'''simple docstring'''





import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roformer.modeling_flax_roformer import (
     FlaxRoFormerForMaskedLM,
     FlaxRoFormerForMultipleChoice,
     FlaxRoFormerForQuestionAnswering,
     FlaxRoFormerForSequenceClassification,
     FlaxRoFormerForTokenClassification,
     FlaxRoFormerModel,
 )






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=5    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=512    ,							lowercase__=16    ,							lowercase__=2    ,							lowercase__=0.0_2    ,							lowercase__=4    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       parent
         SCREAMING_SNAKE_CASE					:			Dict    	=       batch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       seq_length
         SCREAMING_SNAKE_CASE					:			str    	=       is_training
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       use_attention_mask
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       use_token_type_ids
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Any    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[str]    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			int    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			str    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			int    	=       type_vocab_size
         SCREAMING_SNAKE_CASE					:			str    	=       type_sequence_label_size
         SCREAMING_SNAKE_CASE					:			str    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       num_choices






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Any    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       None
         if self.use_attention_mask:
              SCREAMING_SNAKE_CASE					:			str    	=       random_attention_mask([self.batch_size, self.seq_length] )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       None
         if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE					:			Any    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.type_vocab_size )

         SCREAMING_SNAKE_CASE					:			str    	=       RoFormerConfig(
             vocab_size=self.vocab_size    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							type_vocab_size=self.type_vocab_size    ,							is_decoder=lowercase__    ,							initializer_range=self.initializer_range    ,							)

         return config, input_ids, token_type_ids, attention_mask






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict






@require_flax
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Dict							=						True

    snake_case__							:   Optional[Any]							=						(
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       FlaxRoFormerModelTester(self )






    @slow
    def       _UpperCamelCase    (       self )							-> Tuple:
         for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			int    	=       model_class_name.from_pretrained('junnyu/roformer_chinese_small'    ,							from_pt=lowercase__ )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(np.ones((1, 1) ) )
              self.assertIsNotNone(lowercase__ )






@require_flax
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @slow
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
         SCREAMING_SNAKE_CASE					:			str    	=       jnp.array([[0, 1, 2, 3, 4, 5]] )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(lowercase__ )[0]

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       50_000

         SCREAMING_SNAKE_CASE					:			List[str]    	=       (1, 6, vocab_size)
         self.assertEqual(output.shape    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       jnp.array(
             [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]] )

         self.assertTrue(jnp.allclose(output[:, :3, :3]    ,							lowercase__    ,							atol=1E-4 ) )




'''simple docstring'''





from __future__ import annotations


def     __lowerCAmelCase						(       a_       ,      a_ = None		)      ->	list[list[str]]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       word_bank or []
     # create a table
     SCREAMING_SNAKE_CASE					:			int    	=       len(a_		) + 1

     SCREAMING_SNAKE_CASE					:			list[list[list[str]]]    	=       []
     for _ in range(a_		):
          table.append([]		)
     # seed value
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [[]]  # because empty string has empty combination

     # iterate through the indices
     for i in range(a_		):
          # condition
          if table[i] != []:
               for word in word_bank:
                    # slice condition
                    if target[i : i + len(a_		)] == word:
                         SCREAMING_SNAKE_CASE					:			list[list[str]]    	=       [
                             [word, *way] for way in table[i]
                         ]
                         # adds the word to every combination the current position holds
                         # now,push that combination to the table[i+len(word)]
                         table[i + len(a_		)] += new_combinations

    # combinations are in reverse order so reverse for better output
     for combination in table[len(a_		)]:
          combination.reverse()

     return table[len(a_		)]


if __name__ == "__main__":
 print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
 print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
 print(
     all_construct(
         """hexagonosaurus""",
         ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
     )
 )



