# 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			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   List[str]							, lowercase_				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    data







 def __iter__(		self				:   str)						->	Dict:


   """simple docstring"""







   for element in self.data:
     yield element


def 				lowerCAmelCase__       (			a__=True )      ->List[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    Accelerator(even_batches=snake_case__ )
  assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
  return accelerator


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__ = False )      ->List[Any]:




  '''simple docstring'''

  if iterable:
    _UpperCamelCase           =    DummyIterableDataset(torch.as_tensor(range(snake_case__ ) ) )
  else:
    _UpperCamelCase           =    TensorDataset(torch.as_tensor(range(snake_case__ ) ) )

  _UpperCamelCase           =    DataLoader(snake_case__    ,     batch_size=snake_case__ )
  _UpperCamelCase           =    accelerator.prepare(snake_case__ )

  return dl


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__    ,     )      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    create_dataloader(accelerator=snake_case__    ,     dataset_size=snake_case__    ,     batch_size=snake_case__ )

  _UpperCamelCase           =    [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 				lowerCAmelCase__       (			)      ->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    create_accelerator()

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


def 				lowerCAmelCase__       (			)      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    create_accelerator(even_batches=snake_case__ )

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


def 				lowerCAmelCase__       (			)      ->Tuple:




  '''simple docstring'''

  _UpperCamelCase           =    create_accelerator(even_batches=snake_case__ )

  _UpperCamelCase           =    torch.nn.Linear(1    ,     1 )
  _UpperCamelCase           =    accelerator.prepare(snake_case__ )

  _UpperCamelCase           =    create_dataloader(snake_case__    ,     dataset_size=3    ,     batch_size=1 )

  _UpperCamelCase           =    []
  with accelerator.join_uneven_inputs([ddp_model] ):
    for batch_idx, batch in enumerate(snake_case__ ):
      _UpperCamelCase           =    ddp_model(batch[0].float() )
      _UpperCamelCase           =    output.sum()
      loss.backward()
      batch_idxs.append(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 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  with warnings.catch_warnings(record=snake_case__ ) as w:
    with accelerator.join_uneven_inputs([Mock()] ):
      pass

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


def 				lowerCAmelCase__       (			)      ->List[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    True
  _UpperCamelCase           =    False
  _UpperCamelCase           =    create_accelerator(even_batches=snake_case__ )
  _UpperCamelCase           =    torch.nn.Linear(1    ,     1 )
  _UpperCamelCase           =    accelerator.prepare(snake_case__ )
  _UpperCamelCase           =    create_dataloader(snake_case__    ,     dataset_size=3    ,     batch_size=1 )
  _UpperCamelCase           =    create_dataloader(snake_case__    ,     dataset_size=3    ,     batch_size=1 )

  with accelerator.join_uneven_inputs([ddp_model]    ,     even_batches=snake_case__ ):
    _UpperCamelCase           =    train_dl.batch_sampler.even_batches
    _UpperCamelCase           =    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 				lowerCAmelCase__       (			)      ->List[str]:




  '''simple docstring'''

  _UpperCamelCase           =    True
  _UpperCamelCase           =    False
  _UpperCamelCase           =    create_accelerator(even_batches=snake_case__ )
  _UpperCamelCase           =    torch.nn.Linear(1    ,     1 )
  _UpperCamelCase           =    accelerator.prepare(snake_case__ )
  create_dataloader(snake_case__    ,     dataset_size=3    ,     batch_size=1    ,     iterable=snake_case__ )
  _UpperCamelCase           =    create_dataloader(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=snake_case__ ):
        _UpperCamelCase           =    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 				lowerCAmelCase__       (			)      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    create_accelerator()
  _UpperCamelCase           =    torch.nn.Linear(1    ,     1 )
  _UpperCamelCase           =    accelerator.prepare(snake_case__ )
  create_dataloader(snake_case__    ,     dataset_size=3    ,     batch_size=1    ,     iterable=snake_case__ )

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

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


def 				lowerCAmelCase__       (			)      ->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase           =    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" )
  _UpperCamelCase           =    accelerator.state.distributed_type
  _UpperCamelCase           =    DistributedType.FSDP
  test_join_raises_warning_for_non_ddp_distributed(snake_case__ )
  _UpperCamelCase           =    original_state


if __name__ == "__main__":
   main()








from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





