






'''simple docstring'''



# Copyright 2021 The HuggingFace 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




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



         '''simple docstring'''
         __a				     =    data






      def __iter__(  self      )							->int:



         '''simple docstring'''
         for element in self.data:
            yield element







def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int=True    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    Accelerator(even_batches=SCREAMING_SNAKE_CASE__    )
   assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
   return accelerator



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Accelerator,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	bool = False    )  ->			List[str]:

   """simple docstring"""
   if iterable:
      __a				     =    DummyIterableDataset(torch.as_tensor(range(SCREAMING_SNAKE_CASE__    )    )    )
   else:
      __a				     =    TensorDataset(torch.as_tensor(range(SCREAMING_SNAKE_CASE__    )    )    )

   __a				     =    DataLoader(SCREAMING_SNAKE_CASE__,					batch_size=SCREAMING_SNAKE_CASE__    )
   __a				     =    accelerator.prepare(SCREAMING_SNAKE_CASE__    )

   return dl



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Accelerator,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	List[int],					SCREAMING_SNAKE_CASE__:	List[int],					)  ->			List[str]:

   """simple docstring"""
   __a				     =    create_dataloader(accelerator=SCREAMING_SNAKE_CASE__,					dataset_size=SCREAMING_SNAKE_CASE__,					batch_size=SCREAMING_SNAKE_CASE__    )

   __a				     =    [len(batch[0]    ) for batch in dl]

   if accelerator.process_index == 0:
      assert batch_sizes == process_0_expected_batch_sizes
   elif accelerator.process_index == 1:
      assert batch_sizes == process_1_expected_batch_sizes



def    __UpperCAmelCase	(				)  ->			str:

   """simple docstring"""
   __a				     =    create_accelerator()

   # without padding, we would expect a different number of batches
   verify_dataloader_batch_sizes(
       SCREAMING_SNAKE_CASE__,					dataset_size=3,					batch_size=1,					process_0_expected_batch_sizes=[1, 1],					process_1_expected_batch_sizes=[1, 1],					)

   # without padding, we would expect the same number of batches, but different sizes
   verify_dataloader_batch_sizes(
       SCREAMING_SNAKE_CASE__,					dataset_size=7,					batch_size=2,					process_0_expected_batch_sizes=[2, 2],					process_1_expected_batch_sizes=[2, 2],					)



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

   """simple docstring"""
   __a				     =    create_accelerator(even_batches=SCREAMING_SNAKE_CASE__    )

   verify_dataloader_batch_sizes(
       SCREAMING_SNAKE_CASE__,					dataset_size=3,					batch_size=1,					process_0_expected_batch_sizes=[1, 1],					process_1_expected_batch_sizes=[1],					)

   verify_dataloader_batch_sizes(
       SCREAMING_SNAKE_CASE__,					dataset_size=7,					batch_size=2,					process_0_expected_batch_sizes=[2, 2],					process_1_expected_batch_sizes=[2, 1],					)



def    __UpperCAmelCase	(				)  ->			int:

   """simple docstring"""
   __a				     =    create_accelerator(even_batches=SCREAMING_SNAKE_CASE__    )

   __a				     =    torch.nn.Linear(1,					1    )
   __a				     =    accelerator.prepare(SCREAMING_SNAKE_CASE__    )

   __a				     =    create_dataloader(SCREAMING_SNAKE_CASE__,					dataset_size=3,					batch_size=1    )

   __a				     =    []
   with accelerator.join_uneven_inputs([ddp_model]    ):
      for batch_idx, batch in enumerate(SCREAMING_SNAKE_CASE__    ):
         __a				     =    ddp_model(batch[0].float()    )
         __a				     =    output.sum()
         loss.backward()
         batch_idxs.append(SCREAMING_SNAKE_CASE__    )

   accelerator.wait_for_everyone()

   if accelerator.process_index == 0:
      assert batch_idxs == [0, 1]
   elif accelerator.process_index == 1:
      assert batch_idxs == [0]



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			Tuple:

   """simple docstring"""
   with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE__    ) as w:
      with accelerator.join_uneven_inputs([Mock()]    ):
         pass

      assert issubclass(w[-1].category,					SCREAMING_SNAKE_CASE__    )
      assert "only supported for multi-GPU" in str(w[-1].message    )



def    __UpperCAmelCase	(				)  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    True
   __a				     =    False
   __a				     =    create_accelerator(even_batches=SCREAMING_SNAKE_CASE__    )
   __a				     =    torch.nn.Linear(1,					1    )
   __a				     =    accelerator.prepare(SCREAMING_SNAKE_CASE__    )
   __a				     =    create_dataloader(SCREAMING_SNAKE_CASE__,					dataset_size=3,					batch_size=1    )
   __a				     =    create_dataloader(SCREAMING_SNAKE_CASE__,					dataset_size=3,					batch_size=1    )

   with accelerator.join_uneven_inputs([ddp_model],					even_batches=SCREAMING_SNAKE_CASE__    ):
      __a				     =    train_dl.batch_sampler.even_batches
      __a				     =    valid_dl.batch_sampler.even_batches

   assert train_dl_overridden_value == overridden_even_batches
   assert valid_dl_overridden_value == overridden_even_batches
   assert train_dl.batch_sampler.even_batches == default_even_batches
   assert valid_dl.batch_sampler.even_batches == default_even_batches



def    __UpperCAmelCase	(				)  ->			int:

   """simple docstring"""
   __a				     =    True
   __a				     =    False
   __a				     =    create_accelerator(even_batches=SCREAMING_SNAKE_CASE__    )
   __a				     =    torch.nn.Linear(1,					1    )
   __a				     =    accelerator.prepare(SCREAMING_SNAKE_CASE__    )
   create_dataloader(SCREAMING_SNAKE_CASE__,					dataset_size=3,					batch_size=1,					iterable=SCREAMING_SNAKE_CASE__    )
   __a				     =    create_dataloader(SCREAMING_SNAKE_CASE__,					dataset_size=3,					batch_size=1    )

   with warnings.catch_warnings():
      warnings.filterwarnings('ignore'    )
      try:
         with accelerator.join_uneven_inputs([ddp_model],					even_batches=SCREAMING_SNAKE_CASE__    ):
            __a				     =    batch_dl.batch_sampler.even_batches
      except AttributeError:
         # ensure attribute error is not raised when processing iterable dl
         raise AssertionError

   assert batch_dl_overridden_value == overridden_even_batches
   assert batch_dl.batch_sampler.even_batches == default_even_batches



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

   """simple docstring"""
   __a				     =    create_accelerator()
   __a				     =    torch.nn.Linear(1,					1    )
   __a				     =    accelerator.prepare(SCREAMING_SNAKE_CASE__    )
   create_dataloader(SCREAMING_SNAKE_CASE__,					dataset_size=3,					batch_size=1,					iterable=SCREAMING_SNAKE_CASE__    )

   with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE__    ) as w:
      with accelerator.join_uneven_inputs([ddp_model],					even_batches=SCREAMING_SNAKE_CASE__    ):
         pass

      assert issubclass(w[-1].category,					SCREAMING_SNAKE_CASE__    )
      assert "only supported for map-style datasets" in str(w[-1].message    )



def    __UpperCAmelCase	(				)  ->			Tuple:

   """simple docstring"""
   __a				     =    create_accelerator()

   accelerator.print('Test that even_batches variable ensures uniform batches across processes'    )
   test_default_ensures_even_batch_sizes()

   accelerator.print('Run tests with even_batches disabled'    )
   test_can_disable_even_batches()

   accelerator.print('Test joining uneven inputs'    )
   test_can_join_uneven_inputs()

   accelerator.print('Test overriding even_batches when joining uneven inputs'    )
   test_join_can_override_even_batches()

   accelerator.print('Test overriding even_batches for mixed dataloader types'    )
   test_join_can_override_for_mixed_type_dataloaders()

   accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders'    )
   test_join_raises_warning_for_iterable_when_overriding_even_batches()

   accelerator.print('Test join with non DDP distributed raises warning'    )
   __a				     =    accelerator.state.distributed_type
   __a				     =    DistributedType.FSDP
   test_join_raises_warning_for_non_ddp_distributed(SCREAMING_SNAKE_CASE__    )
   __a				     =    original_state


if __name__ == "__main__":
 main()







'''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()