


import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


__lowerCamelCase   	=  get_logger(__name__)


if TYPE_CHECKING:
       from .builder import DatasetBuilder



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase = None			):
     a_     :  int				      =     importlib.import_module(__UpperCamelCase			)

     # TODO(QL): always update the module to add subsequent new authentication
     if hasattr(__UpperCamelCase	,	"""_patched_for_streaming"""			) and module._patched_for_streaming:
          return

     def wrap_auth(__UpperCamelCase			):
          @wraps(__UpperCamelCase			)
          def wrapper(*__UpperCamelCase	,	**__UpperCamelCase			):
               return function(*__UpperCamelCase	,	use_auth_token=__UpperCamelCase	,	**__UpperCamelCase			)

          a_     :  str				      =     """wrap_auth"""
          return wrapper

     # open files in a streaming fashion
     patch_submodule(__UpperCamelCase	,	"""open"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.listdir"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.walk"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""glob.glob"""	,	wrap_auth(__UpperCamelCase			)			).start()
     # allow to navigate in remote zip files
     patch_submodule(__UpperCamelCase	,	"""os.path.join"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.dirname"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.basename"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.relpath"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.split"""	,	__UpperCamelCase			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.splitext"""	,	__UpperCamelCase			).start()
     # allow checks on paths
     patch_submodule(__UpperCamelCase	,	"""os.path.exists"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.isdir"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.isfile"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""os.path.getsize"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""pathlib.Path"""	,	__UpperCamelCase			).start()
     # file readers
     patch_submodule(__UpperCamelCase	,	"""gzip.open"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""numpy.load"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""pandas.read_csv"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""pandas.read_excel"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""scipy.io.loadmat"""	,	wrap_auth(__UpperCamelCase			)	,	attrs=["""__version__"""]			).start()
     patch_submodule(__UpperCamelCase	,	"""xml.etree.ElementTree.parse"""	,	wrap_auth(__UpperCamelCase			)			).start()
     patch_submodule(__UpperCamelCase	,	"""xml.dom.minidom.parse"""	,	wrap_auth(__UpperCamelCase			)			).start()
     a_     :  Optional[int]				      =     True



def 			_a      (    __UpperCamelCase			):
     extend_module_for_streaming(builder.__module__	,	use_auth_token=builder.use_auth_token			)
     # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
     if not builder.__module__.startswith("""datasets."""			):  # check that it's not a packaged builder like csv
          for imports in get_imports(inspect.getfile(builder.__class__			)			):
               if imports[0] == "internal":
                    a_     :  int				      =     imports[1]
                    a_     :  int				      =     """.""".join(builder.__module__.split("""."""			)[:-1] + [internal_import_name]			)
                    extend_module_for_streaming(__UpperCamelCase	,	use_auth_token=builder.use_auth_token			)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
     from .builder import DatasetBuilder

     a_     :  Optional[int]				      =     [
         cls.__module__
         for cls in type(__UpperCamelCase			).__mro__[1:]  # make sure it's not the same module we've already patched
         if issubclass(cls	,	__UpperCamelCase			) and cls.__module__ != DatasetBuilder.__module__
     ]  # check it's not a standard builder from datasets.builder
     for module in parent_builder_modules:
          extend_module_for_streaming(__UpperCamelCase	,	use_auth_token=builder.use_auth_token			)







'''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					, )




