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   lowercase      :




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Any )   ->			Any:



												'''simple docstring'''

												return None



class   lowercase      :




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   List[Any] )   ->			str:



												'''simple docstring'''

												return None



class   lowercase      (       unittest.TestCase       ):
							lowercase__  :    Union[str, Any]									=							[
							    # (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				:   Optional[Any] )   ->			Optional[Any]:



												'''simple docstring'''

												for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(_UpperCamelCase		,							"tf"		,							12		,							**_UpperCamelCase )




							@require_torch
							@slow
							def 		__snake_case(  self				:   List[str] )   ->			int:



												'''simple docstring'''

												for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(_UpperCamelCase		,							"pt"		,							12		,							**_UpperCamelCase )




							@require_torch
							@slow
							def 		__snake_case(  self				:   Union[str, Any] )   ->			List[str]:



												'''simple docstring'''

												from transformers import BertModel

												SCREAMING_SNAKE_CASE           =    ["[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()
																	SCREAMING_SNAKE_CASE           =    BertTokenizerFast(vocab_file.name )

												with TemporaryDirectory() as bert_save_dir:
																	SCREAMING_SNAKE_CASE           =    BertModel(BertConfig(vocab_size=len(_UpperCamelCase ) ) )
																	model.save_pretrained(_UpperCamelCase )
																	self._test_export(_UpperCamelCase		,							"pt"		,							12		,							_UpperCamelCase )




							@require_tf
							@slow
							def 		__snake_case(  self				:   Any )   ->			Dict:



												'''simple docstring'''

												for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	SCREAMING_SNAKE_CASE           =    self._test_export(_UpperCamelCase		,							"tf"		,							12		,							**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    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				:   Any )   ->			List[Any]:



												'''simple docstring'''

												for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	SCREAMING_SNAKE_CASE           =    self._test_export(_UpperCamelCase		,							"pt"		,							12		,							**_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    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				:   Optional[Any]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Tuple=None		,							**_UpperCamelCase				:   Any )   ->			Union[str, Any]:



												'''simple docstring'''

												try:
																	# Compute path
																	with TemporaryDirectory() as tempdir:
																						SCREAMING_SNAKE_CASE           =    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				:   Tuple )   ->			int:



												'''simple docstring'''

												from transformers import BertModel

												SCREAMING_SNAKE_CASE           =    BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
												SCREAMING_SNAKE_CASE           =    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
												self._test_infer_dynamic_axis(_UpperCamelCase		,							_UpperCamelCase		,							"pt" )




							@require_tf
							@require_tokenizers
							@slow
							def 		__snake_case(  self				:   Any )   ->			Union[str, Any]:



												'''simple docstring'''

												from transformers import TFBertModel

												SCREAMING_SNAKE_CASE           =    TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random" ) )
												SCREAMING_SNAKE_CASE           =    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random" )
												self._test_infer_dynamic_axis(_UpperCamelCase		,							_UpperCamelCase		,							"tf" )




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   Dict )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FeatureExtractionPipeline(_UpperCamelCase		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    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				:   Optional[int] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    ["input_ids", "attention_mask", "token_type_ids"]
												SCREAMING_SNAKE_CASE           =    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    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)
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    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				:   Optional[Any] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    generate_identified_filename(Path("/home/something/my_fake_model.onnx" )		,							"-test" )
												self.assertEqual("/home/something/my_fake_model-test.onnx"		,							generated.as_posix() )





from random import randint, random





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							int = 5 ,	):
					SCREAMING_SNAKE_CASE           =    [[-1] * number_of_cells]  # Create a highway without any car
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    max(UpperCAmelCase__ ,	0					)
					while i < number_of_cells:
										SCREAMING_SNAKE_CASE           =    (
										    randint(0 ,	UpperCAmelCase__					) 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							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    highway_now[car_index + 1 :]
					for cell in range(len(UpperCAmelCase__					)					):  # 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(UpperCAmelCase__ ,	-1					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
					# Beforce calculations, the highway is empty
					SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(highway[0]					)

					for i in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    update(highway[i] ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

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

					return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()




