
import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					from transformers.models.roberta.modeling_flax_roberta import (
					    FlaxRobertaForCausalLM,
					    FlaxRobertaForMaskedLM,
					    FlaxRobertaForMultipleChoice,
					    FlaxRobertaForQuestionAnswering,
					    FlaxRobertaForSequenceClassification,
					    FlaxRobertaForTokenClassification,
					    FlaxRobertaModel,
					)







class 			__A( unittest.TestCase			):




				def __init__(     self		,     _snake_case		,     _snake_case=13		,     _snake_case=7		,     _snake_case=True		,     _snake_case=True		,     _snake_case=True		,     _snake_case=True		,     _snake_case=99		,     _snake_case=32		,     _snake_case=5		,     _snake_case=4		,     _snake_case=37		,     _snake_case="gelu"		,     _snake_case=0.1		,     _snake_case=0.1		,     _snake_case=512		,     _snake_case=16		,     _snake_case=2		,     _snake_case=0.02		,     _snake_case=4		,     )    ->      int:


								'''simple docstring'''




								__a										=    parent
								__a										=    batch_size
								__a										=    seq_length
								__a										=    is_training
								__a										=    use_attention_mask
								__a										=    use_token_type_ids
								__a										=    use_labels
								__a										=    vocab_size
								__a										=    hidden_size
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    intermediate_size
								__a										=    hidden_act
								__a										=    hidden_dropout_prob
								__a										=    attention_probs_dropout_prob
								__a										=    max_position_embeddings
								__a										=    type_vocab_size
								__a										=    type_sequence_label_size
								__a										=    initializer_range
								__a										=    num_choices




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size				)

								__a										=    None
								if self.use_attention_mask:
												__a										=    random_attention_mask([self.batch_size, self.seq_length]				)

								__a										=    None
								if self.use_token_type_ids:
												__a										=    ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size				)

								__a										=    RobertaConfig(
								    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=__SCREAMING_SNAKE_CASE		,     initializer_range=self.initializer_range		,     )

								return config, input_ids, token_type_ids, attention_mask




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    self.prepare_config_and_inputs()
								__a							,     __a							,     __a							,     __a										=    config_and_inputs
								__a										=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




								__a										=    self.prepare_config_and_inputs()
								__a							,     __a							,     __a							,     __a										=    config_and_inputs

								__a										=    True
								__a										=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
								__a										=    ids_tensor([self.batch_size, self.seq_length]		,     vocab_size=2				)

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)







@require_flax
class 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    True

				snake_case_						   =    (
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    FlaxRobertaModelTester(self				)




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												__a										=    model_class_name.from_pretrained('''roberta-base'''		,     from_pt=__SCREAMING_SNAKE_CASE				)
												__a										=    model(np.ones((1, 1)				)				)
												self.assertIsNotNone(__SCREAMING_SNAKE_CASE				)


'''simple docstring'''



def    a__       (  a__      ,    a__						):




     """simple docstring"""


     _enforce_args(a__      ,    a__						)
     if n == 0:
          return 0
     __SCREAMING_SNAKE_CASE     =      float("""-inf"""						)
     for i in range(1      ,    n + 1						):
          __SCREAMING_SNAKE_CASE     =      max(
              a__      ,    prices[i - 1] + naive_cut_rod_recursive(n - i      ,    a__						)						)

     return max_revue







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     _enforce_args(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      [float("""-inf"""						) for _ in range(n + 1						)]
     return _top_down_cut_rod_recursive(a__      ,    a__      ,    a__						)







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     if max_rev[n] >= 0:
          return max_rev[n]
     elif n == 0:
          return 0
     else:
          __SCREAMING_SNAKE_CASE     =      float("""-inf"""						)
          for i in range(1      ,    n + 1						):
               __SCREAMING_SNAKE_CASE     =      max(
                   a__      ,    prices[i - 1] + _top_down_cut_rod_recursive(n - i      ,    a__      ,    a__						)      ,    )

          __SCREAMING_SNAKE_CASE     =      max_revenue

     return max_rev[n]







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     _enforce_args(a__      ,    a__						)

     # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
     # length 0.
     __SCREAMING_SNAKE_CASE     =      [float("""-inf"""						) for _ in range(n + 1						)]
     __SCREAMING_SNAKE_CASE     =      0

     for i in range(1      ,    n + 1						):
          __SCREAMING_SNAKE_CASE     =      max_rev[i]
          for j in range(1      ,    i + 1						):
               __SCREAMING_SNAKE_CASE     =      max(a__      ,    prices[j - 1] + max_rev[i - j]						)

          __SCREAMING_SNAKE_CASE     =      max_revenue_i

     return max_rev[n]







def    a__       (  a__      ,    a__						):




     """simple docstring"""


     if n < 0:
          __SCREAMING_SNAKE_CASE     =      F'n must be greater than or equal to 0. Got n = {n}'
          raise ValueError(a__						)

     if n > len(a__						):
          __SCREAMING_SNAKE_CASE     =      (
              """Each integral piece of rod must have a corresponding price. """
              F'Got n = {n} but length of prices = {len(a__						)}'
          )
          raise ValueError(a__						)







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      [6, 10, 12, 15, 20, 23]
     __SCREAMING_SNAKE_CASE     =      len(a__						)

     # the best revenue comes from cutting the rod into 6 pieces, each
     # of length 1 resulting in a revenue of 6 * 6 = 36.
     __SCREAMING_SNAKE_CASE     =      36

     __SCREAMING_SNAKE_CASE     =      top_down_cut_rod(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      bottom_up_cut_rod(a__      ,    a__						)
     __SCREAMING_SNAKE_CASE     =      naive_cut_rod_recursive(a__      ,    a__						)

     assert expected_max_revenue == max_rev_top_down
     assert max_rev_top_down == max_rev_bottom_up
     assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
  main()






