


# 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   __lowercase				(  __snake_case  ):





      def __init__(		self						:					Any					,       __lowerCamelCase						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     data





      def __iter__(		self						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         for element in self.data:
            yield element

def   _UpperCamelCase				(						lowerCAmelCase_=True   )						->List[Any]:
   UpperCAmelCase							=     Accelerator(even_batches=lowerCAmelCase_   )
   assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
   return accelerator
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = False   )						->Optional[Any]:
   if iterable:
      UpperCAmelCase							=     DummyIterableDataset(torch.as_tensor(range(lowerCAmelCase_   )   )   )
   else:
      UpperCAmelCase							=     TensorDataset(torch.as_tensor(range(lowerCAmelCase_   )   )   )

   UpperCAmelCase							=     DataLoader(lowerCAmelCase_  ,						batch_size=lowerCAmelCase_   )
   UpperCAmelCase							=     accelerator.prepare(lowerCAmelCase_   )

   return dl
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->Union[str, Any]:
   UpperCAmelCase							=     create_dataloader(accelerator=lowerCAmelCase_  ,						dataset_size=lowerCAmelCase_  ,						batch_size=lowerCAmelCase_   )

   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   _UpperCamelCase				(						)						->str:
   UpperCAmelCase							=     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				(						)						->str:
   UpperCAmelCase							=     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				(						)						->Optional[int]:
   UpperCAmelCase							=     create_accelerator(even_batches=lowerCAmelCase_   )

   UpperCAmelCase							=     torch.nn.Linear(1  ,						1   )
   UpperCAmelCase							=     accelerator.prepare(lowerCAmelCase_   )

   UpperCAmelCase							=     create_dataloader(lowerCAmelCase_  ,						dataset_size=3  ,						batch_size=1   )

   UpperCAmelCase							=     []
   with accelerator.join_uneven_inputs([ddp_model]   ):
      for batch_idx, batch in enumerate(lowerCAmelCase_   ):
         UpperCAmelCase							=     ddp_model(batch[0].float()   )
         UpperCAmelCase							=     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_   )						->List[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				(						)						->Optional[Any]:
   UpperCAmelCase							=     True
   UpperCAmelCase							=     False
   UpperCAmelCase							=     create_accelerator(even_batches=lowerCAmelCase_   )
   UpperCAmelCase							=     torch.nn.Linear(1  ,						1   )
   UpperCAmelCase							=     accelerator.prepare(lowerCAmelCase_   )
   UpperCAmelCase							=     create_dataloader(lowerCAmelCase_  ,						dataset_size=3  ,						batch_size=1   )
   UpperCAmelCase							=     create_dataloader(lowerCAmelCase_  ,						dataset_size=3  ,						batch_size=1   )

   with accelerator.join_uneven_inputs([ddp_model]  ,						even_batches=lowerCAmelCase_   ):
      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   _UpperCamelCase				(						)						->Optional[int]:
   UpperCAmelCase							=     True
   UpperCAmelCase							=     False
   UpperCAmelCase							=     create_accelerator(even_batches=lowerCAmelCase_   )
   UpperCAmelCase							=     torch.nn.Linear(1  ,						1   )
   UpperCAmelCase							=     accelerator.prepare(lowerCAmelCase_   )
   create_dataloader(lowerCAmelCase_  ,						dataset_size=3  ,						batch_size=1  ,						iterable=lowerCAmelCase_   )
   UpperCAmelCase							=     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_   ):
            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   _UpperCamelCase				(						)						->List[str]:
   UpperCAmelCase							=     create_accelerator()
   UpperCAmelCase							=     torch.nn.Linear(1  ,						1   )
   UpperCAmelCase							=     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				(						)						->str:
   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(lowerCAmelCase_   )
   UpperCAmelCase							=     original_state


if __name__ == "__main__":
 main()







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     tokenizer(example["""content"""]  ,						truncation=lowerCAmelCase_   )["""input_ids"""]
   UpperCAmelCase							=     len(example["""content"""]   ) / len(output["""input_ids"""]   )
   return output


__a     =			HfArgumentParser(PretokenizationArguments)
__a     =			parser.parse_args()
if args.num_workers is None:
 __a     =			multiprocessing.cpu_count()
__a     =			AutoTokenizer.from_pretrained(args.tokenizer_dir)

__a     =			time.time()
__a     =			load_dataset(args.dataset_name, split="""train""")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

__a     =			time.time()
__a     =			ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")

__a     =			time.time()
ds.push_to_hub(args.tokenized_data_repo)







print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")



