


'''simple docstring'''
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 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	Union[str, Any]          =					Dataset.from_dict({"""i""": range(17     )}     )
       snake_case__					:	Union[str, Any]          =					len(UpperCAmelCase     )
       snake_case__					:	str          =					3
       snake_case__					:	Optional[Any]          =					[
           split_dataset_by_node(UpperCAmelCase    ,       rank=UpperCAmelCase    ,       world_size=UpperCAmelCase     ) for rank in range(UpperCAmelCase     )
       ]
       assert sum(len(UpperCAmelCase     ) 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 				lowerCAmelCase__	(       ):






       """simple docstring"""





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

       snake_case__					:	int          =					3
       snake_case__					:	Optional[int]          =					IterableDataset.from_generator(UpperCAmelCase     )
       snake_case__					:	Any          =					len(list(UpperCAmelCase     )     )
       snake_case__					:	List[str]          =					[
           split_dataset_by_node(UpperCAmelCase    ,       rank=UpperCAmelCase    ,       world_size=UpperCAmelCase     ) for rank in range(UpperCAmelCase     )
       ]
       assert sum(len(list(UpperCAmelCase     )     ) 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 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





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

       snake_case__					:	List[Any]          =					3
       snake_case__					:	List[Any]          =					shards_per_node * world_size
       snake_case__					:	List[str]          =					{"""shards""": [f"""shard_{shard_idx}.txt""" for shard_idx in range(UpperCAmelCase     )]}
       snake_case__					:	Dict          =					IterableDataset.from_generator(UpperCAmelCase    ,       gen_kwargs=UpperCAmelCase     )
       snake_case__					:	Tuple          =					len(list(UpperCAmelCase     )     )
       assert full_ds.n_shards == world_size * shards_per_node
       snake_case__					:	List[Any]          =					[
           split_dataset_by_node(UpperCAmelCase    ,       rank=UpperCAmelCase    ,       world_size=UpperCAmelCase     ) for rank in range(UpperCAmelCase     )
       ]
       assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
       assert sum(len(list(UpperCAmelCase     )     ) 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 				lowerCAmelCase__	(       ):






       """simple docstring"""





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

       snake_case__					:	str          =					2
       snake_case__					:	Any          =					IterableDataset.from_generator(UpperCAmelCase     )
       snake_case__					:	str          =					len(list(UpperCAmelCase     )     )

       snake_case__					:	List[str]          =					split_dataset_by_node(UpperCAmelCase    ,       rank=0    ,       world_size=UpperCAmelCase     ).shuffle(seed=42     )
       assert len(list(UpperCAmelCase     )     ) == 1 + full_size // world_size
       with pytest.raises(UpperCAmelCase     ):
              split_dataset_by_node(UpperCAmelCase    ,       rank=0    ,       world_size=UpperCAmelCase     ).shuffle()

       snake_case__					:	List[Any]          =					split_dataset_by_node(full_ds.shuffle(seed=42     )    ,       rank=0    ,       world_size=UpperCAmelCase     )
       assert len(list(UpperCAmelCase     )     ) == 1 + full_size // world_size
       with pytest.raises(UpperCAmelCase     ):
              split_dataset_by_node(full_ds.shuffle()    ,       rank=0    ,       world_size=UpperCAmelCase     )



@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 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	List[Any]          =					2
       snake_case__					:	List[Any]          =					get_torch_dist_unique_port()
       snake_case__					:	Any          =					Path(UpperCAmelCase     ).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
       snake_case__					:	Optional[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__					:	Tuple          =					[sys.executable] + distributed_args + args
       execute_subprocess_async(UpperCAmelCase    ,       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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





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








'''simple docstring'''
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   __lowerCAmelCase						(	self	:					Tuple						)->	Optional[int]:
          snake_case__					:	Any          =					XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""						)
          model.to(lowerCamelCase						)

          # encoder-decoder outputs
          snake_case__					:	Optional[int]          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[Any]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          snake_case__					:	Optional[int]          =					torch.tensor(
              [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]						).to(lowerCamelCase						)
          self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # encoder outputs
          snake_case__					:	Dict          =					model.prophetnet.encoder(lowerCamelCase						)[0]
          snake_case__					:	Optional[Any]          =					torch.tensor(
              [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]						).to(lowerCamelCase						)
          snake_case__					:	str          =					torch.Size((1, 4, 1_024)						)
          self.assertEqual(encoder_outputs.shape					, lowerCamelCase						)
          self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)

          # decoder outputs
          snake_case__					:	int          =					model.prophetnet.decoder(
              lowerCamelCase					, encoder_hidden_states=lowerCamelCase					, )
          snake_case__					:	Any          =					decoder_outputs[1].view(1					, model.config.ngram					, 14					, -1						)
          snake_case__					:	Optional[Any]          =					model.lm_head(lowerCamelCase						)
          snake_case__					:	str          =					predicting_streams_logits[:, 0]
          self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]					, lowerCamelCase					, atol=1e-4						)						)




   @slow
   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	Optional[Any]:
          snake_case__					:	Tuple          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)

          snake_case__					:	str          =					torch.tensor([[17, 96_208, 103_471, 2]]						).to(lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					torch.tensor(
              [[2, 250, 9_953, 34, 69_489, 1_620, 32, 118_424, 624, 210, 105, 2_913, 1_032, 351]]						).to(lowerCamelCase						)
          snake_case__					:	List[Any]          =					model(
              input_ids=lowerCamelCase					, attention_mask=lowerCamelCase					, encoder_outputs=lowerCamelCase					, decoder_input_ids=lowerCamelCase						)
          snake_case__					:	List[str]          =					output[0]
          snake_case__					:	Any          =					torch.Size((1, 14, 250_012)						)
          self.assertEqual(output_predited_logis.shape					, lowerCamelCase						)
          # compare the actual values for a slice.
          snake_case__					:	List[str]          =					torch.tensor(
              [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]						).to(lowerCamelCase						)

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




   @slow
   def   __lowerCAmelCase						(	self	:					str						)->	str:
          snake_case__					:	Dict          =					XLMProphetNetForConditionalGeneration.from_pretrained(
              """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)
          model.to(lowerCamelCase						)
          snake_case__					:	Optional[int]          =					512

          snake_case__					:	Any          =					XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""						)

          snake_case__					:	Optional[int]          =					(
              """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."""
          )
          snake_case__					:	Union[str, Any]          =					(
              """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."""
          )
          snake_case__					:	int          =					(
              """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。"""
          )

          snake_case__					:	int          =					tokenizer(
              [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]					, padding=lowerCamelCase					, max_length=255					, return_tensors="""pt"""						).input_ids
          snake_case__					:	Optional[int]          =					input_ids.to(lowerCamelCase						)

          snake_case__					:	Optional[Any]          =					model.generate(
              lowerCamelCase					, num_beams=10					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Union[str, Any]          =					[tokenizer.decode(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids]
          snake_case__					:	Optional[Any]          =					"""Microsoft to end Windows 7 free support after January 14, 2020"""
          snake_case__					:	Dict          =					"""Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
          snake_case__					:	Optional[int]          =					"""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]					, lowerCamelCase					, )

          snake_case__					:	Optional[int]          =					model.generate(
              lowerCamelCase					, num_beams=1					, length_penalty=1.0					, no_repeat_ngram_size=3					, early_stopping=lowerCamelCase						)
          snake_case__					:	Tuple          =					[
              tokenizer.convert_ids_to_tokens(lowerCamelCase					, skip_special_tokens=lowerCamelCase						) for g in summary_ids_beama
          ]
          snake_case__					:	Any          =					"""▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """						)
          snake_case__					:	Union[str, Any]          =					"""▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
              """ """						)
          snake_case__					:	Tuple          =					"""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]					, lowerCamelCase					, )




