

import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_SCREAMING_SNAKE_CASE					:  Any             =      16
_SCREAMING_SNAKE_CASE					:  Tuple             =      32
def     UpperCAmelCase_       (						_A				,		_A = 16				,		_A = "bert-base-cased"    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   AutoTokenizer.from_pretrained(_A    )
     SCREAMING_SNAKE_CASE__   =   load_dataset('''glue'''				,		'''mrpc'''    )

     def tokenize_function(_A    ):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE__   =   tokenizer(examples['''sentence1''']				,		examples['''sentence2''']				,		truncation=_A				,		max_length=_A    )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     SCREAMING_SNAKE_CASE__   =   datasets.map(
         _A				,		batched=_A				,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,		load_from_cache_file=_A    )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     SCREAMING_SNAKE_CASE__   =   tokenized_datasets.rename_column('''label'''				,		'''labels'''    )

     def collate_fn(_A    ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_A				,		padding='''max_length'''				,		max_length=1_28				,		return_tensors='''pt'''    )
          return tokenizer.pad(_A				,		padding='''longest'''				,		return_tensors='''pt'''    )

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE__   =   DataLoader(
         tokenized_datasets['''train''']				,		shuffle=_A				,		collate_fn=_A				,		batch_size=_A    )
     SCREAMING_SNAKE_CASE__   =   DataLoader(
         tokenized_datasets['''validation''']				,		shuffle=_A				,		collate_fn=_A				,		batch_size=_A    )

     return train_dataloader, eval_dataloader
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     model.eval()
     SCREAMING_SNAKE_CASE__   =   0
     for step, batch in enumerate(_A    ):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device    )
          with torch.no_grad():
               SCREAMING_SNAKE_CASE__   =   model(**_A    )
          SCREAMING_SNAKE_CASE__   =   outputs.logits.argmax(dim=-1    )
          # It is slightly faster to call this once, than multiple times
          SCREAMING_SNAKE_CASE__   =   accelerator.gather(
              (predictions, batch['''labels'''])    )  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
               if step == len(_A    ) - 1:
                    SCREAMING_SNAKE_CASE__   =   predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                    SCREAMING_SNAKE_CASE__   =   references[: len(eval_dataloader.dataset    ) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=_A				,		references=_A				,		)

     SCREAMING_SNAKE_CASE__   =   metric.compute()
     return eval_metric["accuracy"]
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE__   =   config["""lr"""]
     SCREAMING_SNAKE_CASE__   =   int(config['''num_epochs''']    )
     SCREAMING_SNAKE_CASE__   =   int(config['''seed''']    )
     SCREAMING_SNAKE_CASE__   =   int(config['''batch_size''']    )
     SCREAMING_SNAKE_CASE__   =   args.model_name_or_path

     set_seed(_A    )
     SCREAMING_SNAKE_CASE__   =   get_dataloaders(_A				,		_A				,		_A    )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     SCREAMING_SNAKE_CASE__   =   AutoModelForSequenceClassification.from_pretrained(_A				,		return_dict=_A    )

     # Instantiate optimizer
     SCREAMING_SNAKE_CASE__   =   (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     SCREAMING_SNAKE_CASE__   =   optimizer_cls(params=model.parameters()				,		lr=_A    )

     if accelerator.state.deepspeed_plugin is not None:
          SCREAMING_SNAKE_CASE__   =   accelerator.state.deepspeed_plugin.deepspeed_config[
              """gradient_accumulation_steps"""
          ]
     else:
          SCREAMING_SNAKE_CASE__   =   1
     SCREAMING_SNAKE_CASE__   =   (len(_A    ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          SCREAMING_SNAKE_CASE__   =   get_linear_schedule_with_warmup(
              optimizer=_A				,		num_warmup_steps=0				,		num_training_steps=_A				,		)
     else:
          SCREAMING_SNAKE_CASE__   =   DummyScheduler(_A				,		total_num_steps=_A				,		warmup_num_steps=0    )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     SCREAMING_SNAKE_CASE__   =   accelerator.prepare(
         _A				,		_A				,		_A				,		_A				,		_A    )

     # We need to keep track of how many total steps we have iterated over
     SCREAMING_SNAKE_CASE__   =   0
     # We also need to keep track of the stating epoch so files are named properly
     SCREAMING_SNAKE_CASE__   =   0
     SCREAMING_SNAKE_CASE__   =   evaluate.load('''glue'''				,		'''mrpc'''    )
     SCREAMING_SNAKE_CASE__   =   num_epochs

     if args.partial_train_epoch is not None:
          SCREAMING_SNAKE_CASE__   =   args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint    )
          SCREAMING_SNAKE_CASE__   =   args.resume_from_checkpoint.split('''epoch_'''    )[1]
          SCREAMING_SNAKE_CASE__   =   """"""
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          SCREAMING_SNAKE_CASE__   =   int(_A    ) + 1
          SCREAMING_SNAKE_CASE__   =   evaluation_loop(_A				,		_A				,		_A				,		_A    )
          accelerator.print('''resumed checkpoint performance:'''				,		_A    )
          accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''				,		lr_scheduler.get_lr()[0]    )
          accelerator.print('''resumed optimizers\'s lr:'''				,		optimizer.param_groups[0]['''lr''']    )
          with open(os.path.join(args.output_dir				,		F'''state_{starting_epoch-1}.json'''    )				,		'''r'''    ) as f:
               SCREAMING_SNAKE_CASE__   =   json.load(_A    )
               assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["lr"] == lr_scheduler.get_lr()[0]
               ), "Scheduler learning rate mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
               ), "Optimizer learning rate mismatch, loading from checkpoint failed"
               assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
               return

                # Now we train the model
     SCREAMING_SNAKE_CASE__   =   {}
     for epoch in range(_A				,		_A    ):
          model.train()
          for step, batch in enumerate(_A    ):
               SCREAMING_SNAKE_CASE__   =   model(**_A    )
               SCREAMING_SNAKE_CASE__   =   outputs.loss
               SCREAMING_SNAKE_CASE__   =   loss / gradient_accumulation_steps
               accelerator.backward(_A    )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1
          SCREAMING_SNAKE_CASE__   =   F'''epoch_{epoch}'''
          SCREAMING_SNAKE_CASE__   =   os.path.join(args.output_dir				,		_A    )
          accelerator.save_state(_A    )
          SCREAMING_SNAKE_CASE__   =   evaluation_loop(_A				,		_A				,		_A				,		_A    )
          SCREAMING_SNAKE_CASE__   =   accuracy
          SCREAMING_SNAKE_CASE__   =   lr_scheduler.get_lr()[0]
          SCREAMING_SNAKE_CASE__   =   optimizer.param_groups[0]["""lr"""]
          SCREAMING_SNAKE_CASE__   =   epoch
          SCREAMING_SNAKE_CASE__   =   overall_step
          accelerator.print(F'''epoch {epoch}:'''				,		_A    )

          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               with open(os.path.join(args.output_dir				,		F'''state_{epoch}.json'''    )				,		'''w'''    ) as f:
                    json.dump(_A				,		_A    )
def     UpperCAmelCase_       (						):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''    )
     parser.add_argument(
         '''--model_name_or_path'''				,		type=_A				,		default='''bert-base-cased'''				,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''				,		required=_A				,		)
     parser.add_argument(
         '''--output_dir'''				,		type=_A				,		default='''.'''				,		help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''				,		)
     parser.add_argument(
         '''--resume_from_checkpoint'''				,		type=_A				,		default=_A				,		help='''If the training should continue from a checkpoint folder.'''				,		)
     parser.add_argument(
         '''--partial_train_epoch'''				,		type=_A				,		default=_A				,		help='''If passed, the training will stop after this number of epochs.'''				,		)
     parser.add_argument(
         '''--num_epochs'''				,		type=_A				,		default=2				,		help='''Number of train epochs.'''				,		)
     SCREAMING_SNAKE_CASE__   =   parser.parse_args()
     SCREAMING_SNAKE_CASE__   =   {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

     training_function(_A				,		_A    )


if __name__ == "__main__":
       main()






import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  def __init__(							self						:		List[str]     ,       __lowerCamelCase						:		Tuple=0.01     ,       __lowerCamelCase						:		Optional[Any]=1000 )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   p_stop
       SCREAMING_SNAKE_CASE__   =   max_length







  def __iter__(							self						:		List[str] )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   0
       SCREAMING_SNAKE_CASE__   =   False
       while not stop and count < self.max_length:
            yield count
            count += 1
            SCREAMING_SNAKE_CASE__   =   random.random() < self.p_stop






class 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Any=False     ,       __lowerCamelCase						:		Optional[int]=True )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   [
           BatchSamplerShard(__lowerCamelCase     ,       2     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )
           for i in range(2 )
       ]
       SCREAMING_SNAKE_CASE__   =   [list(__lowerCamelCase ) for batch_sampler_shard in batch_sampler_shards]
       if not split_batches:
            self.assertListEqual([len(__lowerCamelCase ) for shard in batch_sampler_shards]     ,       [len(__lowerCamelCase ) for e in expected] )
       self.assertListEqual(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Any )		->							Optional[Any]:
       # Check the shards when the dataset is a round multiple of total batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       # Expected shouldn't change
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       # Check the shards when the dataset is a round multiple of batch size but not total batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
       # num_processes batch.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
       # num_processes batch.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(20 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(20 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       # Check the shards when the dataset is very small.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[[0, 1, 0]], [[1, 0, 1]]]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[int] )		->							int:
       # Check the shards when the dataset is a round multiple of batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       # Expected shouldn't change
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size or num_processes.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       # Check the shards when the dataset is very small.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[[0, 1]], [[0, 1]]]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase )






  def 				lowercase_		(							self						:		str )		->							Dict:
       # Check the shards when the dataset is a round multiple of total batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       # Expected shouldn't change
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is a round multiple of batch size but not total batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
       # num_processes batch.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
       # num_processes batch.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(20 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(20 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
           [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is very small.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[[0, 1]], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=3     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase )






  def 				lowercase_		(							self						:		List[str] )		->							Tuple:
       # Check the shards when the dataset is a round multiple of batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(24 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       # Expected shouldn't change
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(22 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is not a round multiple of batch size or num_processes.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(21 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [
           [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
           [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
       ]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       # Check the shards when the dataset is very small.
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[[0, 1]], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(2 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   [[], []]
       self.check_batch_sampler_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       even_batches=__lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[int] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
       SCREAMING_SNAKE_CASE__   =   [BatchSamplerShard(__lowerCamelCase     ,       2     ,       __lowerCamelCase     ,       even_batches=__lowerCamelCase ) for i in range(2 )]

       self.assertEqual(len(batch_sampler_shards[0] )     ,       3 )
       self.assertEqual(len(batch_sampler_shards[1] )     ,       2 )

       self.assertListEqual(list(batch_sampler_shards[0] )     ,       [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
       self.assertListEqual(list(batch_sampler_shards[1] )     ,       [[3, 4], [9, 10, 11]] )






  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Tuple=False     ,       __lowerCamelCase						:		Optional[int]=2     ,       __lowerCamelCase						:		Dict=False )		->							str:
       random.seed(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   list(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   [
           IterableDatasetShard(
               __lowerCamelCase     ,       batch_size=__lowerCamelCase     ,       drop_last=__lowerCamelCase     ,       num_processes=__lowerCamelCase     ,       process_index=__lowerCamelCase     ,       split_batches=__lowerCamelCase     ,       )
           for i in range(__lowerCamelCase )
       ]
       SCREAMING_SNAKE_CASE__   =   []
       for iterable_dataset_shard in iterable_dataset_shards:
            # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
            random.seed(__lowerCamelCase )
            iterable_dataset_lists.append(list(__lowerCamelCase ) )

       SCREAMING_SNAKE_CASE__   =   batch_size // num_processes if split_batches else batch_size
       # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
       SCREAMING_SNAKE_CASE__   =   iterable_dataset_lists[0]
       for l in iterable_dataset_lists[1:]:
            self.assertEqual(len(__lowerCamelCase )     ,       len(__lowerCamelCase ) )
            self.assertTrue(len(__lowerCamelCase ) % shard_batch_size == 0 )

       SCREAMING_SNAKE_CASE__   =   []
       for idx in range(0     ,       len(__lowerCamelCase )     ,       __lowerCamelCase ):
            for l in iterable_dataset_lists:
                 observed += l[idx : idx + shard_batch_size]

       if not drop_last:
            while len(__lowerCamelCase ) < len(__lowerCamelCase ):
                 reference += reference
       self.assertListEqual(__lowerCamelCase     ,       reference[: len(__lowerCamelCase )] )






  def 				lowercase_		(							self						:		str )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   42
       SCREAMING_SNAKE_CASE__   =   RandomIterableDataset()

       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )

       # Edge case with a very small dataset
       SCREAMING_SNAKE_CASE__   =   RandomIterableDataset(max_length=2 )

       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )
       self.check_iterable_dataset_shards(__lowerCamelCase     ,       __lowerCamelCase     ,       batch_size=4     ,       drop_last=__lowerCamelCase     ,       split_batches=__lowerCamelCase )






  def 				lowercase_		(							self						:		Optional[Any] )		->							Any:
       SCREAMING_SNAKE_CASE__   =   BatchSampler(range(16 )     ,       batch_size=4     ,       drop_last=__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   SkipBatchSampler(__lowerCamelCase     ,       2 )
       self.assertListEqual(list(__lowerCamelCase )     ,       [[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 				lowercase_		(							self						:		Union[str, Any] )		->							str:
       SCREAMING_SNAKE_CASE__   =   SkipDataLoader(list(range(16 ) )     ,       batch_size=4     ,       skip_batches=2 )
       self.assertListEqual([t.tolist() for t in dataloader]     ,       [[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 				lowercase_		(							self						:		Dict )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   DataLoader(list(range(16 ) )     ,       batch_size=4 )
       SCREAMING_SNAKE_CASE__   =   skip_first_batches(__lowerCamelCase     ,       num_batches=2 )
       self.assertListEqual([t.tolist() for t in new_dataloader]     ,       [[8, 9, 10, 11], [12, 13, 14, 15]] )






  def 				lowercase_		(							self						:		List[Any] )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   DataLoaderShard(list(range(16 ) )     ,       batch_size=4 )
       for idx, _ in enumerate(__lowerCamelCase ):
            self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3 )

       # Test it also works on the second iteration
       for idx, _ in enumerate(__lowerCamelCase ):
            self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3 )







  def 				lowercase_		(							self						:		Union[str, Any] )		->							str:
       Accelerator()
       SCREAMING_SNAKE_CASE__   =   DataLoaderDispatcher(range(16 )     ,       batch_size=4 )
       for idx, _ in enumerate(__lowerCamelCase ):
            self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3 )

       # Test it also works on the second iteration
       for idx, _ in enumerate(__lowerCamelCase ):
            self.assertEqual(dataloader.end_of_dataloader     ,       idx == 3 )


