
"""simple docstring"""


from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class   __a  (      _SCREAMING_SNAKE_CASE	):






							def __init__(  self :							Union[str, Any]       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							List[Any] = None       ,      UpperCAmelCase_ :							Optional[Any] = None       ,      UpperCAmelCase_ :							Dict = True       ,      UpperCAmelCase_ :							Any = None       ,      UpperCAmelCase_ :							Dict = False       ,      UpperCAmelCase_ :							Tuple = None       ,      UpperCAmelCase_ :							List[Any] = True       ,      UpperCAmelCase_ :							Tuple = "arrow"       ,      **UpperCAmelCase_ :							List[str]       ,      )->  int:







														"""simple docstring"""
														super().__init__(
														    split=A_       ,      features=A_       ,      cache_dir=A_       ,      keep_in_memory=A_       ,      streaming=A_       ,      **A_       ,      )
														UpperCamelCase					 =		load_from_cache_file
														UpperCamelCase					 =		file_format
														UpperCamelCase					 =		Spark(
														    df=A_       ,      features=A_       ,      cache_dir=A_       ,      working_dir=A_       ,      **A_       ,      )






							def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Tuple:







														"""simple docstring"""
														if self.streaming:
																					return self.builder.as_streaming_dataset(split=self.split			)
														UpperCamelCase					 =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
														self.builder.download_and_prepare(
														    download_mode=A_       ,      file_format=self._file_format       ,      )
														return self.builder.as_dataset(split=self.split			)







"""simple docstring"""


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 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		XLMRobertaModel.from_pretrained("xlm-roberta-base"			)
              UpperCamelCase					 =		torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
              # The dog is cute and lives in the garden house

              UpperCamelCase					 =		torch.Size((1, 12, 768)			)  # batch_size, sequence_length, embedding_vector_dim
              UpperCamelCase					 =		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():
                     UpperCamelCase					 =		model(UpperCAmelCase_			)["last_hidden_state"].detach()
              self.assertEqual(output.shape       ,      UpperCAmelCase_			)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		XLMRobertaModel.from_pretrained("xlm-roberta-large"			)
              UpperCamelCase					 =		torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
              # The dog is cute and lives in the garden house

              UpperCamelCase					 =		torch.Size((1, 12, 1_024)			)  # batch_size, sequence_length, embedding_vector_dim
              UpperCamelCase					 =		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():
                     UpperCamelCase					 =		model(UpperCAmelCase_			)["last_hidden_state"].detach()
              self.assertEqual(output.shape       ,      UpperCAmelCase_			)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)





