


'''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 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[str]     = data


    def __iter__(  self      :							Optional[Any]):
           for element in self.data:
                  yield element






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[Any]=True       ):
       _A					:     Union[str, Any]     = Accelerator(even_batches=lowerCamelCase       )
       assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
       return accelerator






def 				lowerCAmelCase__	(    lowerCamelCase					:      Accelerator  ,lowerCamelCase					:      int  ,lowerCamelCase					:      int  ,lowerCamelCase					:      bool = False       ):
       if iterable:
              _A					:     Any     = DummyIterableDataset(torch.as_tensor(range(lowerCamelCase       )       )       )
       else:
              _A					:     str     = TensorDataset(torch.as_tensor(range(lowerCamelCase       )       )       )

       _A					:     str     = DataLoader(lowerCamelCase  ,batch_size=lowerCamelCase       )
       _A					:     Any     = accelerator.prepare(lowerCamelCase       )

       return dl






def 				lowerCAmelCase__	(    lowerCamelCase					:      Accelerator  ,lowerCamelCase					:      int  ,lowerCamelCase					:      int  ,lowerCamelCase					:      List[int]  ,lowerCamelCase					:      List[int]  ,):
       _A					:     Dict     = create_dataloader(accelerator=lowerCamelCase  ,dataset_size=lowerCamelCase  ,batch_size=lowerCamelCase       )

       _A					:     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 				lowerCAmelCase__	(    ):
       _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 				lowerCAmelCase__	(    ):
       _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 				lowerCAmelCase__	(    ):
       _A					:     str     = create_accelerator(even_batches=lowerCamelCase       )

       _A					:     Any     = torch.nn.Linear(1  ,1       )
       _A					:     Tuple     = accelerator.prepare(lowerCamelCase       )

       _A					:     Any     = create_dataloader(lowerCamelCase  ,dataset_size=3  ,batch_size=1       )

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

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


if __name__ == "__main__":
   main()










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A				:					str    						=			{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Optional[Any]    						=			['''BlipImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BlipModel''',
       '''BlipPreTrainedModel''',
       '''BlipForConditionalGeneration''',
       '''BlipForQuestionAnswering''',
       '''BlipVisionModel''',
       '''BlipTextModel''',
       '''BlipForImageTextRetrieval''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Union[str, Any]    						=			[
       '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFBlipModel''',
       '''TFBlipPreTrainedModel''',
       '''TFBlipForConditionalGeneration''',
       '''TFBlipForQuestionAnswering''',
       '''TFBlipVisionModel''',
       '''TFBlipTextModel''',
       '''TFBlipForImageTextRetrieval''',
   ]

if TYPE_CHECKING:
   from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
   from .processing_blip import BlipProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_blip import BlipImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blip import (
          BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlipForConditionalGeneration,
          BlipForImageTextRetrieval,
          BlipForQuestionAnswering,
          BlipModel,
          BlipPreTrainedModel,
          BlipTextModel,
          BlipVisionModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blip import (
          TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBlipForConditionalGeneration,
          TFBlipForImageTextRetrieval,
          TFBlipForQuestionAnswering,
          TFBlipModel,
          TFBlipPreTrainedModel,
          TFBlipTextModel,
          TFBlipVisionModel,
      )

else:
   import sys

   A				:					Optional[int]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






