

'''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 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		Any		,					UpperCAmelCase__     :		Tuple		,					UpperCAmelCase__     :		Optional[Any]=13		,					UpperCAmelCase__     :		Union[str, Any]=7		,					UpperCAmelCase__     :		Optional[int]=True		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Dict=True		,					UpperCAmelCase__     :		Union[str, Any]=True		,					UpperCAmelCase__     :		Tuple=99		,					UpperCAmelCase__     :		Tuple=32		,					UpperCAmelCase__     :		List[Any]=5		,					UpperCAmelCase__     :		List[Any]=4		,					UpperCAmelCase__     :		Optional[int]=37		,					UpperCAmelCase__     :		Optional[Any]="gelu"		,					UpperCAmelCase__     :		Dict=0.1		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		Optional[int]=512		,					UpperCAmelCase__     :		Optional[int]=16		,					UpperCAmelCase__     :		int=2		,					UpperCAmelCase__     :		Union[str, Any]=0.02		,					UpperCAmelCase__     :		List[Any]=4		,					):
										'''simple docstring'''
										lowercase :							str    		=parent
										lowercase :							Tuple    		=batch_size
										lowercase :							List[Any]    		=seq_length
										lowercase :							str    		=is_training
										lowercase :							Optional[Any]    		=use_attention_mask
										lowercase :							Union[str, Any]    		=use_token_type_ids
										lowercase :							int    		=use_labels
										lowercase :							Optional[int]    		=vocab_size
										lowercase :							Any    		=hidden_size
										lowercase :							Dict    		=num_hidden_layers
										lowercase :							Optional[Any]    		=num_attention_heads
										lowercase :							List[str]    		=intermediate_size
										lowercase :							Dict    		=hidden_act
										lowercase :							List[Any]    		=hidden_dropout_prob
										lowercase :							Union[str, Any]    		=attention_probs_dropout_prob
										lowercase :							Any    		=max_position_embeddings
										lowercase :							str    		=type_vocab_size
										lowercase :							List[str]    		=type_sequence_label_size
										lowercase :							Optional[int]    		=initializer_range
										lowercase :							Any    		=num_choices







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size   )

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

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

										lowercase :							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     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=self.prepare_config_and_inputs()
										lowercase		,	lowercase		,	lowercase		,	lowercase :							List[Any]    		=config_and_inputs
										lowercase :							List[Any]    		={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							str    		=self.prepare_config_and_inputs()
										lowercase		,	lowercase		,	lowercase		,	lowercase :							Dict    		=config_and_inputs

										lowercase :							Any    		=True
										lowercase :							List[Any]    		=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
										lowercase :							Union[str, 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 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    True

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







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		=FlaxRobertaModelTester(self   )







				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										for model_class_name in self.all_model_classes:
																lowercase :							str    		=model_class_name.from_pretrained('''roberta-base'''		,					from_pt=UpperCAmelCase__   )
																lowercase :							Optional[int]    		=model(np.ones((1, 1)   )   )
																self.assertIsNotNone(UpperCAmelCase__   )









from random import randint, random




def    a__    (		snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							int						,   snake_case__					:							bool = False						,   snake_case__					:							bool = False						,   snake_case__					:							int = 5						,   ):
  _UpperCAmelCase		:							Optional[int]							=     [[-1] * number_of_cells]  # Create a highway without any car
  _UpperCAmelCase		:							Optional[Any]							=     0
  _UpperCAmelCase		:							Dict							=     max(snake_case__						,   0     )
  while i < number_of_cells:
    _UpperCAmelCase		:							int							=     (
        randint(0						,   snake_case__     ) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1						,   max_speed * 2     ) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							List[Any]							=     0
  _UpperCAmelCase		:							int							=     highway_now[car_index + 1 :]
  for cell in range(len(snake_case__     )     ):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(snake_case__						,   -1     )




def    a__    (		snake_case__					:							list						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Optional[Any]							=     len(snake_case__     )
  # Beforce calculations, the highway is empty
  _UpperCAmelCase		:							Dict							=     [-1] * number_of_cells

  for car_index in range(snake_case__     ):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _UpperCAmelCase		:							Dict							=     min(highway_now[car_index] + 1						,   snake_case__     )
      # Number of empty cell before the next car
      _UpperCAmelCase		:							List[str]							=     get_distance(snake_case__						,   snake_case__     ) - 1
      # We can't have the car causing an accident
      _UpperCAmelCase		:							List[str]							=     min(next_highway[car_index]						,   snake_case__     )
      if random() < probability:
        # Randomly, a driver will slow down
        _UpperCAmelCase		:							Dict							=     max(next_highway[car_index] - 1						,   0     )
  return next_highway




def    a__    (		snake_case__					:							list						,   snake_case__					:							int						,   snake_case__					:							float						,   snake_case__					:							int     ):
  _UpperCAmelCase		:							Union[str, Any]							=     len(highway[0]     )

  for i in range(snake_case__     ):
    _UpperCAmelCase		:							Tuple							=     update(highway[i]						,   snake_case__						,   snake_case__     )
    _UpperCAmelCase		:							int							=     [-1] * number_of_cells

    for car_index in range(snake_case__     ):
      _UpperCAmelCase		:							List[Any]							=     next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _UpperCAmelCase		:							Optional[Any]							=     (car_index + speed) % number_of_cells
        # Commit the change of position
        _UpperCAmelCase		:							Optional[Any]							=     speed
    highway.append(snake_case__     )

  return highway


if __name__ == "__main__":
 import doctest

 doctest.testmod()





