


"""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     :





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      int       )					->List[Any]:



														"""simple docstring"""



														return None





class      __A     :





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      int       )					->Tuple:



														"""simple docstring"""



														return None





class      __A     (unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Optional[int]							      =    [
							    # (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]       )					->str:



														"""simple docstring"""



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



							@require_torch
							@slow
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



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



							@require_torch
							@slow
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->str:



														"""simple docstring"""



														from transformers import BertModel

														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()
																					snake_case_           =		BertTokenizerFast(vocab_file.name       )

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



							@require_tf
							@slow
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																					snake_case_           =		self._test_export(UpperCAmelCase_		,       """tf"""		,       12		,       **UpperCAmelCase_       )
																					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 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



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



														"""simple docstring"""



														try:
																					# Compute path
																					with TemporaryDirectory() as tempdir:
																												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 		lowerCAmelCase       (				self							:      List[str]       )					->Dict:



														"""simple docstring"""



														from transformers import BertModel

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



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



														"""simple docstring"""



														from transformers import TFBertModel

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



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      Union[str, Any]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      int       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		FeatureExtractionPipeline(UpperCAmelCase_		,       UpperCAmelCase_       )

														snake_case_           =		["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
														snake_case_       ,						snake_case_       ,						snake_case_       ,						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 		lowerCAmelCase       (				self							:      List[str]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		["""input_ids""", """attention_mask""", """token_type_ids"""]
														snake_case_           =		{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
														snake_case_       ,						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)
														snake_case_       ,						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 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Optional[Any]:



														"""simple docstring"""



														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()       )







"""simple docstring"""
__SCREAMING_SNAKE_CASE							:    str		=    'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE							:    Dict		=    'TRWAGMYFPDXBNJZSQVHLCKE'







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				bool:

							if not isinstance(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				):
														snake_case_           =		f"""Expected string as input, found {type(_SCREAMING_SNAKE_CASE				).__name__}"""
														raise TypeError(_SCREAMING_SNAKE_CASE				)

							snake_case_           =		spanish_id.replace("""-"""				, """"""				).upper()
							if len(_SCREAMING_SNAKE_CASE				) != 9:
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							try:
														snake_case_           =		int(spanish_id_clean[0:8]				)
														snake_case_           =		spanish_id_clean[8]
							except ValueError as ex:
														raise ValueError(_SCREAMING_SNAKE_CASE				) from ex

							if letter.isdigit():
														raise ValueError(_SCREAMING_SNAKE_CASE				)

							return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



