
# 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__  (       __lowercase    ):
   def __init__(self :					str					,				snake_case_ :					str   ):
    __a :  str						=  data







   def __iter__(self :					str   ):
    for element in self.data:
     yield element





def    __UpperCamelCase     (				lowerCAmelCase__							:				str=True  ):
 __a :  Optional[int]						=  Accelerator(even_batches=lowerCAmelCase__  )
 assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
 return accelerator




def    __UpperCamelCase     (				lowerCAmelCase__							:				Accelerator  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				bool = False  ):
 if iterable:
  __a :  List[Any]						=  DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase__  )  )  )
 else:
  __a :  Optional[Any]						=  TensorDataset(torch.as_tensor(range(lowerCAmelCase__  )  )  )

 __a :  int						=  DataLoader(lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  )
 __a :  Any						=  accelerator.prepare(lowerCAmelCase__  )

 return dl




def    __UpperCamelCase     (				lowerCAmelCase__							:				Accelerator  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				List[int]  ,   lowerCAmelCase__							:				List[int]  ,   ):
 __a :  Union[str, Any]						=  create_dataloader(accelerator=lowerCAmelCase__  ,   dataset_size=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  )

 __a :  str						=  [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     (				):
 __a :  List[str]						=  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    __UpperCamelCase     (				):
 __a :  List[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    __UpperCamelCase     (				):
 __a :  Tuple						=  create_accelerator(even_batches=lowerCAmelCase__  )

 __a :  Dict						=  torch.nn.Linear(1  ,   1  )
 __a :  Optional[int]						=  accelerator.prepare(lowerCAmelCase__  )

 __a :  int						=  create_dataloader(lowerCAmelCase__  ,   dataset_size=3  ,   batch_size=1  )

 __a :  Optional[Any]						=  []
 with accelerator.join_uneven_inputs([ddp_model]  ):
  for batch_idx, batch in enumerate(lowerCAmelCase__  ):
   __a :  int						=  ddp_model(batch[0].float()  )
   __a :  Optional[int]						=  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    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ):
 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    __UpperCamelCase     (				):
 __a :  List[Any]						=  True
 __a :  str						=  False
 __a :  Any						=  create_accelerator(even_batches=lowerCAmelCase__  )
 __a :  Union[str, Any]						=  torch.nn.Linear(1  ,   1  )
 __a :  Tuple						=  accelerator.prepare(lowerCAmelCase__  )
 __a :  str						=  create_dataloader(lowerCAmelCase__  ,   dataset_size=3  ,   batch_size=1  )
 __a :  str						=  create_dataloader(lowerCAmelCase__  ,   dataset_size=3  ,   batch_size=1  )

 with accelerator.join_uneven_inputs([ddp_model]  ,   even_batches=lowerCAmelCase__  ):
  __a :  Dict						=  train_dl.batch_sampler.even_batches
  __a :  Union[str, Any]						=  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     (				):
 __a :  int						=  True
 __a :  int						=  False
 __a :  Union[str, Any]						=  create_accelerator(even_batches=lowerCAmelCase__  )
 __a :  int						=  torch.nn.Linear(1  ,   1  )
 __a :  str						=  accelerator.prepare(lowerCAmelCase__  )
 create_dataloader(lowerCAmelCase__  ,   dataset_size=3  ,   batch_size=1  ,   iterable=lowerCAmelCase__  )
 __a :  str						=  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__  ):
    __a :  Any						=  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     (				):
 __a :  List[str]						=  create_accelerator()
 __a :  Tuple						=  torch.nn.Linear(1  ,   1  )
 __a :  Optional[Any]						=  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    __UpperCamelCase     (				):
 __a :  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'''  )
 __a :  Optional[Any]						=  accelerator.state.distributed_type
 __a :  List[Any]						=  DistributedType.FSDP
 test_join_raises_warning_for_non_ddp_distributed(lowerCAmelCase__  )
 __a :  Optional[Any]						=  original_state


if __name__ == "__main__":
      main()


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      lowercase__       =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
