
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  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  List[str]					=       1_0


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  Optional[Any]					=       [1, 2, 3, 4]
      _A:  Optional[int]					=       [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
      self.assertEqual(truncate_or_pad(lowerCAmelCase_			,			self.block_size			,			0     )			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Dict					=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      _A:  Any					=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      self.assertEqual(truncate_or_pad(lowerCAmelCase_			,			self.block_size			,			0     )			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  Dict					=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
      _A:  Optional[Any]					=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      self.assertEqual(truncate_or_pad(lowerCAmelCase_			,			self.block_size			,			0     )			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  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.'''
      _A			,  _A:  List[str]					=       process_story(lowerCAmelCase_     )
      self.assertEqual(lowerCAmelCase_			,			[]     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  str					=       ''''''
      _A			,  _A:  Tuple					=       process_story(lowerCAmelCase_     )
      self.assertEqual(lowerCAmelCase_			,			[]     )
      self.assertEqual(lowerCAmelCase_			,			[]     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  Optional[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'''
      )
      _A			,  _A:  Tuple					=       process_story(lowerCAmelCase_     )

      _A:  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(lowerCAmelCase_			,			lowerCAmelCase_     )

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


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Optional[int]					=       torch.tensor([1, 2, 3, 4]     )
      _A:  List[str]					=       torch.tensor([1, 1, 1, 1]     )
      np.testing.assert_array_equal(build_mask(lowerCAmelCase_			,			0     ).numpy()			,			expected.numpy()     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  List[Any]					=       torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]     )
      _A:  Dict					=       torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
      np.testing.assert_array_equal(build_mask(lowerCAmelCase_			,			2_3     ).numpy()			,			expected.numpy()     )


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

      """simple docstring"""
      _A:  int					=       torch.tensor([8, 2, 3, 4, 1, 1, 1]     )
      _A:  str					=       torch.tensor([1, 1, 1, 1, 0, 0, 0]     )
      np.testing.assert_array_equal(build_mask(lowerCAmelCase_			,			1     ).numpy()			,			expected.numpy()     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       1_0_1
      _A:  Tuple					=       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]]     )
      _A:  Dict					=       torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]     )

      _A:  Tuple					=       compute_token_type_ids(lowerCAmelCase_			,			lowerCAmelCase_     )
      np.testing.assert_array_equal(lowerCAmelCase_			,			lowerCAmelCase_     )





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''maskformer-swin'''

   __UpperCamelCase		:    Any 				=    {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int=2_2_4			,			lowerCAmelCase_     :							Tuple=4			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							Dict=9_6			,			lowerCAmelCase_     :							Union[str, Any]=[2, 2, 6, 2]			,			lowerCAmelCase_     :							Optional[Any]=[3, 6, 1_2, 2_4]			,			lowerCAmelCase_     :							Optional[Any]=7			,			lowerCAmelCase_     :							Optional[Any]=4.0			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							Optional[Any]=0.0			,			lowerCAmelCase_     :							Union[str, Any]=0.0			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Optional[Any]="gelu"			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Dict=0.02			,			lowerCAmelCase_     :							str=1e-5			,			lowerCAmelCase_     :							Union[str, Any]=None			,			lowerCAmelCase_     :							Any=None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[Any]					=       image_size
      _A:  Optional[int]					=       patch_size
      _A:  Optional[Any]					=       num_channels
      _A:  str					=       embed_dim
      _A:  Any					=       depths
      _A:  str					=       len(lowerCAmelCase_     )
      _A:  Any					=       num_heads
      _A:  int					=       window_size
      _A:  Dict					=       mlp_ratio
      _A:  str					=       qkv_bias
      _A:  List[str]					=       hidden_dropout_prob
      _A:  List[Any]					=       attention_probs_dropout_prob
      _A:  Dict					=       drop_path_rate
      _A:  List[Any]					=       hidden_act
      _A:  Optional[int]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  Union[str, Any]					=       initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _A:  Any					=       int(embed_dim * 2 ** (len(lowerCAmelCase_     ) - 1)     )
      _A:  Tuple					=       ['''stem'''] + [F"""stage{idx}""" for idx in range(1			,			len(lowerCAmelCase_     ) + 1     )]
      _A			,  _A:  str					=       get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase_			,			out_indices=lowerCAmelCase_			,			stage_names=self.stage_names     )



