

import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad





class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    1_0

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    [1, 2, 3, 4]
      _lowerCAmelCase   :					int        =    [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
      self.assertEqual(truncate_or_pad(A_     ,						self.block_size     ,						0       )     ,						A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      _lowerCAmelCase   :					Dict        =    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      self.assertEqual(truncate_or_pad(A_     ,						self.block_size     ,						0       )     ,						A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
      _lowerCAmelCase   :					Any        =    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      self.assertEqual(truncate_or_pad(A_     ,						self.block_size     ,						0       )     ,						A_       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    "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   :					List[Any]        =    process_story(A_       )
      self.assertEqual(A_     ,						[]       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    ""
      _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    process_story(A_       )
      self.assertEqual(A_     ,						[]       )
      self.assertEqual(A_     ,						[]       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _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   :					Dict        =    process_story(A_       )

      _lowerCAmelCase   :					Any        =    [
          "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(A_     ,						A_       )

      _lowerCAmelCase   :					Any        =    ["It was the best of times."]
      self.assertEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    torch.tensor([1, 2, 3, 4]       )
      _lowerCAmelCase   :					Any        =    torch.tensor([1, 1, 1, 1]       )
      np.testing.assert_array_equal(build_mask(A_     ,						0       ).numpy()     ,						expected.numpy()       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]       )
      _lowerCAmelCase   :					Optional[Any]        =    torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
      np.testing.assert_array_equal(build_mask(A_     ,						2_3       ).numpy()     ,						expected.numpy()       )

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    torch.tensor([8, 2, 3, 4, 1, 1, 1]       )
      _lowerCAmelCase   :					str        =    torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
      np.testing.assert_array_equal(build_mask(A_     ,						1       ).numpy()     ,						expected.numpy()       )


 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    1_0_1
      _lowerCAmelCase   :					Any        =    torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]       )
      _lowerCAmelCase   :					int        =    torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]       )

      _lowerCAmelCase   :					Any        =    compute_token_type_ids(A_     ,						A_       )
      np.testing.assert_array_equal(A_     ,						A_       )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






