




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
							import torch

							from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 		__A   (      unittest.TestCase      ):

						@slow
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=XLMRobertaModel.from_pretrained("""xlm-roberta-base"""			)
										__magic_name__						:						List[str]										=torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]			)
										# The dog is cute and lives in the garden house

										__magic_name__						:						Optional[Any]										=torch.Size((1, 12, 7_68)			)  # batch_size, sequence_length, embedding_vector_dim
										__magic_name__						:						List[str]										=torch.tensor(
										    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]			)
										#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
										#  xlmr.eval()
										#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
										with torch.no_grad():
														__magic_name__						:						Union[str, Any]										=model(__snake_case			)["""last_hidden_state"""].detach()
										self.assertEqual(output.shape     ,			__snake_case			)
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]     ,			__snake_case     ,			atol=1E-3			)			)


						@slow
						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						str										=XLMRobertaModel.from_pretrained("""xlm-roberta-large"""			)
										__magic_name__						:						int										=torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]]			)
										# The dog is cute and lives in the garden house

										__magic_name__						:						Any										=torch.Size((1, 12, 10_24)			)  # batch_size, sequence_length, embedding_vector_dim
										__magic_name__						:						Optional[int]										=torch.tensor(
										    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]			)
										#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
										#  xlmr.eval()
										#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
										with torch.no_grad():
														__magic_name__						:						List[str]										=model(__snake_case			)["""last_hidden_state"""].detach()
										self.assertEqual(output.shape     ,			__snake_case			)
										# compare the actual values for a slice of last dim
										self.assertTrue(torch.allclose(output[:, :, -1]     ,			__snake_case     ,			atol=1E-3			)			)






import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



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




			'''simple docstring'''


			for shard in shards:
						for i in range(a	):
									yield {"i": i, "shard": shard}


def    a      ( )     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= int(os.environ['''RANK''']	)
			SCREAMING_SNAKE_CASE     						= int(os.environ['''WORLD_SIZE''']	)

			SCREAMING_SNAKE_CASE     						= ArgumentParser()
			parser.add_argument('''--streaming'''		,						type=a	)
			parser.add_argument('''--local_rank'''		,						type=a	)
			parser.add_argument('''--num_workers'''		,						type=a		,						default=0	)
			SCREAMING_SNAKE_CASE     						= parser.parse_args()
			SCREAMING_SNAKE_CASE     						= args.streaming
			SCREAMING_SNAKE_CASE     						= args.num_workers

			SCREAMING_SNAKE_CASE     						= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

			SCREAMING_SNAKE_CASE     						= NUM_SHARDS * NUM_ITEMS_PER_SHARD
			SCREAMING_SNAKE_CASE     						= full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)	)

			SCREAMING_SNAKE_CASE     						= sum(1 for _ in dataloader	)
			if local_size != expected_local_size:
						raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()