




# 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 		UpperCamelCase__	(A__     ):

    '''simple docstring'''



    def __init__(    self     ,  UpperCamelCase__     )     ->      Any:
           lowerCamelCase   :      List[Any]			 =					data







    def __iter__(    self     )     ->      Dict:






           for element in self.data:
                  yield element




def      A      (      _SCREAMING_SNAKE_CASE=True    )	-> Optional[int]:
       lowerCamelCase   :      Dict			 =					Accelerator(even_batches=__lowerCAmelCase    )
       assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
       return accelerator

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False    )	-> Union[str, Any]:
       if iterable:
              lowerCamelCase   :      Union[str, Any]			 =					DummyIterableDataset(torch.as_tensor(range(__lowerCAmelCase    )    )    )
       else:
              lowerCamelCase   :      Union[str, Any]			 =					TensorDataset(torch.as_tensor(range(__lowerCAmelCase    )    )    )

       lowerCamelCase   :      Any			 =					DataLoader(__lowerCAmelCase ,batch_size=__lowerCAmelCase    )
       lowerCamelCase   :      Optional[int]			 =					accelerator.prepare(__lowerCAmelCase    )

       return dl

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,)	-> str:
       lowerCamelCase   :      str			 =					create_dataloader(accelerator=__lowerCAmelCase ,dataset_size=__lowerCAmelCase ,batch_size=__lowerCAmelCase    )

       lowerCamelCase   :      Optional[Any]			 =					[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      A      (      )	-> Union[str, Any]:
       lowerCamelCase   :      Union[str, Any]			 =					create_accelerator()

       # without padding, we would expect a different number of batches
       verify_dataloader_batch_sizes(
           __lowerCAmelCase ,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(
           __lowerCAmelCase ,dataset_size=7 ,batch_size=2 ,process_0_expected_batch_sizes=[2, 2] ,process_1_expected_batch_sizes=[2, 2] ,)

def      A      (      )	-> Any:
       lowerCamelCase   :      str			 =					create_accelerator(even_batches=__lowerCAmelCase    )

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

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

def      A      (      )	-> List[Any]:
       lowerCamelCase   :      Optional[int]			 =					create_accelerator(even_batches=__lowerCAmelCase    )

       lowerCamelCase   :      int			 =					torch.nn.Linear(1 ,1    )
       lowerCamelCase   :      int			 =					accelerator.prepare(__lowerCAmelCase    )

       lowerCamelCase   :      List[str]			 =					create_dataloader(__lowerCAmelCase ,dataset_size=3 ,batch_size=1    )

       lowerCamelCase   :      List[str]			 =					[]
       with accelerator.join_uneven_inputs([ddp_model]    ):
              for batch_idx, batch in enumerate(__lowerCAmelCase    ):
                     lowerCamelCase   :      Dict			 =					ddp_model(batch[0].float()    )
                     lowerCamelCase   :      Union[str, Any]			 =					output.sum()
                     loss.backward()
                     batch_idxs.append(__lowerCAmelCase    )

       accelerator.wait_for_everyone()

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

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Tuple:
       with warnings.catch_warnings(record=__lowerCAmelCase    ) as w:
              with accelerator.join_uneven_inputs([Mock()]    ):
                     pass

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

def      A      (      )	-> int:
       lowerCamelCase   :      Union[str, Any]			 =					True
       lowerCamelCase   :      List[Any]			 =					False
       lowerCamelCase   :      Dict			 =					create_accelerator(even_batches=__lowerCAmelCase    )
       lowerCamelCase   :      Union[str, Any]			 =					torch.nn.Linear(1 ,1    )
       lowerCamelCase   :      List[str]			 =					accelerator.prepare(__lowerCAmelCase    )
       lowerCamelCase   :      Any			 =					create_dataloader(__lowerCAmelCase ,dataset_size=3 ,batch_size=1    )
       lowerCamelCase   :      Tuple			 =					create_dataloader(__lowerCAmelCase ,dataset_size=3 ,batch_size=1    )

       with accelerator.join_uneven_inputs([ddp_model] ,even_batches=__lowerCAmelCase    ):
              lowerCamelCase   :      int			 =					train_dl.batch_sampler.even_batches
              lowerCamelCase   :      Tuple			 =					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      A      (      )	-> List[str]:
       lowerCamelCase   :      Tuple			 =					True
       lowerCamelCase   :      Tuple			 =					False
       lowerCamelCase   :      List[str]			 =					create_accelerator(even_batches=__lowerCAmelCase    )
       lowerCamelCase   :      str			 =					torch.nn.Linear(1 ,1    )
       lowerCamelCase   :      Any			 =					accelerator.prepare(__lowerCAmelCase    )
       create_dataloader(__lowerCAmelCase ,dataset_size=3 ,batch_size=1 ,iterable=__lowerCAmelCase    )
       lowerCamelCase   :      Any			 =					create_dataloader(__lowerCAmelCase ,dataset_size=3 ,batch_size=1    )

       with warnings.catch_warnings():
              warnings.filterwarnings("ignore"    )
              try:
                     with accelerator.join_uneven_inputs([ddp_model] ,even_batches=__lowerCAmelCase    ):
                            lowerCamelCase   :      str			 =					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      A      (      )	-> int:
       lowerCamelCase   :      List[Any]			 =					create_accelerator()
       lowerCamelCase   :      Tuple			 =					torch.nn.Linear(1 ,1    )
       lowerCamelCase   :      Dict			 =					accelerator.prepare(__lowerCAmelCase    )
       create_dataloader(__lowerCAmelCase ,dataset_size=3 ,batch_size=1 ,iterable=__lowerCAmelCase    )

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

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

def      A      (      )	-> List[Any]:
       lowerCamelCase   :      Tuple			 =					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"    )
       lowerCamelCase   :      int			 =					accelerator.state.distributed_type
       lowerCamelCase   :      int			 =					DistributedType.FSDP
       test_join_raises_warning_for_non_ddp_distributed(__lowerCAmelCase    )
       lowerCamelCase   :      str			 =					original_state


if __name__ == "__main__":
       main()














import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
 import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class 			__a  (    unittest.TestCase       ,   A__       ):







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  load_tool("text-to-speech"						)
      self.tool.setup()







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   Union[str, Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   Any       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   int       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)