






'''simple docstring'''



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()