
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        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_=0.01       ,   A_=1_000 )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  p_stop
				UpperCamelCase						=  max_length



		def __iter__(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  0
				UpperCamelCase						=  False
				while not stop and count < self.max_length:
						yield count
						count += 1
						UpperCamelCase						=  random.random() < self.p_stop






class        lowercase      (	unittest.TestCase     ):



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_=False       ,   A_=True )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  [
				    BatchSamplerShard(A_       ,   2       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )
				    for i in range(2 )
				]
				UpperCamelCase						=  [list(A_ ) for batch_sampler_shard in batch_sampler_shards]
				if not split_batches:
						self.assertListEqual([len(A_ ) for shard in batch_sampler_shards]       ,   [len(A_ ) for e in expected] )
				self.assertListEqual(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				# Check the shards when the dataset is a round multiple of total batch size.
				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_ )

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

				# Check the shards when the dataset is a round multiple of batch size but not total batch size.
				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_ )

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

				# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
				# num_processes batch.
				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_ )

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

				# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
				# num_processes batch.
				UpperCamelCase						=  BatchSampler(range(20 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_ )

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

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

				UpperCamelCase						=  BatchSampler(range(2 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [[], []]
				self.check_batch_sampler_shards(A_       ,   A_ )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				# Check the shards when the dataset is a round multiple of batch size.
				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_ )

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

				# Check the shards when the dataset is not a round multiple of batch size.
				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_ )

				# Check the shards when the dataset is not a round multiple of batch size or num_processes.
				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_ )

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

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



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				# Check the shards when the dataset is a round multiple of total batch size.
				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   even_batches=A_ )

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

				# Check the shards when the dataset is a round multiple of batch size but not total batch size.
				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   even_batches=A_ )

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

				# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
				# num_processes batch.
				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   even_batches=A_ )

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

				# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
				# num_processes batch.
				UpperCamelCase						=  BatchSampler(range(20 )       ,   batch_size=3       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   even_batches=A_ )

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

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

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



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				# Check the shards when the dataset is a round multiple of batch size.
				UpperCamelCase						=  BatchSampler(range(24 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

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

				# Check the shards when the dataset is not a round multiple of batch size.
				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(22 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				# Check the shards when the dataset is not a round multiple of batch size or num_processes.
				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

				UpperCamelCase						=  BatchSampler(range(21 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  [
				    [[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(A_       ,   A_       ,   split_batches=A_       ,   even_batches=A_ )

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

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



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
				UpperCamelCase						=  [BatchSamplerShard(A_       ,   2       ,   A_       ,   even_batches=A_ ) 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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_=False       ,   A_=2       ,   A_=False )						->      Union[str, Any]:
				"""simple docstring"""
				random.seed(A_ )
				UpperCamelCase						=  list(A_ )

				UpperCamelCase						=  [
				    IterableDatasetShard(
				        A_       ,   batch_size=A_       ,   drop_last=A_       ,   num_processes=A_       ,   process_index=A_       ,   split_batches=A_       ,   )
				    for i in range(A_ )
				]
				UpperCamelCase						=  []
				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(A_ )
						iterable_dataset_lists.append(list(A_ ) )

				UpperCamelCase						=  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
				UpperCamelCase						=  iterable_dataset_lists[0]
				for l in iterable_dataset_lists[1:]:
						self.assertEqual(len(A_ )       ,   len(A_ ) )
						self.assertTrue(len(A_ ) % shard_batch_size == 0 )

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

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



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  42
				UpperCamelCase						=  RandomIterableDataset()

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

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

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



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  BatchSampler(range(16 )       ,   batch_size=4       ,   drop_last=A_ )
				UpperCamelCase						=  SkipBatchSampler(A_       ,   2 )
				self.assertListEqual(list(A_ )       ,   [[8, 9, 10, 11], [12, 13, 14, 15]] )



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  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 __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  DataLoader(list(range(16 ) )       ,   batch_size=4 )
				UpperCamelCase						=  skip_first_batches(A_       ,   num_batches=2 )
				self.assertListEqual([t.tolist() for t in new_dataloader]       ,   [[8, 9, 10, 11], [12, 13, 14, 15]] )



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  DataLoaderShard(list(range(16 ) )       ,   batch_size=4 )
				for idx, _ in enumerate(A_ ):
						self.assertEqual(dataloader.end_of_dataloader       ,   idx == 3 )

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



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				Accelerator()
				UpperCamelCase						=  DataLoaderDispatcher(range(16 )       ,   batch_size=4 )
				for idx, _ in enumerate(A_ ):
						self.assertEqual(dataloader.end_of_dataloader       ,   idx == 3 )

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


import os

_UpperCAmelCase      :				int          = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}


def    A (      lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		UpperCamelCase						=  0
		while index < len(lowercase				) - 1:
				UpperCamelCase						=  SYMBOLS[numerals[index]]
				UpperCamelCase						=  SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''

		UpperCamelCase						=  num // 1_000
		numerals += m_count * "M"
		num %= 1_000

		UpperCamelCase						=  num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		UpperCamelCase						=  num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals


def    A (      lowercase = "/p089_roman.txt"				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		with open(os.path.dirname(lowercase				) + roman_numerals_filename				) as filea:
				UpperCamelCase						=  filea.readlines()

		for line in lines:
				UpperCamelCase						=  line.strip()
				UpperCamelCase						=  parse_roman_numerals(lowercase				)
				UpperCamelCase						=  generate_roman_numerals(lowercase				)
				savings += len(lowercase				) - len(lowercase				)

		return savings


if __name__ == "__main__":
					print(F'''{solution() = }''')
