





"""simple docstring"""






from random import randint, random

def     __A      (			a_						:int   ,       a_						:int   ,       a_						:int   ,       a_						:bool = False   ,       a_						:bool = False   ,       a_						:int = 5   ,       )		->		list:
				__a :						Any			       =  [[-1] * number_of_cells]  # Create a highway without any car
				__a :						Optional[Any]			       =  0
				__a :						Dict			       =  max(a_   ,       0)
				while i < number_of_cells:
								__a :						str			       =  (
								    randint(0   ,       a_) 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      (			a_						:list   ,       a_						:int)		->		int:
				__a :						str			       =  0
				__a :						Union[str, Any]			       =  highway_now[car_index + 1 :]
				for cell in range(len(a_)):  # 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(a_   ,       -1)

def     __A      (			a_						:list   ,       a_						:float   ,       a_						:int)		->		list:
				__a :						List[str]			       =  len(a_)
				# Beforce calculations, the highway is empty
				__a :						Dict			       =  [-1] * number_of_cells

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

def     __A      (			a_						:list   ,       a_						:int   ,       a_						:float   ,       a_						:int)		->		list:
				__a :						List[Any]			       =  len(highway[0])

				for i in range(a_):
								__a :						int			       =  update(highway[i]   ,       a_   ,       a_)
								__a :						int			       =  [-1] * number_of_cells

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

				return highway


if __name__ == "__main__":
		import doctest

		doctest.testmod()






"""simple docstring"""






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


A 			=  ['''bert-base-uncased''', '''bert-base-cased''']
A 			=  '''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():




		class 				__lowercase			(			tf.keras.Model							):







							'''simple docstring'''




							def __init__(						self	,			_UpperCAmelCase     ):
											super().__init__()
											__a :						Any			       =  tokenizer
											__a :						Optional[Any]			       =  AutoConfig.from_pretrained(_UpperCAmelCase     )
											__a :						str			       =  TFAutoModel.from_config(_UpperCAmelCase     )







							def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
											__a :						Any			       =  self.tokenizer(_UpperCAmelCase     )
											__a :						int			       =  self.bert(**_UpperCAmelCase     )
											return out["pooler_output"]




@require_tf
@require_tensorflow_text
class 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									super().setUp()

									__a :						Any			       =  [
									    BertTokenizer.from_pretrained(_UpperCAmelCase     ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
									]  # repeat for when fast_bert_tokenizer=false
									__a :						Union[str, Any]			       =  [TFBertTokenizer.from_pretrained(_UpperCAmelCase     ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
									    TFBertTokenizer.from_pretrained(_UpperCAmelCase	,			use_fast_bert_tokenizer=_UpperCAmelCase     )
									    for checkpoint in TOKENIZER_CHECKPOINTS
									]
									assert len(self.tokenizers     ) == len(self.tf_tokenizers     )

									__a :						Tuple			       =  [
									    '''This is a straightforward English test sentence.''',
									    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
									    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
									    '''And some much more rare Chinese: 齉 堃 齉堃''',
									    '''Je vais aussi écrire en français pour tester les accents''',
									    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
									]
									__a :						Any			       =  list(zip(self.test_sentences	,			self.test_sentences[::-1]     )     )







					def  _lowerCamelCase		(						self     ):
									for tokenizer, tf_tokenizer in zip(self.tokenizers	,			self.tf_tokenizers     ):
													for test_inputs in (self.test_sentences, self.paired_sentences):
																	__a :						List[Any]			       =  tokenizer(_UpperCAmelCase	,			return_tensors='''tf'''	,			padding='''longest'''     )
																	__a :						List[str]			       =  tf_tokenizer(_UpperCAmelCase     )

																	for key in python_outputs.keys():
																					self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape     )     )
																					self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,			tf.intaa     ) == tf_outputs[key]     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for tf_tokenizer in self.tf_tokenizers:
													__a :						Dict			       =  tf_tokenizer(self.paired_sentences     )
													__a :						str			       =  tf_tokenizer(
													    text=[sentence[0] for sentence in self.paired_sentences]	,			text_pair=[sentence[1] for sentence in self.paired_sentences]	,			)
													for key in merged_outputs.keys():
																	self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,			tf.intaa     ) == separated_outputs[key]     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for tf_tokenizer in self.tf_tokenizers:
													__a :						Tuple			       =  tf.function(_UpperCAmelCase     )
													for test_inputs in (self.test_sentences, self.paired_sentences):
																	__a :						List[str]			       =  tf.constant(_UpperCAmelCase     )
																	__a :						Tuple			       =  compiled_tokenizer(_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  tf_tokenizer(_UpperCAmelCase     )

																	for key in eager_outputs.keys():
																					self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]     )     )







					@slow
					def  _lowerCamelCase		(						self     ):
									for tf_tokenizer in self.tf_tokenizers:
													__a :						Dict			       =  ModelToSave(tokenizer=_UpperCAmelCase     )
													__a :						Tuple			       =  tf.convert_to_tensor(self.test_sentences     )
													__a :						List[str]			       =  model(_UpperCAmelCase     )  # Build model with some sample inputs
													with TemporaryDirectory() as tempdir:
																	__a :						Tuple			       =  Path(_UpperCAmelCase     ) / '''saved.model'''
																	model.save(_UpperCAmelCase     )
																	__a :						Tuple			       =  tf.keras.models.load_model(_UpperCAmelCase     )
													__a :						int			       =  loaded_model(_UpperCAmelCase     )
													# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
													self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output     )     )	,			1e-5     )