





from random import randint, random





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a = False	,    __a = False	,    __a = 5	,    )     ->		list:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =[[-1] * number_of_cells]  # Create a highway without any car
  lowerCamelCase__:      Dict			  =0
  lowerCamelCase__:      Any			  =max(__a	,    0		)
  while i < number_of_cells:
    lowerCamelCase__:      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      lowerCAmelCase_ (       __a	,    __a		)     ->		int:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =0
  lowerCamelCase__:      Optional[int]			  =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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		list:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =len(__a		)
  # Beforce calculations, the highway is empty
  lowerCamelCase__:      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
      lowerCamelCase__:      int			  =min(highway_now[car_index] + 1	,    __a		)
      # Number of empty cell before the next car
      lowerCamelCase__:      Union[str, Any]			  =get_distance(__a	,    __a		) - 1
      # We can't have the car causing an accident
      lowerCamelCase__:      Dict			  =min(next_highway[car_index]	,    __a		)
      if random() < probability:
        # Randomly, a driver will slow down
        lowerCamelCase__:      int			  =max(next_highway[car_index] - 1	,    0		)
  return next_highway





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a		)     ->		list:







  """simple docstring"""





  lowerCamelCase__:      Dict			  =len(highway[0]		)

  for i in range(__a		):
    lowerCamelCase__:      Dict			  =update(highway[i]	,    __a	,    __a		)
    lowerCamelCase__:      List[str]			  =[-1] * number_of_cells

    for car_index in range(__a		):
      lowerCamelCase__:      Any			  =next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        lowerCamelCase__:      Dict			  =(car_index + speed) % number_of_cells
        # Commit the change of position
        lowerCamelCase__:      Optional[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 NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													return None






class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													return None






class 			UpperCamelCase__(	unittest.TestCase       ):
						lowerCAmelCase__     :  Tuple  							=						[
						    # (model_name, model_kwargs)
						    ('bert-base-cased', {}),
						    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
						]





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
													with NamedTemporaryFile(mode='w+t'   ) as vocab_file:
																				vocab_file.write('\n'.join(__UpperCAmelCase   )   )
																				vocab_file.flush()
																				A__		   =							BertTokenizerFast(vocab_file.name   )

													with TemporaryDirectory() as bert_save_dir:
																				A__		   =							BertModel(BertConfig(vocab_size=len(__UpperCAmelCase   )   )   )
																				model.save_pretrained(__UpperCAmelCase   )
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,__UpperCAmelCase   )





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(Path(__UpperCAmelCase   )   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> str:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(__UpperCAmelCase   )

																				# Ensure the actual quantized model is not bigger than the original one
																				if quantized_path.stat().st_size >= Path(__UpperCAmelCase   ).stat().st_size:
																											self.fail('Quantized model is bigger than initial ONNX model'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Union[str, Any]:
													try:
																				# Compute path
																				with TemporaryDirectory() as tempdir:
																											A__		   =							Path(__UpperCAmelCase   ).joinpath('model.onnx'   )

																				# Remove folder if exists
																				if path.parent.exists():
																											path.parent.rmdir()

																				# Export
																				convert(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,**__UpperCAmelCase   )

																				return path
													except Exception as e:
																				self.fail(__UpperCAmelCase   )





						@require_torch
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'pt'   )





						@require_tf
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import TFBertModel

													A__		   =							TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'tf'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													A__		   =							FeatureExtractionPipeline(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
													A__   ,     A__   ,     A__   ,     A__		   =							infer_shapes(__UpperCAmelCase						,__UpperCAmelCase   )

													# Assert all variables are present
													self.assertEqual(len(__UpperCAmelCase   )						,len(__UpperCAmelCase   )   )
													self.assertTrue(all(var_name in shapes for var_name in variable_names   )   )
													self.assertSequenceEqual(variable_names[:3]						,__UpperCAmelCase   )
													self.assertSequenceEqual(variable_names[3:]						,__UpperCAmelCase   )

													# Assert inputs are {0: batch, 1: sequence}
													for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
																				self.assertDictEqual(shapes[var_name]						,{0: 'batch', 1: 'sequence'}   )

													# Assert outputs are {0: batch, 1: sequence} and {0: batch}
													self.assertDictEqual(shapes['output_0']						,{0: 'batch', 1: 'sequence'}   )
													self.assertDictEqual(shapes['output_1']						,{0: 'batch'}   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							['input_ids', 'attention_mask', 'token_type_ids']
													A__		   =							{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
													A__   ,     A__		   =							ensure_valid_input(FuncContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the same number of args (all are valid)
													self.assertEqual(len(__UpperCAmelCase   )						,3   )

													# Should have exactly the same input names
													self.assertEqual(set(__UpperCAmelCase   )						,set(__UpperCAmelCase   )   )

													# Parameter should be reordered according to their respective place in the function:
													# (input_ids, token_type_ids, attention_mask)
													self.assertEqual(__UpperCAmelCase						,(tokens['input_ids'], tokens['token_type_ids'], tokens['attention_mask'])   )

													# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
													A__   ,     A__		   =							ensure_valid_input(FuncNonContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

													# Should have exactly the one arg (all before the one not provided "some_other_args")
													self.assertEqual(len(__UpperCAmelCase   )						,1   )
													self.assertEqual(len(__UpperCAmelCase   )						,1   )

													# Should have only "input_ids"
													self.assertEqual(inputs_args[0]						,tokens['input_ids']   )
													self.assertEqual(ordered_input_names[0]						,'input_ids'   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							generate_identified_filename(Path('/home/something/my_fake_model.onnx'   )						,'-test'   )
													self.assertEqual('/home/something/my_fake_model-test.onnx'						,generated.as_posix()   )





