





"""simple docstring"""






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 					__lowercase      (      unittest.TestCase				):







					'''simple docstring'''




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







					def 			_lowerCamelCase		(    self  ):
									__a			:    int      							=	ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size  )

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

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

									__a			:    Any      							=	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=_UpperCAmelCase						,      initializer_range=self.initializer_range						,      )

									return config, input_ids, token_type_ids, attention_mask







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







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	self.prepare_config_and_inputs()
									__a							,    __a							,    __a							,    __a			:    Union[str, Any]      							=	config_and_inputs

									__a			:    Optional[int]      							=	True
									__a			:    str      							=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
									__a			:    List[Any]      							=	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 					__lowercase      (      _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	True

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







					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	FlaxRobertaModelTester(self  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									for model_class_name in self.all_model_classes:
													__a			:    int      							=	model_class_name.from_pretrained('''roberta-base'''						,      from_pt=_UpperCAmelCase  )
													__a			:    List[str]      							=	model(np.ones((1, 1)  )  )
													self.assertIsNotNone(_UpperCAmelCase  )




"""simple docstring"""






from itertools import product




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				list[int]:
  _snake_case   	=      sides_number
  _snake_case   	=      max_face_number * dice_number
  _snake_case   	=      [0] * (max_total + 1)

  _snake_case   	=      1
  _snake_case   	=      range(lowerCAmelCase_  ,						max_face_number + 1	)
  for dice_numbers in product(lowerCAmelCase_  ,						repeat=lowerCAmelCase_	):
    _snake_case   	=      sum(lowerCAmelCase_	)
    totals_frequencies[total] += 1

  return totals_frequencies




def 			snake_case		(		)						->				float:
  _snake_case   	=      total_frequency_distribution(
      sides_number=4  ,						dice_number=9	)
  _snake_case   	=      total_frequency_distribution(
      sides_number=6  ,						dice_number=6	)

  _snake_case   	=      0
  _snake_case   	=      9
  _snake_case   	=      4 * 9
  _snake_case   	=      6
  for peter_total in range(lowerCAmelCase_  ,						max_peter_total + 1	):
    peter_wins_count += peter_totals_frequencies[peter_total] * sum(
        colin_totals_frequencies[min_colin_total:peter_total]	)

  _snake_case   	=      (4**9) * (6**6)
  _snake_case   	=      peter_wins_count / total_games_number

  _snake_case   	=      round(lowerCAmelCase_  ,						ndigits=7	)

  return rounded_peter_win_probability


if __name__ == "__main__":
     print(F"{solution() = }")





