

"""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     (    __snake_case  ):

				def __init__(     self							:    Dict					,  UpperCamelCase__							:    List[str]=0.01					,  UpperCamelCase__							:    Optional[int]=1_0_0_0)->       Dict:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Dict   				=      p_stop
						SCREAMING_SNAKE_CASE:	Tuple   				=      max_length




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



						'''simple docstring'''


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





class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    str=False					,  UpperCamelCase__							:    Tuple=True)->       Optional[Any]:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Dict   				=      [
						    BatchSamplerShard(UpperCamelCase__					,  2					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)
						    for i in range(2)
						]
						SCREAMING_SNAKE_CASE:	Tuple   				=      [list(UpperCamelCase__) for batch_sampler_shard in batch_sampler_shards]
						if not split_batches:
								self.assertListEqual([len(UpperCamelCase__) for shard in batch_sampler_shards]					,  [len(UpperCamelCase__) for e in expected])
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      BatchSampler(range(2_4)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Tuple   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      BatchSampler(range(2_4)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						# Expected shouldn't change
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

						# Check the shards when the dataset is a round multiple of batch size but not total batch size.
						SCREAMING_SNAKE_CASE:	List[str]   				=      BatchSampler(range(2_1)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Union[str, 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], [0, 1, 2]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	Tuple   				=      BatchSampler(range(2_1)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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(UpperCamelCase__					,  UpperCamelCase__)

						# 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:	int   				=      BatchSampler(range(2_2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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, 0, 1]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	int   				=      BatchSampler(range(2_2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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(UpperCamelCase__					,  UpperCamelCase__)

						# 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:	List[Any]   				=      BatchSampler(range(2_0)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	List[str]   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	Optional[int]   				=      BatchSampler(range(2_0)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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(UpperCamelCase__					,  UpperCamelCase__)

						# Check the shards when the dataset is very small.
						SCREAMING_SNAKE_CASE:	Tuple   				=      BatchSampler(range(2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      [[[0, 1, 0]], [[1, 0, 1]]]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	str   				=      BatchSampler(range(2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	str   				=      [[], []]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__)

				def 							lowercase_					(     self							:    List[Any])->       Dict:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Dict   				=      BatchSampler(range(2_4)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Dict   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	List[str]   				=      BatchSampler(range(2_4)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						# Expected shouldn't change
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						# Check the shards when the dataset is not a round multiple of batch size.
						SCREAMING_SNAKE_CASE:	Tuple   				=      BatchSampler(range(2_2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Optional[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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	List[Any]   				=      BatchSampler(range(2_2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						# Check the shards when the dataset is not a round multiple of batch size or num_processes.
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      BatchSampler(range(2_1)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	Any   				=      BatchSampler(range(2_1)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Dict   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						# Check the shards when the dataset is very small.
						SCREAMING_SNAKE_CASE:	Tuple   				=      BatchSampler(range(2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	List[str]   				=      [[[0, 1]], [[0, 1]]]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	str   				=      BatchSampler(range(2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Tuple   				=      [[], []]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__)

				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	List[Any]   				=      BatchSampler(range(2_4)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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], [2_1, 2_2, 2_3]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      BatchSampler(range(2_4)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						# Expected shouldn't change
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						# Check the shards when the dataset is a round multiple of batch size but not total batch size.
						SCREAMING_SNAKE_CASE:	List[Any]   				=      BatchSampler(range(2_1)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	int   				=      BatchSampler(range(2_1)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Tuple   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						# 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:	Tuple   				=      BatchSampler(range(2_2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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], [2_1]],
						]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	Dict   				=      BatchSampler(range(2_2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						# 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:	Optional[Any]   				=      BatchSampler(range(2_0)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	str   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	List[Any]   				=      BatchSampler(range(2_0)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Optional[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(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

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

						SCREAMING_SNAKE_CASE:	Dict   				=      BatchSampler(range(2)					,  batch_size=3					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Tuple   				=      [[], []]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  even_batches=UpperCamelCase__)

				def 							lowercase_					(     self							:    int)->       Tuple:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	List[Any]   				=      BatchSampler(range(2_4)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Optional[int]   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	List[Any]   				=      BatchSampler(range(2_4)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						# Expected shouldn't change
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

						# Check the shards when the dataset is not a round multiple of batch size.
						SCREAMING_SNAKE_CASE:	str   				=      BatchSampler(range(2_2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Optional[int]   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	int   				=      BatchSampler(range(2_2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

						# Check the shards when the dataset is not a round multiple of batch size or num_processes.
						SCREAMING_SNAKE_CASE:	Dict   				=      BatchSampler(range(2_1)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	List[str]   				=      [
						    [[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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	Tuple   				=      BatchSampler(range(2_1)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	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(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

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

						SCREAMING_SNAKE_CASE:	str   				=      BatchSampler(range(2)					,  batch_size=4					,  drop_last=UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      [[], []]
						self.check_batch_sampler_shards(UpperCamelCase__					,  UpperCamelCase__					,  split_batches=UpperCamelCase__					,  even_batches=UpperCamelCase__)

				def 							lowercase_					(     self							:    Union[str, Any])->       Union[str, Any]:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	int   				=      [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 1_0, 1_1], [1_2, 1_3]]
						SCREAMING_SNAKE_CASE:	Tuple   				=      [BatchSamplerShard(UpperCamelCase__					,  2					,  UpperCamelCase__					,  even_batches=UpperCamelCase__) 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 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Optional[Any]=False					,  UpperCamelCase__							:    str=2					,  UpperCamelCase__							:    Optional[int]=False)->       Union[str, Any]:



						'''simple docstring'''


						random.seed(UpperCamelCase__)
						SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      list(UpperCamelCase__)

						SCREAMING_SNAKE_CASE:	List[str]   				=      [
						    IterableDatasetShard(
						        UpperCamelCase__					,  batch_size=UpperCamelCase__					,  drop_last=UpperCamelCase__					,  num_processes=UpperCamelCase__					,  process_index=UpperCamelCase__					,  split_batches=UpperCamelCase__					,  )
						    for i in range(UpperCamelCase__)
						]
						SCREAMING_SNAKE_CASE:	Tuple   				=      []
						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(UpperCamelCase__)
								iterable_dataset_lists.append(list(UpperCamelCase__))

						SCREAMING_SNAKE_CASE:	Optional[int]   				=      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:	int   				=      iterable_dataset_lists[0]
						for l in iterable_dataset_lists[1:]:
								self.assertEqual(len(UpperCamelCase__)					,  len(UpperCamelCase__))
								self.assertTrue(len(UpperCamelCase__) % shard_batch_size == 0)

						SCREAMING_SNAKE_CASE:	Optional[int]   				=      []
						for idx in range(0					,  len(UpperCamelCase__)					,  UpperCamelCase__):
								for l in iterable_dataset_lists:
										observed += l[idx : idx + shard_batch_size]

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

				def 							lowercase_					(     self							:    int)->       Any:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	List[Any]   				=      4_2
						SCREAMING_SNAKE_CASE:	Optional[Any]   				=      RandomIterableDataset()

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

						# Edge case with a very small dataset
						SCREAMING_SNAKE_CASE:	int   				=      RandomIterableDataset(max_length=2)

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

				def 							lowercase_					(     self							:    Dict)->       Any:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Optional[Any])->       Dict:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	List[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 							lowercase_					(     self							:    Optional[Any])->       Optional[int]:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	List[str]   				=      DataLoader(list(range(1_6))					,  batch_size=4)
						SCREAMING_SNAKE_CASE:	str   				=      skip_first_batches(UpperCamelCase__					,  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 							lowercase_					(     self							:    str)->       str:



						'''simple docstring'''


						SCREAMING_SNAKE_CASE:	Any   				=      DataLoaderShard(list(range(1_6))					,  batch_size=4)
						for idx, _ in enumerate(UpperCamelCase__):
								self.assertEqual(dataloader.end_of_dataloader					,  idx == 3)

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




				def 							lowercase_					(     self							:    str)->       int:



						'''simple docstring'''


						Accelerator()
						SCREAMING_SNAKE_CASE:	List[str]   				=      DataLoaderDispatcher(range(1_6)					,  batch_size=4)
						for idx, _ in enumerate(UpperCamelCase__):
								self.assertEqual(dataloader.end_of_dataloader					,  idx == 3)

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





"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A						       =      logging.get_logger(__name__)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[str]:
		__lowerCAmelCase:	Dict   				=      original_name.split("."			)[0]
		__lowerCAmelCase:	Any   				=      key.split("."			)
		__lowerCAmelCase:	Union[str, Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 2]			)
		__lowerCAmelCase:	List[Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 1]			)
		__lowerCAmelCase:	List[str]   				=      orig_block_num - offset

		__lowerCAmelCase:	Tuple   				=      key.replace(F"{orig_block_num}.{layer_num}.{original_name}" ,			F"block.{new_block_num}.{layer_num}.{new_name}"			)
		return key






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		__lowerCAmelCase:	List[Any]   				=      OrderedDict()
		__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      0, 0
		for key, value in state_dict.items():
				if key.startswith("network"			):
						__lowerCAmelCase:	Dict   				=      key.replace("network" ,			"poolformer.encoder"			)
				if "proj" in key:
						# Works for the first embedding as well as the internal embedding layers
						if key.endswith("bias"			) and "patch_embed" not in key:
								patch_emb_offset += 1
						__lowerCAmelCase:	int   				=      key[: key.find("proj"			)]
						__lowerCAmelCase:	Dict   				=      key.replace(__SCREAMING_SNAKE_CASE ,			F"patch_embeddings.{total_embed_found}."			)
						__lowerCAmelCase:	Optional[int]   				=      key.replace("proj" ,			"projection"			)
						if key.endswith("bias"			):
								total_embed_found += 1
				if "patch_embeddings" in key:
						__lowerCAmelCase:	int   				=      "poolformer.encoder." + key
				if "mlp.fc1" in key:
						__lowerCAmelCase:	Optional[Any]   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"mlp.fc1" ,			"output.conv1"			)
				if "mlp.fc2" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"mlp.fc2" ,			"output.conv2"			)
				if "norm1" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"norm1" ,			"before_norm"			)
				if "norm2" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"norm2" ,			"after_norm"			)
				if "layer_scale_1" in key:
						__lowerCAmelCase:	Optional[int]   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"layer_scale_1" ,			"layer_scale_1"			)
				if "layer_scale_2" in key:
						__lowerCAmelCase:	Any   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"layer_scale_2" ,			"layer_scale_2"			)
				if "head" in key:
						__lowerCAmelCase:	int   				=      key.replace("head" ,			"classifier"			)
				__lowerCAmelCase:	Tuple   				=      value
		return new_state_dict






def    a__   (   )     ->     Tuple:
		__lowerCAmelCase:	Union[str, Any]   				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase:	int   				=      Image.open(requests.get(__SCREAMING_SNAKE_CASE ,			stream=__SCREAMING_SNAKE_CASE			).raw			)

		return image






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		__lowerCAmelCase:	Any   				=      PoolFormerConfig()

		# set attributes based on model_name
		__lowerCAmelCase:	Any   				=      "huggingface/label-files"
		__lowerCAmelCase:	int   				=      model_name[-3:]
		__lowerCAmelCase:	List[Any]   				=      1_0_0_0
		__lowerCAmelCase:	Tuple   				=      "imagenet-1k-id2label.json"
		__lowerCAmelCase:	str   				=      (1, 1_0_0_0)

		# set config attributes
		__lowerCAmelCase:	Dict   				=      json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			repo_type="dataset"			) ,			"r"			)			)
		__lowerCAmelCase:	List[str]   				=      {int(__SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
		__lowerCAmelCase:	Any   				=      idalabel
		__lowerCAmelCase:	Any   				=      {v: k for k, v in idalabel.items()}
		if size == "s12":
				__lowerCAmelCase:	Dict   				=      [2, 2, 6, 2]
				__lowerCAmelCase:	str   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	Optional[Any]   				=      4.0
				__lowerCAmelCase:	Union[str, Any]   				=      0.9
		elif size == "s24":
				__lowerCAmelCase:	Tuple   				=      [4, 4, 1_2, 4]
				__lowerCAmelCase:	List[str]   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	Tuple   				=      4.0
				__lowerCAmelCase:	Optional[int]   				=      0.9
		elif size == "s36":
				__lowerCAmelCase:	int   				=      [6, 6, 1_8, 6]
				__lowerCAmelCase:	int   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	List[str]   				=      4.0
				__lowerCAmelCase:	Dict   				=      1E-6
				__lowerCAmelCase:	List[Any]   				=      0.9
		elif size == "m36":
				__lowerCAmelCase:	Dict   				=      [6, 6, 1_8, 6]
				__lowerCAmelCase:	Dict   				=      [9_6, 1_9_2, 3_8_4, 7_6_8]
				__lowerCAmelCase:	str   				=      4.0
				__lowerCAmelCase:	Union[str, Any]   				=      1E-6
				__lowerCAmelCase:	Union[str, Any]   				=      0.95
		elif size == "m48":
				__lowerCAmelCase:	str   				=      [8, 8, 2_4, 8]
				__lowerCAmelCase:	Optional[int]   				=      [9_6, 1_9_2, 3_8_4, 7_6_8]
				__lowerCAmelCase:	str   				=      4.0
				__lowerCAmelCase:	int   				=      1E-6
				__lowerCAmelCase:	str   				=      0.95
		else:
				raise ValueError(F"Size {size} not supported"			)

		# load image processor
		__lowerCAmelCase:	Union[str, Any]   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)

		# Prepare image
		__lowerCAmelCase:	int   				=      prepare_img()
		__lowerCAmelCase:	Tuple   				=      image_processor(images=__SCREAMING_SNAKE_CASE ,			return_tensors="pt"			).pixel_values

		logger.info(F"Converting model {model_name}..."			)

		# load original state dict
		__lowerCAmelCase:	Optional[int]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)

		# rename keys
		__lowerCAmelCase:	Any   				=      rename_keys(__SCREAMING_SNAKE_CASE			)

		# create HuggingFace model and load state dict
		__lowerCAmelCase:	str   				=      PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE			)
		model.load_state_dict(__SCREAMING_SNAKE_CASE			)
		model.eval()

		# Define image processor
		__lowerCAmelCase:	Any   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      image_processor(images=prepare_img() ,			return_tensors="pt"			).pixel_values

		# forward pass
		__lowerCAmelCase:	int   				=      model(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.3045, -0.6758, -0.4869]			)
		elif size == "s24":
				__lowerCAmelCase:	Optional[int]   				=      torch.tensor([0.4402, -0.1374, -0.8045]			)
		elif size == "s36":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.6080, -0.5133, -0.5898]			)
		elif size == "m36":
				__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor([0.3952, 0.2263, -1.2668]			)
		elif size == "m48":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([0.1167, -0.0656, -0.3423]			)
		else:
				raise ValueError(F"Size {size} not supported"			)

		# verify logits
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-2			)

		# finally, save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."			)
		Path(__SCREAMING_SNAKE_CASE			).mkdir(exist_ok=__SCREAMING_SNAKE_CASE			)
		model.save_pretrained(__SCREAMING_SNAKE_CASE			)
		print(F"Saving image processor to {pytorch_dump_folder_path}"			)
		image_processor.save_pretrained(__SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()

			parser.add_argument(
			    "--model_name",
			    default="poolformer_s12",
			    type=str,
			    help="Name of the model you'd like to convert.",
			)
			parser.add_argument(
			    "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
			)
			__A						       =      parser.parse_args()
			convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


