




"""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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase=0.01  ,      lowercase=1000  ):
							_lowerCamelCase     :			List[str]    =    p_stop
							_lowerCamelCase     :			str    =    max_length






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






class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase=False  ,      lowercase=True  ):
							_lowerCamelCase     :			List[Any]    =    [
							    BatchSamplerShard(lowercase  ,      2  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )
							    for i in range(2  )
							]
							_lowerCamelCase     :			List[Any]    =    [list(lowercase  ) for batch_sampler_shard in batch_sampler_shards]
							if not split_batches:
											self.assertListEqual([len(lowercase  ) for shard in batch_sampler_shards]  ,      [len(lowercase  ) for e in expected]  )
							self.assertListEqual(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							# Check the shards when the dataset is a round multiple of total batch size.
							_lowerCamelCase     :			List[str]    =    BatchSampler(range(24  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			int    =    [
							    [[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(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    BatchSampler(range(24  )  ,      batch_size=3  ,      drop_last=lowercase  )
							# Expected shouldn't change
							self.check_batch_sampler_shards(lowercase  ,      lowercase  )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(21  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    [
							    [[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(lowercase  ,      lowercase  )

							_lowerCamelCase     :			Optional[int]    =    BatchSampler(range(21  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    [
							    [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
							    [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
							]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  )

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

							_lowerCamelCase     :			List[str]    =    BatchSampler(range(22  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_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(lowercase  ,      lowercase  )

							# 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     :			Union[str, Any]    =    BatchSampler(range(20  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [
							    [[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(lowercase  ,      lowercase  )

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

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

							_lowerCamelCase     :			Optional[int]    =    BatchSampler(range(2  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [[], []]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							# Check the shards when the dataset is a round multiple of batch size.
							_lowerCamelCase     :			int    =    BatchSampler(range(24  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Union[str, 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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							_lowerCamelCase     :			int    =    BatchSampler(range(24  )  ,      batch_size=4  ,      drop_last=lowercase  )
							# Expected shouldn't change
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      split_batches=lowercase  )

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

							_lowerCamelCase     :			int    =    BatchSampler(range(22  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[str]    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							# Check the shards when the dataset is not a round multiple of batch size or num_processes.
							_lowerCamelCase     :			Optional[Any]    =    BatchSampler(range(21  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

							_lowerCamelCase     :			Tuple    =    BatchSampler(range(21  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			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(lowercase  ,      lowercase  ,      split_batches=lowercase  )

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

							_lowerCamelCase     :			str    =    BatchSampler(range(2  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[str]    =    [[], []]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      split_batches=lowercase  )
			def 					A_    (  self  ):
							# Check the shards when the dataset is a round multiple of total batch size.
							_lowerCamelCase     :			Dict    =    BatchSampler(range(24  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			str    =    [
							    [[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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			Union[str, Any]    =    BatchSampler(range(24  )  ,      batch_size=3  ,      drop_last=lowercase  )
							# Expected shouldn't change
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      even_batches=lowercase  )

							# Check the shards when the dataset is a round multiple of batch size but not total batch size.
							_lowerCamelCase     :			str    =    BatchSampler(range(21  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Union[str, Any]    =    [
							    [[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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

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

							# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
							# num_processes batch.
							_lowerCamelCase     :			List[str]    =    BatchSampler(range(22  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_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]],
							]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			Tuple    =    BatchSampler(range(22  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

							# 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     :			Any    =    BatchSampler(range(20  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			int    =    [
							    [[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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			str    =    BatchSampler(range(20  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_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(lowercase  ,      lowercase  ,      even_batches=lowercase  )

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

							_lowerCamelCase     :			int    =    BatchSampler(range(2  )  ,      batch_size=3  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    [[], []]
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      even_batches=lowercase  )
			def 					A_    (  self  ):
							# Check the shards when the dataset is a round multiple of batch size.
							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(24  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(24  )  ,      batch_size=4  ,      drop_last=lowercase  )
							# Expected shouldn't change
							self.check_batch_sampler_shards(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

							# Check the shards when the dataset is not a round multiple of batch size.
							_lowerCamelCase     :			int    =    BatchSampler(range(22  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(22  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			int    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

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

							_lowerCamelCase     :			List[Any]    =    BatchSampler(range(21  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			Optional[int]    =    [
							    [[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(lowercase  ,      lowercase  ,      split_batches=lowercase  ,      even_batches=lowercase  )

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

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

							_lowerCamelCase     :			Dict    =    [
							    IterableDatasetShard(
							        lowercase  ,      batch_size=lowercase  ,      drop_last=lowercase  ,      num_processes=lowercase  ,      process_index=lowercase  ,      split_batches=lowercase  ,      )
							    for i in range(lowercase  )
							]
							_lowerCamelCase     :			Optional[int]    =    []
							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(lowercase  )
											iterable_dataset_lists.append(list(lowercase  )  )

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

							_lowerCamelCase     :			List[Any]    =    []
							for idx in range(0  ,      len(lowercase  )  ,      lowercase  ):
											for l in iterable_dataset_lists:
															observed += l[idx : idx + shard_batch_size]

							if not drop_last:
											while len(lowercase  ) < len(lowercase  ):
															reference += reference
							self.assertListEqual(lowercase  ,      reference[: len(lowercase  )]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    42
							_lowerCamelCase     :			Tuple    =    RandomIterableDataset()

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

							# Edge case with a very small dataset
							_lowerCamelCase     :			Optional[Any]    =    RandomIterableDataset(max_length=2  )

							self.check_iterable_dataset_shards(lowercase  ,      lowercase  ,      batch_size=4  ,      drop_last=lowercase  ,      split_batches=lowercase  )
							self.check_iterable_dataset_shards(lowercase  ,      lowercase  ,      batch_size=4  ,      drop_last=lowercase  ,      split_batches=lowercase  )
							self.check_iterable_dataset_shards(lowercase  ,      lowercase  ,      batch_size=4  ,      drop_last=lowercase  ,      split_batches=lowercase  )
							self.check_iterable_dataset_shards(lowercase  ,      lowercase  ,      batch_size=4  ,      drop_last=lowercase  ,      split_batches=lowercase  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    BatchSampler(range(16  )  ,      batch_size=4  ,      drop_last=lowercase  )
							_lowerCamelCase     :			List[Any]    =    SkipBatchSampler(lowercase  ,      2  )
							self.assertListEqual(list(lowercase  )  ,      [[8, 9, 10, 11], [12, 13, 14, 15]]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    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 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    DataLoader(list(range(16  )  )  ,      batch_size=4  )
							_lowerCamelCase     :			Union[str, Any]    =    skip_first_batches(lowercase  ,      num_batches=2  )
							self.assertListEqual([t.tolist() for t in new_dataloader]  ,      [[8, 9, 10, 11], [12, 13, 14, 15]]  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[Any]    =    DataLoaderShard(list(range(16  )  )  ,      batch_size=4  )
							for idx, _ in enumerate(lowercase  ):
											self.assertEqual(dataloader.end_of_dataloader  ,      idx == 3  )

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






			def 					A_    (  self  ):
							Accelerator()
							_lowerCamelCase     :			List[str]    =    DataLoaderDispatcher(range(16  )  ,      batch_size=4  )
							for idx, _ in enumerate(lowercase  ):
											self.assertEqual(dataloader.end_of_dataloader  ,      idx == 3  )

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

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 			UpperCAmelCase  (						pl.LightningModule					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        lowercase__				:    List[str]  =					model
        lowercase__				:    Dict  =					2
        lowercase__				:    Any  =					nn.Linear(self.model.config.hidden_size	,   self.num_labels )


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

 # init longformer question answering model
 lowercase__				:    Optional[int]  =					LongformerForQuestionAnswering.from_pretrained(UpperCAmelCase     )

 # transfer weights
 longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()     )
 longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()     )
 longformer_for_qa.eval()

 # save model
 longformer_for_qa.save_pretrained(UpperCAmelCase     )

 print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""     )


if __name__ == "__main__":
  __a: List[Any]          =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--longformer_model""",
      default=None,
      type=str,
      required=True,
      help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
  )
  parser.add_argument(
      """--longformer_question_answering_ckpt_path""",
      default=None,
      type=str,
      required=True,
      help="""Path the official PyTorch Lightning Checkpoint.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

