


'''simple docstring'''



import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad





class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Union[str, Any]								=		10







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Dict								=		[1, 2, 3, 4]
         __lowerCamelCase   :	Any								=		[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_			,     self.block_size			,     0	)			,     SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	List[Any]								=		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         __lowerCamelCase   :	Union[str, Any]								=		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_			,     self.block_size			,     0	)			,     SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	Optional[Any]								=		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
         __lowerCamelCase   :	Optional[Any]								=		[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
         self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_			,     self.block_size			,     0	)			,     SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	int								=		'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
         __lowerCamelCase				,       __lowerCamelCase   :	str								=		process_story(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(SCREAMING_SNAKE_CASE_			,     []	)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	int								=		''
         __lowerCamelCase				,       __lowerCamelCase   :	List[Any]								=		process_story(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(SCREAMING_SNAKE_CASE_			,     []	)
         self.assertEqual(SCREAMING_SNAKE_CASE_			,     []	)







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[Any]								=		(
             'It was the year of Our Lord one thousand seven hundred and '
             'seventy-five\n\nSpiritual revelations were conceded to England '
             'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
         )
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		process_story(SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		[
             'It was the year of Our Lord one thousand seven hundred and seventy-five.',
             'Spiritual revelations were conceded to England at that favoured period, as at this.',
         ]
         self.assertEqual(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Tuple								=		['It was the best of times.']
         self.assertEqual(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Tuple								=		torch.tensor([1, 2, 3, 4]	)
         __lowerCamelCase   :	Union[str, Any]								=		torch.tensor([1, 1, 1, 1]	)
         np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_			,     0	).numpy()			,     expected.numpy()	)







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		torch.tensor([1, 2, 3, 4, 23, 23, 23]	)
         __lowerCamelCase   :	List[str]								=		torch.tensor([1, 1, 1, 1, 0, 0, 0]	)
         np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_			,     23	).numpy()			,     expected.numpy()	)







    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	int								=		torch.tensor([8, 2, 3, 4, 1, 1, 1]	)
         __lowerCamelCase   :	Tuple								=		torch.tensor([1, 1, 1, 1, 0, 0, 0]	)
         np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_			,     1	).numpy()			,     expected.numpy()	)







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	List[str]								=		1_01
         __lowerCamelCase   :	Tuple								=		torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_01, 5, 6], [1, 1_01, 3, 4, 1_01, 6]]	)
         __lowerCamelCase   :	List[Any]								=		torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]	)

         __lowerCamelCase   :	List[Any]								=		compute_token_type_ids(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
         np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

      A__		:					Optional[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






