


"""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_     :

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Any      ,      UpperCAmelCase__      :    Tuple       )	->     str:
						return None




class 			UpperCamelCase_     :

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Optional[int]      ,      UpperCAmelCase__      :    Dict      ,      UpperCAmelCase__      :    Optional[int]       )	->     Optional[int]:
						return None




class 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  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   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Dict:
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(UpperCAmelCase__      ,      "tf"      ,      1_2      ,      **UpperCAmelCase__       )




	@require_torch
	@slow
	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     str:
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(UpperCAmelCase__      ,      "pt"      ,      1_2      ,      **UpperCAmelCase__       )




	@require_torch
	@slow
	def   UpperCAmelCase_				(			self      :    List[str]       )	->     int:
						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"      ,      1_2      ,      UpperCAmelCase__       )




	@require_tf
	@slow
	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Tuple:
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											__SCREAMING_SNAKE_CASE						       =    self._test_export(UpperCAmelCase__      ,      "tf"      ,      1_2      ,      **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   UpperCAmelCase_				(			self      :    int       )	->     Tuple:
						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											__SCREAMING_SNAKE_CASE						       =    self._test_export(UpperCAmelCase__      ,      "pt"      ,      1_2      ,      **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   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    Tuple      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    str=None      ,      **UpperCAmelCase__      :    str       )	->     Tuple:
						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   UpperCAmelCase_				(			self      :    int       )	->     str:
						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   UpperCAmelCase_				(			self      :    int       )	->     Optional[Any]:
						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   UpperCAmelCase_				(			self      :    Dict      ,      UpperCAmelCase__      :    List[Any]      ,      UpperCAmelCase__      :    int      ,      UpperCAmelCase__      :    str       )	->     Optional[Any]:
						__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   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Tuple:
						__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   UpperCAmelCase_				(			self      :    Tuple       )	->     Union[str, Any]:
						__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()       )




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if digit_amount > 0:
										return round(number - int(lowerCAmelCase_       )  ,						lowerCAmelCase_       )
					return number - int(lowerCAmelCase_       )


if __name__ == "__main__":
		print(decimal_isolate(1.53, 0))
		print(decimal_isolate(35.3_45, 1))
		print(decimal_isolate(35.3_45, 2))
		print(decimal_isolate(35.3_45, 3))
		print(decimal_isolate(-14.7_89, 3))
		print(decimal_isolate(0, 2))
		print(decimal_isolate(-14.1_23, 1))
		print(decimal_isolate(-14.1_23, 2))
		print(decimal_isolate(-14.1_23, 3))
