






"""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       __A		:

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Any     ,		__snake_case  :      Any					)			->							Tuple:
											return None



class       __A		:

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      str					)			->							str:
											return None



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

					@require_tf
					@slow
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Dict:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(__snake_case     ,		"""tf"""     ,		1_2     ,		**__snake_case					)

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      List[str]					)			->							int:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		**__snake_case					)

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[str]:
											from transformers import BertModel

											__magic_name__:	Optional[Any]							=       ["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
											with NamedTemporaryFile(mode="""w+t"""					) as vocab_file:
																	vocab_file.write("""\n""".join(__snake_case					)					)
																	vocab_file.flush()
																	__magic_name__:	str							=       BertTokenizerFast(vocab_file.name					)

											with TemporaryDirectory() as bert_save_dir:
																	__magic_name__:	Optional[int]							=       BertModel(BertConfig(vocab_size=len(__snake_case					)					)					)
																	model.save_pretrained(__snake_case					)
																	self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		__snake_case					)

					@require_tf
					@slow
					def   lowerCamelCase__   (			self  :      Tuple					)			->							List[str]:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	__magic_name__:	int							=       self._test_export(__snake_case     ,		"""tf"""     ,		1_2     ,		**__snake_case					)
																	__magic_name__:	List[str]							=       quantize(Path(__snake_case					)					)

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

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	__magic_name__:	Dict							=       self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		**__snake_case					)
																	__magic_name__:	Tuple							=       quantize(__snake_case					)

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

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      Tuple=None     ,		**__snake_case  :      int					)			->							Dict:
											try:
																	# Compute path
																	with TemporaryDirectory() as tempdir:
																							__magic_name__:	Tuple							=       Path(__snake_case					).joinpath("""model.onnx"""					)

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

																	# Export
																	convert(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					)

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

					@require_torch
					@require_tokenizers
					@slow
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Dict:
											from transformers import BertModel

											__magic_name__:	str							=       BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
											__magic_name__:	int							=       BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
											self._test_infer_dynamic_axis(__snake_case     ,		__snake_case     ,		"""pt"""					)

					@require_tf
					@require_tokenizers
					@slow
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											from transformers import TFBertModel

											__magic_name__:	str							=       TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
											__magic_name__:	List[Any]							=       BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
											self._test_infer_dynamic_axis(__snake_case     ,		__snake_case     ,		"""tf"""					)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      int					)			->							int:
											__magic_name__:	List[Any]							=       FeatureExtractionPipeline(__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       infer_shapes(__snake_case     ,		__snake_case					)

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

											# 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   lowerCamelCase__   (			self  :      int					)			->							List[str]:
											__magic_name__:	int							=       ["""input_ids""", """attention_mask""", """token_type_ids"""]
											__magic_name__:	str							=       {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
											__magic_name__,			__magic_name__:	int							=       ensure_valid_input(FuncContiguousArgs()     ,		__snake_case     ,		__snake_case					)

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

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

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

											# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
											__magic_name__,			__magic_name__:	List[Any]							=       ensure_valid_input(FuncNonContiguousArgs()     ,		__snake_case     ,		__snake_case					)

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

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






					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											__magic_name__:	Optional[int]							=       generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""					)     ,		"""-test"""					)
											self.assertEqual("""/home/something/my_fake_model-test.onnx"""     ,		generated.as_posix()					)











"""simple docstring"""





from typing import List

from .keymap import KEYMAP, get_character



def   a    (     __UpperCAmelCase      :    str  )    ->							Optional[int]:

						def decorator(__UpperCAmelCase      :    List[str]  ):
												__magic_name__:	Union[str, Any]							=       getattr(__UpperCAmelCase      ,   """handle_key"""      ,   []  )
												handle += [key]
												setattr(__UpperCAmelCase      ,   """handle_key"""      ,   __UpperCAmelCase  )
												return func

						return decorator



def   a    (     *__UpperCAmelCase      :    List[str]  )    ->							Union[str, Any]:

						def decorator(__UpperCAmelCase      :    Tuple  ):
												__magic_name__:	Union[str, Any]							=       getattr(__UpperCAmelCase      ,   """handle_key"""      ,   []  )
												handle += keys
												setattr(__UpperCAmelCase      ,   """handle_key"""      ,   __UpperCAmelCase  )
												return func

						return decorator



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __new__(			cls  :      Optional[Any]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any					)			->							List[Any]:
											__magic_name__:	str							=       super().__new__(cls     ,		__snake_case     ,		__snake_case     ,		__snake_case					)
											if not hasattr(__snake_case     ,		"""key_handler"""					):
																	setattr(__snake_case     ,		"""key_handler"""     ,		{}					)
											setattr(__snake_case     ,		"""handle_input"""     ,		KeyHandler.handle_input					)

											for value in attrs.values():
																	__magic_name__:	List[str]							=       getattr(__snake_case     ,		"""handle_key"""     ,		[]					)
																	for key in handled_keys:
																							__magic_name__:	Any							=       value
											return new_cls






					@staticmethod
					def   lowerCamelCase__   (			cls  :      List[Any]					)			->							Optional[Any]:
											__magic_name__:	List[str]							=       get_character()
											if char != KEYMAP["undefined"]:
																	__magic_name__:	Union[str, Any]							=       ord(__snake_case					)
											__magic_name__:	Dict							=       cls.key_handler.get(__snake_case					)
											if handler:
																	__magic_name__:	List[str]							=       char
																	return handler(cls					)
											else:
																	return None



def   a    (     cls      :    List[Any]  )    ->							int:
						return KeyHandler(cls.__name__      ,   cls.__bases__      ,   cls.__dict__.copy()  )



