



"""simple docstring"""






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 			__snake_case			(      _lowercase):




  def __init__(					self	:		Tuple		,   __lowerCAmelCase	:		str=0.01		,   __lowerCAmelCase	:		Any=1_0_0_0						):






         """simple docstring"""






         _lowerCamelCase		:							List[str]            =					p_stop
         _lowerCamelCase		:							List[Any]            =					max_length




  def __iter__(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							int            =					0
         _lowerCamelCase		:							Dict            =					False
         while not stop and count < self.max_length:
                yield count
                count += 1
                _lowerCamelCase		:							int            =					random.random() < self.p_stop






class 			__snake_case			(      unittest.TestCase):




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		Tuple		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		int=False		,   __lowerCAmelCase	:		Dict=True						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[Any]            =					[
             BatchSamplerShard(__lowerCAmelCase		,   2		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase		,   even_batches=__lowerCAmelCase						)
             for i in range(2						)
         ]
         _lowerCamelCase		:							List[str]            =					[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 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					BatchSampler(range(2_4						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							List[str]            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							Union[str, Any]            =					BatchSampler(range(2_4						)		,   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.
         _lowerCamelCase		:							str            =					BatchSampler(range(2_1						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Dict            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [0, 1, 2]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							Tuple            =					BatchSampler(range(2_1						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							str            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
         ]
         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.
         _lowerCamelCase		:							Optional[Any]            =					BatchSampler(range(2_2						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							int            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 0, 1]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							Dict            =					BatchSampler(range(2_2						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Any            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
         ]
         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.
         _lowerCamelCase		:							Tuple            =					BatchSampler(range(2_0						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Dict            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 0]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [1, 2, 3]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							str            =					BatchSampler(range(2_0						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							str            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase						)

         # Check the shards when the dataset is very small.
         _lowerCamelCase		:							int            =					BatchSampler(range(2						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					[[[0, 1, 0]], [[1, 0, 1]]]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							List[Any]            =					BatchSampler(range(2						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[Any]            =					[[], []]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					BatchSampler(range(2_4						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							List[str]            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							List[Any]            =					BatchSampler(range(2_4						)		,   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.
         _lowerCamelCase		:							Optional[Any]            =					BatchSampler(range(2_2						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Any            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [0, 1]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							List[str]            =					BatchSampler(range(2_2						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Union[str, Any]            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
         ]
         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.
         _lowerCamelCase		:							Union[str, Any]            =					BatchSampler(range(2_1						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 0]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [1, 2]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							Dict            =					BatchSampler(range(2_1						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Tuple            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase						)

         # Check the shards when the dataset is very small.
         _lowerCamelCase		:							Optional[Any]            =					BatchSampler(range(2						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Union[str, Any]            =					[[[0, 1]], [[0, 1]]]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							Tuple            =					BatchSampler(range(2						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Dict            =					[[], []]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							int            =					BatchSampler(range(2_4						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							int            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1, 2_2, 2_3]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							Union[str, Any]            =					BatchSampler(range(2_4						)		,   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.
         _lowerCamelCase		:							Tuple            =					BatchSampler(range(2_1						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Any            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							int            =					BatchSampler(range(2_1						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Dict            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
         ]
         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.
         _lowerCamelCase		:							Union[str, Any]            =					BatchSampler(range(2_2						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							List[str]            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9, 2_0]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7], [2_1]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							Optional[int]            =					BatchSampler(range(2_2						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							str            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
         ]
         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.
         _lowerCamelCase		:							List[Any]            =					BatchSampler(range(2_0						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							int            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4], [1_8, 1_9]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							Any            =					BatchSampler(range(2_0						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					[
             [[0, 1, 2], [6, 7, 8], [1_2, 1_3, 1_4]],
             [[3, 4, 5], [9, 1_0, 1_1], [1_5, 1_6, 1_7]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         # Check the shards when the dataset is very small.
         _lowerCamelCase		:							List[Any]            =					BatchSampler(range(2						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Any            =					[[[0, 1]], []]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							Any            =					BatchSampler(range(2						)		,   batch_size=3		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Tuple            =					[[], []]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   even_batches=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[Any]            =					BatchSampler(range(2_4						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Union[str, Any]            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9], [2_2, 2_3]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							List[Any]            =					BatchSampler(range(2_4						)		,   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.
         _lowerCamelCase		:							List[str]            =					BatchSampler(range(2_2						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							str            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0, 2_1]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							Dict            =					BatchSampler(range(2_2						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[Any]            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
         ]
         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.
         _lowerCamelCase		:							str            =					BatchSampler(range(2_1						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Union[str, Any]            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7], [2_0]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

         _lowerCamelCase		:							Any            =					BatchSampler(range(2_1						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							List[str]            =					[
             [[0, 1], [4, 5], [8, 9], [1_2, 1_3], [1_6, 1_7]],
             [[2, 3], [6, 7], [1_0, 1_1], [1_4, 1_5], [1_8, 1_9]],
         ]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase		,   even_batches=__lowerCAmelCase						)

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

         _lowerCamelCase		:							Any            =					BatchSampler(range(2						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							List[str]            =					[[], []]
         self.check_batch_sampler_shards(__lowerCAmelCase		,   __lowerCAmelCase		,   split_batches=__lowerCAmelCase		,   even_batches=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					[[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
         _lowerCamelCase		:							Any            =					[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], [1_2, 1_3]]						)
         self.assertListEqual(list(batch_sampler_shards[1]						)		,   [[3, 4], [9, 1_0, 1_1]]						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		Tuple=False		,   __lowerCAmelCase	:		Any=2		,   __lowerCAmelCase	:		Any=False						):






         """simple docstring"""






         random.seed(__lowerCAmelCase						)
         _lowerCamelCase		:							int            =					list(__lowerCAmelCase						)

         _lowerCamelCase		:							Optional[int]            =					[
             IterableDatasetShard(
                 __lowerCAmelCase		,   batch_size=__lowerCAmelCase		,   drop_last=__lowerCAmelCase		,   num_processes=__lowerCAmelCase		,   process_index=__lowerCAmelCase		,   split_batches=__lowerCAmelCase		,   )
             for i in range(__lowerCAmelCase						)
         ]
         _lowerCamelCase		:							Union[str, Any]            =					[]
         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						)						)

         _lowerCamelCase		:							Dict            =					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
         _lowerCamelCase		:							Union[str, Any]            =					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						)

         _lowerCamelCase		:							List[str]            =					[]
         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 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					4_2
         _lowerCamelCase		:							Any            =					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
         _lowerCamelCase		:							Optional[Any]            =					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 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					BatchSampler(range(1_6						)		,   batch_size=4		,   drop_last=__lowerCAmelCase						)
         _lowerCamelCase		:							Dict            =					SkipBatchSampler(__lowerCAmelCase		,   2						)
         self.assertListEqual(list(__lowerCAmelCase						)		,   [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					SkipDataLoader(list(range(1_6						)						)		,   batch_size=4		,   skip_batches=2						)
         self.assertListEqual([t.tolist() for t in dataloader]		,   [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					DataLoader(list(range(1_6						)						)		,   batch_size=4						)
         _lowerCamelCase		:							str            =					skip_first_batches(__lowerCAmelCase		,   num_batches=2						)
         self.assertListEqual([t.tolist() for t in new_dataloader]		,   [[8, 9, 1_0, 1_1], [1_2, 1_3, 1_4, 1_5]]						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         _lowerCamelCase		:							Tuple            =					DataLoaderShard(list(range(1_6						)						)		,   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 		SCREAMING_SNAKE_CASE							(					self	:		Dict						):






         """simple docstring"""






         Accelerator()
         _lowerCamelCase		:							Any            =					DataLoaderDispatcher(range(1_6						)		,   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						)






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__							:						int							     =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase=False				)    ->				int:
  snake_case__						= []
  for i in range(config.num_hidden_layers				):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")				)
    rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")				)
    rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")				)
    rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")				)
    rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")				)
    rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")				)
    rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")				)
    rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")				)
    rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")				)
    rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")				)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''vit.embeddings.cls_token'''),
          ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
      ]				)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('''norm.weight''', '''layernorm.weight'''),
            ('''norm.bias''', '''layernorm.bias'''),
            ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
            ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
        ]				)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    snake_case__						= [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''				) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('''norm.weight''', '''vit.layernorm.weight'''),
            ('''norm.bias''', '''vit.layernorm.bias'''),
            ('''head.weight''', '''classifier.weight'''),
            ('''head.bias''', '''classifier.bias'''),
        ]				)

  return rename_keys







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=False				)    ->				Dict:
  for i in range(config.num_hidden_layers				):
    if base_model:
      snake_case__						= ''''''
    else:
      snake_case__						= '''vit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    snake_case__						= state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
    snake_case__						= state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""				)
    # next, add query, keys and values (in that order) to the state dict
    snake_case__						= in_proj_weight[
        : config.hidden_size, :
    ]
    snake_case__						= in_proj_bias[: config.hidden_size]
    snake_case__						= in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    snake_case__						= in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    snake_case__						= in_proj_weight[
        -config.hidden_size :, :
    ]
    snake_case__						= in_proj_bias[-config.hidden_size :]







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  snake_case__						= ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(__lowerCAmelCase							,   __lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Union[str, Any]:
  snake_case__						= dct.pop(__lowerCAmelCase				)
  snake_case__						= val







def SCREAMING_SNAKE_CASE	(			)    ->				str:
  snake_case__						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  snake_case__						= Image.open(requests.get(__lowerCAmelCase							,   stream=__lowerCAmelCase				).raw				)
  return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Dict:
  snake_case__						= ViTConfig()
  snake_case__						= False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    snake_case__						= True
    snake_case__						= int(vit_name[-12:-10]				)
    snake_case__						= int(vit_name[-9:-6]				)
  else:
    snake_case__						= 1000
    snake_case__						= '''huggingface/label-files'''
    snake_case__						= '''imagenet-1k-id2label.json'''
    snake_case__						= json.load(open(hf_hub_download(__lowerCAmelCase							,   __lowerCAmelCase							,   repo_type='''dataset'''				)							,   '''r'''				)				)
    snake_case__						= {int(__lowerCAmelCase				): v for k, v in idalabel.items()}
    snake_case__						= idalabel
    snake_case__						= {v: k for k, v in idalabel.items()}
    snake_case__						= int(vit_name[-6:-4]				)
    snake_case__						= int(vit_name[-3:]				)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith('''tiny'''				):
      snake_case__						= 192
      snake_case__						= 768
      snake_case__						= 12
      snake_case__						= 3
    elif vit_name[9:].startswith('''small'''				):
      snake_case__						= 384
      snake_case__						= 1536
      snake_case__						= 12
      snake_case__						= 6
    else:
      pass
  else:
    if vit_name[4:].startswith('''small'''				):
      snake_case__						= 768
      snake_case__						= 2304
      snake_case__						= 8
      snake_case__						= 8
    elif vit_name[4:].startswith('''base'''				):
      pass
    elif vit_name[4:].startswith('''large'''				):
      snake_case__						= 1024
      snake_case__						= 4096
      snake_case__						= 24
      snake_case__						= 16
    elif vit_name[4:].startswith('''huge'''				):
      snake_case__						= 1280
      snake_case__						= 5120
      snake_case__						= 32
      snake_case__						= 16

    # load original model from timm
  snake_case__						= timm.create_model(__lowerCAmelCase							,   pretrained=__lowerCAmelCase				)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  snake_case__						= timm_model.state_dict()
  if base_model:
    remove_classification_head_(__lowerCAmelCase				)
  snake_case__						= create_rename_keys(__lowerCAmelCase							,   __lowerCAmelCase				)
  for src, dest in rename_keys:
    rename_key(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)
  read_in_q_k_v(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    snake_case__						= ViTModel(__lowerCAmelCase				).eval()
  else:
    snake_case__						= ViTForImageClassification(__lowerCAmelCase				).eval()
  model.load_state_dict(__lowerCAmelCase				)

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    snake_case__						= DeiTImageProcessor(size=config.image_size				)
  else:
    snake_case__						= ViTImageProcessor(size=config.image_size				)
  snake_case__						= image_processor(images=prepare_img()							,   return_tensors='''pt'''				)
  snake_case__						= encoding['''pixel_values''']
  snake_case__						= model(__lowerCAmelCase				)

  if base_model:
    snake_case__						= timm_model.forward_features(__lowerCAmelCase				)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(__lowerCAmelCase							,   outputs.pooler_output							,   atol=1e-3				)
  else:
    snake_case__						= timm_model(__lowerCAmelCase				)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(__lowerCAmelCase							,   outputs.logits							,   atol=1e-3				)

  Path(__lowerCAmelCase				).mkdir(exist_ok=__lowerCAmelCase				)
  print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""				)
  model.save_pretrained(__lowerCAmelCase				)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
  image_processor.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--vit_name""",
       default="""vit_base_patch16_224""",
       type=str,
       help="""Name of the ViT timm model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   lowerCamelCase__							:						str							     =   parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

