






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer




@require_torch
class 							a_ (						unittest.TestCase		):


							'''simple docstring'''






							@slow
							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'   )
														model.to(lowercase_   )

														# encoder-decoder outputs
														lowerCAmelCase_										=     torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]   ).to(lowercase_   )
														lowerCAmelCase_										=     torch.tensor(
														    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]   ).to(lowercase_   )
														lowerCAmelCase_										=     model(
														    input_ids=lowercase_			,			attention_mask=lowercase_			,			encoder_outputs=lowercase_			,			decoder_input_ids=lowercase_   )
														lowerCAmelCase_										=     output[0]
														lowerCAmelCase_										=     torch.Size((1, 1_4, 2_5_0_0_1_2)   )
														self.assertEqual(output_predited_logis.shape			,			lowercase_   )
														lowerCAmelCase_										=     torch.tensor(
														    [[[-6.39_86, -8.23_91, 12.51_89], [-6.32_89, -8.08_64, 12.62_11], [-6.24_18, -8.04_45, 12.79_68]]]   ).to(lowercase_   )
														self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]			,			lowercase_			,			atol=1e-4   )   )

														# encoder outputs
														lowerCAmelCase_										=     model.prophetnet.encoder(lowercase_   )[0]
														lowerCAmelCase_										=     torch.tensor(
														    [[[-1.42_60, -0.76_28, 0.84_53], [-1.47_19, -0.13_91, 0.78_07], [-1.76_78, 0.01_14, 0.46_46]]]   ).to(lowercase_   )
														lowerCAmelCase_										=     torch.Size((1, 4, 1_0_2_4)   )
														self.assertEqual(encoder_outputs.shape			,			lowercase_   )
														self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]			,			lowercase_			,			atol=1e-4   )   )

														# decoder outputs
														lowerCAmelCase_										=     model.prophetnet.decoder(
														    lowercase_			,			encoder_hidden_states=lowercase_			,			)
														lowerCAmelCase_										=     decoder_outputs[1].view(1			,			model.config.ngram			,			1_4			,			-1   )
														lowerCAmelCase_										=     model.lm_head(lowercase_   )
														lowerCAmelCase_										=     predicting_streams_logits[:, 0]
														self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]			,			lowercase_			,			atol=1e-4   )   )

							@slow
							def      _lowercase       (		self   )					->					List[Any]:
														'''simple docstring'''

														lowerCAmelCase_										=     XLMProphetNetForConditionalGeneration.from_pretrained(
														    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )
														model.to(lowercase_   )

														lowerCAmelCase_										=     torch.tensor([[1_7, 9_6_2_0_8, 1_0_3_4_7_1, 2]]   ).to(lowercase_   )
														lowerCAmelCase_										=     torch.tensor(
														    [[2, 2_5_0, 9_9_5_3, 3_4, 6_9_4_8_9, 1_6_2_0, 3_2, 1_1_8_4_2_4, 6_2_4, 2_1_0, 1_0_5, 2_9_1_3, 1_0_3_2, 3_5_1]]   ).to(lowercase_   )
														lowerCAmelCase_										=     model(
														    input_ids=lowercase_			,			attention_mask=lowercase_			,			encoder_outputs=lowercase_			,			decoder_input_ids=lowercase_   )
														lowerCAmelCase_										=     output[0]
														lowerCAmelCase_										=     torch.Size((1, 1_4, 2_5_0_0_1_2)   )
														self.assertEqual(output_predited_logis.shape			,			lowercase_   )
														# compare the actual values for a slice.
														lowerCAmelCase_										=     torch.tensor(
														    [[[-9.22_53, -9.71_73, -6.35_29], [-7.67_01, -9.01_45, -1.93_82], [-8.01_95, -7.00_04, -0.15_23]]]   ).to(lowercase_   )

														self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]			,			lowercase_			,			atol=1e-4   )   )






							@slow
							def      _lowercase       (		self   )					->					Dict:
														'''simple docstring'''

														lowerCAmelCase_										=     XLMProphetNetForConditionalGeneration.from_pretrained(
														    'microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )
														model.to(lowercase_   )
														lowerCAmelCase_										=     5_1_2

														lowerCAmelCase_										=     XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'   )

														lowerCAmelCase_										=     (
														    "Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"
														    " January 14, 2020, according to the official portal of the organization. From that day, users of this"
														    " system will not be able to receive security updates, which could make their computers vulnerable to"
														    " cyber attacks."
														)
														lowerCAmelCase_										=     (
														    "орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"
														    " после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"
														    " этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"
														    " к кибератакам."
														)
														lowerCAmelCase_										=     (
														    "根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"
														)

														lowerCAmelCase_										=     tokenizer(
														    [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]			,			padding=lowercase_			,			max_length=2_5_5			,			return_tensors='pt'   ).input_ids
														lowerCAmelCase_										=     input_ids.to(lowercase_   )

														lowerCAmelCase_										=     model.generate(
														    lowercase_			,			num_beams=1_0			,			length_penalty=1.0			,			no_repeat_ngram_size=3			,			early_stopping=lowercase_   )
														lowerCAmelCase_										=     [tokenizer.decode(lowercase_			,			skip_special_tokens=lowercase_   ) for g in summary_ids]
														lowerCAmelCase_										=     "Microsoft to end Windows 7 free support after January 14, 2020"
														lowerCAmelCase_										=     "Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"
														lowerCAmelCase_										=     "微软打算终止对Windows 7操作系统的免费支持"
														self.assertListEqual(
														    [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]			,			lowercase_			,			)

														lowerCAmelCase_										=     model.generate(
														    lowercase_			,			num_beams=1			,			length_penalty=1.0			,			no_repeat_ngram_size=3			,			early_stopping=lowercase_   )
														lowerCAmelCase_										=     [
														    tokenizer.convert_ids_to_tokens(lowercase_			,			skip_special_tokens=lowercase_   ) for g in summary_ids_beama
														]
														lowerCAmelCase_										=     "▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7".split(' '   )
														lowerCAmelCase_										=     "▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года".split(
														    ' '   )
														lowerCAmelCase_										=     "微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持".split(' '   )
														self.assertListEqual(
														    [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]			,			lowercase_			,			)




import os
import sys
from pathlib import Path

import pytest

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

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def 					snake_case						()       ->       int:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    Dataset.from_dict({"i": range(17						)}						)
						_snake_case						:     Tuple				=    len(__lowercase						)
						_snake_case						:     Any				=    3
						_snake_case						:     List[str]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(__lowercase						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     List[Any]				=    3
						_snake_case						:     Dict				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     List[Any]				=    len(list(__lowercase						)						)
						_snake_case						:     str				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize("shards_per_node"					,			[1, 2, 3]						)
def 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



						def gen(__lowercase						):
												for shard in shards:
																		yield from ({"i": i, "shard": shard} for i in range(17						))

						_snake_case						:     str				=    3
						_snake_case						:     List[str]				=    shards_per_node * world_size
						_snake_case						:     List[Any]				=    {"shards": [F"""shard_{shard_idx}.txt""" for shard_idx in range(__lowercase						)]}
						_snake_case						:     Optional[int]				=    IterableDataset.from_generator(__lowercase					,			gen_kwargs=__lowercase						)
						_snake_case						:     Tuple				=    len(list(__lowercase						)						)
						assert full_ds.n_shards == world_size * shards_per_node
						_snake_case						:     List[Any]				=    [
						    split_dataset_by_node(__lowercase					,			rank=__lowercase					,			world_size=__lowercase						) for rank in range(__lowercase						)
						]
						assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
						assert sum(len(list(__lowercase						)						) for ds in datasets_per_rank						) == full_size
						assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def 					snake_case						()       ->       Dict:

						'''simple docstring'''



						def gen():
												return ({"i": i} for i in range(17						))

						_snake_case						:     Dict				=    2
						_snake_case						:     str				=    IterableDataset.from_generator(__lowercase						)
						_snake_case						:     Dict				=    len(list(__lowercase						)						)

						_snake_case						:     Optional[int]				=    split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle(seed=42						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(__lowercase					,			rank=0					,			world_size=__lowercase						).shuffle()

						_snake_case						:     Optional[int]				=    split_dataset_by_node(full_ds.shuffle(seed=42						)					,			rank=0					,			world_size=__lowercase						)
						assert len(list(__lowercase						)						) == 1 + full_size // world_size
						with pytest.raises(__lowercase						):
												split_dataset_by_node(full_ds.shuffle()					,			rank=0					,			world_size=__lowercase						)
@pytest.mark.parametrize("streaming"					,			[False, True]						)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase						)       ->       Any:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    2
						_snake_case						:     Optional[Any]				=    get_torch_dist_unique_port()
						_snake_case						:     Optional[int]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     Any				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     str				=    F"""
        --streaming={streaming}
    """.split()
						_snake_case						:     Optional[Any]				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)

@pytest.mark.parametrize(
    "nproc_per_node, num_workers"					,			[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]					,			)
@require_torch
@pytest.mark.skipif(os.name == "nt"					,			reason="execute_subprocess_async doesn't support windows"						)
@pytest.mark.integration
def 					snake_case						(__lowercase					,			__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						_snake_case						:     Dict				=    True
						_snake_case						:     Optional[int]				=    get_torch_dist_unique_port()
						_snake_case						:     List[str]				=    Path(__lowercase						).resolve().parent / "distributed_scripts" / "launch_torch_distributed.py"
						_snake_case						:     List[str]				=    F"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
						_snake_case						:     Tuple				=    F"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
						_snake_case						:     Any				=    [sys.executable] + distributed_args + args
						execute_subprocess_async(__lowercase					,			env=os.environ.copy()						)