



"""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 					A_(snake_case_      ):







			"""simple docstring"""



			def __init__(	self				,   A=0.0_1				,   A=1000							):
						_lowerCamelCase					:					Optional[int]	       =    p_stop
						_lowerCamelCase					:					List[Any]	       =    max_length

			def __iter__(	self							):
						_lowerCamelCase					:					Tuple	       =    0
						_lowerCamelCase					:					List[str]	       =    False
						while not stop and count < self.max_length:
									yield count
									count += 1
									_lowerCamelCase					:					List[str]	       =    random.random() < self.p_stop







class 					A_(unittest.TestCase      ):







			"""simple docstring"""



			def    _lowerCAmelCase		(	self				,   A				,   A				,   A=False				,   A=True							):
						_lowerCamelCase					:					Optional[Any]	       =    [
						    BatchSamplerShard(A_				,   2				,   A_				,   split_batches=A_				,   even_batches=A_							)
						    for i in range(2							)
						]
						_lowerCamelCase					:					int	       =    [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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    BatchSampler(range(24							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Tuple	       =    [
						    [[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_							)

						_lowerCamelCase					:					Dict	       =    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.
						_lowerCamelCase					:					Tuple	       =    BatchSampler(range(21							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Tuple	       =    [
						    [[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_							)

						_lowerCamelCase					:					int	       =    BatchSampler(range(21							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[int]	       =    [
						    [[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.
						_lowerCamelCase					:					Optional[int]	       =    BatchSampler(range(22							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[int]	       =    [
						    [[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_							)

						_lowerCamelCase					:					Optional[int]	       =    BatchSampler(range(22							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[int]	       =    [
						    [[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.
						_lowerCamelCase					:					Dict	       =    BatchSampler(range(20							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Tuple	       =    [
						    [[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_							)

						_lowerCamelCase					:					int	       =    BatchSampler(range(20							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Any	       =    [
						    [[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.
						_lowerCamelCase					:					Optional[int]	       =    BatchSampler(range(2							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Union[str, Any]	       =    [[[0, 1, 0]], [[1, 0, 1]]]
						self.check_batch_sampler_shards(A_				,   A_							)

						_lowerCamelCase					:					Tuple	       =    BatchSampler(range(2							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					str	       =    [[], []]
						self.check_batch_sampler_shards(A_				,   A_							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    BatchSampler(range(24							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					List[Any]	       =    [
						    [[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_							)

						_lowerCamelCase					:					Any	       =    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.
						_lowerCamelCase					:					Optional[int]	       =    BatchSampler(range(22							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[Any]	       =    [
						    [[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_							)

						_lowerCamelCase					:					int	       =    BatchSampler(range(22							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[Any]	       =    [
						    [[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.
						_lowerCamelCase					:					List[Any]	       =    BatchSampler(range(21							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					List[Any]	       =    [
						    [[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_							)

						_lowerCamelCase					:					Union[str, Any]	       =    BatchSampler(range(21							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[Any]	       =    [
						    [[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.
						_lowerCamelCase					:					str	       =    BatchSampler(range(2							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					str	       =    [[[0, 1]], [[0, 1]]]
						self.check_batch_sampler_shards(A_				,   A_				,   split_batches=A_							)

						_lowerCamelCase					:					str	       =    BatchSampler(range(2							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Dict	       =    [[], []]
						self.check_batch_sampler_shards(A_				,   A_				,   split_batches=A_							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					str	       =    BatchSampler(range(24							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Any	       =    [
						    [[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_							)

						_lowerCamelCase					:					str	       =    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.
						_lowerCamelCase					:					Optional[Any]	       =    BatchSampler(range(21							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Tuple	       =    [
						    [[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_							)

						_lowerCamelCase					:					Any	       =    BatchSampler(range(21							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					int	       =    [
						    [[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.
						_lowerCamelCase					:					Union[str, Any]	       =    BatchSampler(range(22							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					str	       =    [
						    [[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_							)

						_lowerCamelCase					:					Dict	       =    BatchSampler(range(22							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Tuple	       =    [
						    [[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.
						_lowerCamelCase					:					Optional[Any]	       =    BatchSampler(range(20							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					List[str]	       =    [
						    [[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_							)

						_lowerCamelCase					:					List[str]	       =    BatchSampler(range(20							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					int	       =    [
						    [[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.
						_lowerCamelCase					:					Optional[int]	       =    BatchSampler(range(2							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[Any]	       =    [[[0, 1]], []]
						self.check_batch_sampler_shards(A_				,   A_				,   even_batches=A_							)

						_lowerCamelCase					:					List[str]	       =    BatchSampler(range(2							)				,   batch_size=3				,   drop_last=A_							)
						_lowerCamelCase					:					Tuple	       =    [[], []]
						self.check_batch_sampler_shards(A_				,   A_				,   even_batches=A_							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    BatchSampler(range(24							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[int]	       =    [
						    [[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_							)

						_lowerCamelCase					:					Optional[int]	       =    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.
						_lowerCamelCase					:					Optional[Any]	       =    BatchSampler(range(22							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[Any]	       =    [
						    [[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_							)

						_lowerCamelCase					:					Optional[Any]	       =    BatchSampler(range(22							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Tuple	       =    [
						    [[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.
						_lowerCamelCase					:					Union[str, Any]	       =    BatchSampler(range(21							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					List[str]	       =    [
						    [[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_							)

						_lowerCamelCase					:					Union[str, Any]	       =    BatchSampler(range(21							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					List[Any]	       =    [
						    [[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.
						_lowerCamelCase					:					str	       =    BatchSampler(range(2							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[Any]	       =    [[[0, 1]], []]
						self.check_batch_sampler_shards(A_				,   A_				,   split_batches=A_				,   even_batches=A_							)

						_lowerCamelCase					:					str	       =    BatchSampler(range(2							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Tuple	       =    [[], []]
						self.check_batch_sampler_shards(A_				,   A_				,   split_batches=A_				,   even_batches=A_							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Tuple	       =    [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
						_lowerCamelCase					:					str	       =    [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    _lowerCAmelCase		(	self				,   A				,   A				,   A				,   A=False				,   A=2				,   A=False							):
						random.seed(A_							)
						_lowerCamelCase					:					Any	       =    list(A_							)

						_lowerCamelCase					:					Any	       =    [
						    IterableDatasetShard(
						        A_				,   batch_size=A_				,   drop_last=A_				,   num_processes=A_				,   process_index=A_				,   split_batches=A_				,   )
						    for i in range(A_							)
						]
						_lowerCamelCase					:					Dict	       =    []
						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_							)							)

						_lowerCamelCase					:					Any	       =    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(A_							)				,   len(A_							)							)
									self.assertTrue(len(A_							) % shard_batch_size == 0							)

						_lowerCamelCase					:					str	       =    []
						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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[str]	       =    42
						_lowerCamelCase					:					Union[str, Any]	       =    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
						_lowerCamelCase					:					Optional[Any]	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[int]	       =    BatchSampler(range(16							)				,   batch_size=4				,   drop_last=A_							)
						_lowerCamelCase					:					Optional[Any]	       =    SkipBatchSampler(A_				,   2							)
						self.assertListEqual(list(A_							)				,   [[8, 9, 10, 11], [12, 13, 14, 15]]							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					str	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Dict	       =    DataLoader(list(range(16							)							)				,   batch_size=4							)
						_lowerCamelCase					:					Optional[int]	       =    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    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					List[Any]	       =    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    _lowerCAmelCase		(	self							):
						Accelerator()
						_lowerCamelCase					:					Optional[Any]	       =    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							)







'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase					:    Any           = logging.get_logger(__name__)

lowerCAmelCase					:    Optional[int]           = {
    'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""perceiver"""







				def __init__(       self						,				A_=256						,				A_=1280						,				A_=768						,				A_=1						,				A_=26						,				A_=8						,				A_=8						,				A_=None						,				A_=None						,				A_="kv"						,				A_=1						,				A_=1						,				A_="gelu"						,				A_=0.1						,				A_=0.02						,				A_=1e-12						,				A_=True						,				A_=262						,				A_=2048						,				A_=56						,				A_=[368, 496]						,				A_=16						,				A_=1920						,				A_=16						,				A_=[1, 16, 224, 224]						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_     )

											UpperCamelCase   			=     num_latents
											UpperCamelCase   			=     d_latents
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     num_blocks
											UpperCamelCase   			=     num_self_attends_per_block
											UpperCamelCase   			=     num_self_attention_heads
											UpperCamelCase   			=     num_cross_attention_heads
											UpperCamelCase   			=     qk_channels
											UpperCamelCase   			=     v_channels
											UpperCamelCase   			=     cross_attention_shape_for_attention
											UpperCamelCase   			=     self_attention_widening_factor
											UpperCamelCase   			=     cross_attention_widening_factor
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     use_query_residual
											# masked language modeling attributes
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     max_position_embeddings
											# image classification attributes
											UpperCamelCase   			=     image_size
											# flow attributes
											UpperCamelCase   			=     train_size
											# multimodal autoencoding attributes
											UpperCamelCase   			=     num_frames
											UpperCamelCase   			=     audio_samples_per_frame
											UpperCamelCase   			=     samples_per_patch
											UpperCamelCase   			=     output_shape


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@property
				def 						UpperCAmelCase_    (       self     )->		Mapping[str, Mapping[int, str]]:







											'''simple docstring'''






											if self.task == "multiple-choice":
																		UpperCamelCase   			=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		UpperCamelCase   			=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('inputs', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]     )







				@property
				def 						UpperCAmelCase_    (       self     )->		float:







											'''simple docstring'''






											return 1e-4







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = -1						,				A_ = -1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 40						,				A_ = 40						,				)->		Mapping[str, Any]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_batch						,				num_token_to_add=0     )
																		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																		UpperCamelCase   			=     preprocessor.num_special_tokens_to_add(A_     )
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_sequence						,				num_token_to_add=A_     )
																		# Generate dummy inputs according to compute batch and sequence
																		UpperCamelCase   			=     [' '.join(['a']     ) * seq_length] * batch_size
																		UpperCamelCase   			=     dict(preprocessor(A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('input_ids'     )
																		return inputs
											elif isinstance(A_						,				A_     ) and preprocessor.model_input_names[0] == "pixel_values":
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(A_						,				fixed_dimension=OnnxConfig.default_fixed_batch     )
																		UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
																		UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('pixel_values'     )
																		return inputs
											else:
																		raise ValueError(
																		    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'     )




