
         _lowercase:      Any   			=  randn_tensor(A_			,					generator=A_			,					device=A_							)
         _lowercase:      Union[str, Any]   			=  {'''hidden_states''': hidden_states}

         if include_temb:
           _lowercase:      Optional[Any]   			=  128
           _lowercase:      str   			=  randn_tensor((batch_size, temb_channels)			,					generator=A_			,					device=A_							)

         if include_res_hidden_states_tuple:
           _lowercase:      Union[str, Any]   			=  torch.manual_seed(1							)
           _lowercase:      List[Any]   			=  (randn_tensor(A_			,					generator=A_			,					device=A_							),)

         if include_encoder_hidden_states:
           _lowercase:      Optional[Any]   			=  floats_tensor((batch_size, 32, 32)							).to(A_							)

         if include_skip_sample:
           _lowercase:      Optional[int]   			=  randn_tensor(((batch_size, 3) + sizes)			,					generator=A_			,					device=A_							)

         return dummy_input
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      int   			=  {
             '''in_channels''': 32,
             '''out_channels''': 32,
             '''temb_channels''': 128,
         }
         if self.block_type == "up":
           _lowercase:      str   			=  32

         if self.block_type == "mid":
           init_dict.pop('''out_channels'''							)

         _lowercase:      List[Any]   			=  self.dummy_input
         return init_dict, inputs_dict
       def 				lowercase_				(  self			,					A_							)  ->						Any:





         """simple docstring"""




         _lowercase			,					_lowercase:      Any   			=  self.prepare_init_args_and_inputs_for_common()
         _lowercase:      Optional[Any]   			=  self.block_class(**A_							)
         unet_block.to(A_							)
         unet_block.eval()

         with torch.no_grad():
           _lowercase:      str   			=  unet_block(**A_							)

         if isinstance(A_			,					A_							):
           _lowercase:      Optional[Any]   			=  output[0]

         self.assertEqual(output.shape			,					self.output_shape							)

         _lowercase:      Tuple   			=  output[0, -1, -3:, -3:]
         _lowercase:      Union[str, Any]   			=  torch.tensor(A_							).to(A_							)
         assert torch_all_close(output_slice.flatten()			,					A_			,					atol=5E-3							)



       @unittest.skipIf(torch_device == '''mps'''			,					'''Training is not supported in mps'''							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase			,					_lowercase:      Union[str, Any]   			=  self.prepare_init_args_and_inputs_for_common()
         _lowercase:      List[Any]   			=  self.block_class(**A_							)
         model.to(A_							)
         model.train()
         _lowercase:      Union[str, Any]   			=  model(**A_							)

         if isinstance(A_			,					A_							):
           _lowercase:      Tuple   			=  output[0]

         _lowercase:      Optional[int]   			=  torch.device(A_							)
         _lowercase:      Union[str, Any]   			=  randn_tensor(output.shape			,					device=A_							)
         _lowercase:      Optional[int]   			=  torch.nn.functional.mse_loss(A_			,					A_							)
         loss.backward()


"""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       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''microsoft/speecht5_tts'''
       UpperCamelCase_ =		(
           '''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.'''
       )
       UpperCamelCase_ =		'''text_reader'''
       UpperCamelCase_ =		SpeechTaProcessor
       UpperCamelCase_ =		SpeechTaForTextToSpeech
       UpperCamelCase_ =		SpeechTaHifiGan

       UpperCamelCase_ =		['''text''']
       UpperCamelCase_ =		['''audio''']
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         if self.post_processor is None:
           _lowercase:      Tuple   			=  '''microsoft/speecht5_hifigan'''
         super().setup()
       def 				lowercase_				(  self			,					A_			,					A_=None							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.pre_processor(text=A_			,					return_tensors='''pt'''			,					truncation=A_							)

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

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

         return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
       def 				lowercase_				(  self			,					A_							)  ->						Optional[int]:





         """simple docstring"""




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



       def 				lowercase_				(  self			,					A_							)  ->						Dict:





         """simple docstring"""




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