

'''simple docstring'''
import math

import flax.linen as nn
import jax.numpy as jnp


def        _lowerCAmelCase					(						_UpperCamelCase						:     jnp.ndarray							, _UpperCamelCase						:     int							, _UpperCamelCase						:     float = 1							, _UpperCamelCase						:     float = 1							, _UpperCamelCase						:     float = 1.0E4							, _UpperCamelCase						:     bool = False							, _UpperCamelCase						:     float = 1.0							, )      ->			jnp.ndarray:
     """simple docstring"""




     assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
     assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
     _SCREAMING_SNAKE_CASE							    =float(embedding_dim // 2				)
     _SCREAMING_SNAKE_CASE							    =math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
     _SCREAMING_SNAKE_CASE							    =min_timescale * jnp.exp(jnp.arange(_UpperCamelCase							, dtype=jnp.floataa				) * -log_timescale_increment				)
     _SCREAMING_SNAKE_CASE							    =jnp.expand_dims(_UpperCamelCase							, 1				) * jnp.expand_dims(_UpperCamelCase							, 0				)

     # scale embeddings
     _SCREAMING_SNAKE_CASE							    =scale * emb

     if flip_sin_to_cos:
          _SCREAMING_SNAKE_CASE							    =jnp.concatenate([jnp.cos(_UpperCamelCase				), jnp.sin(_UpperCamelCase				)]							, axis=1				)
     else:
          _SCREAMING_SNAKE_CASE							    =jnp.concatenate([jnp.sin(_UpperCamelCase				), jnp.cos(_UpperCamelCase				)]							, axis=1				)
     _SCREAMING_SNAKE_CASE							    =jnp.reshape(_UpperCamelCase							, [jnp.shape(_UpperCamelCase				)[0], embedding_dim]				)
     return signal







class 							A__    (       nn.Module  ):
  A__		 =				32
  A__		 =				jnp.floataa





  @nn.compact
  def __call__(		self      :	int							,					_a      :	Union[str, Any] )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =nn.Dense(self.time_embed_dim							,					dtype=self.dtype							,					name='linear_1' )(_a )
       _SCREAMING_SNAKE_CASE							    =nn.silu(_a )
       _SCREAMING_SNAKE_CASE							    =nn.Dense(self.time_embed_dim							,					dtype=self.dtype							,					name='linear_2' )(_a )
       return temb







class 							A__    (       nn.Module  ):
  A__		 =				32
  A__		 =				False
  A__		 =				1





  @nn.compact
  def __call__(		self      :	Tuple							,					_a      :	Dict )	->    List[Any]:





       '''simple docstring'''






       return get_sinusoidal_embeddings(
           _a							,					embedding_dim=self.dim							,					flip_sin_to_cos=self.flip_sin_to_cos							,					freq_shift=self.freq_shift )







"""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 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							1_0






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[int]											=							[1, 2, 3, 4]
         lowerCAmelCase__			:Tuple											=							[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         lowerCAmelCase__			:List[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
         lowerCAmelCase__			:List[Any]											=							[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
         self.assertEqual(truncate_or_pad(__UpperCAmelCase    , self.block_size    , 0      )    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							'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(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , []      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							''
         lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							process_story(__UpperCAmelCase      )
         self.assertEqual(__UpperCAmelCase    , []      )
         self.assertEqual(__UpperCAmelCase    , []      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							(
             '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__			:str											=							process_story(__UpperCAmelCase      )

         lowerCAmelCase__			:List[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(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:List[str]											=							['It was the best of times.']
         self.assertEqual(__UpperCAmelCase    , __UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							torch.tensor([1, 2, 3, 4]      )
         lowerCAmelCase__			:List[str]											=							torch.tensor([1, 1, 1, 1]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 0      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]      )
         lowerCAmelCase__			:Optional[int]											=							torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 2_3      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							torch.tensor([8, 2, 3, 4, 1, 1, 1]      )
         lowerCAmelCase__			:Optional[Any]											=							torch.tensor([1, 1, 1, 1, 0, 0, 0]      )
         np.testing.assert_array_equal(build_mask(__UpperCAmelCase    , 1      ).numpy()    , expected.numpy()      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							1_0_1
         lowerCAmelCase__			:str											=							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__			: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(__UpperCAmelCase    , __UpperCAmelCase      )
         np.testing.assert_array_equal(__UpperCAmelCase    , __UpperCAmelCase      )
