

'''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     lowercase_			:




		"""simple docstring"""




		def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[int]    ):
			return None






class     lowercase_			:




		"""simple docstring"""




		def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Tuple      ,lowercase__     :  str      ,lowercase__     :  str      ,lowercase__     :  str    ):
			return None






class     lowercase_			(unittest.TestCase   ):




		"""simple docstring"""




		SCREAMING_SNAKE_CASE					:						Optional[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       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
			for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
				self._test_export(__a      ,'''tf'''      ,1_2      ,**__a    )






		@require_torch
		@slow
		def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
			for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
				self._test_export(__a      ,'''pt'''      ,1_2      ,**__a    )






		@require_torch
		@slow
		def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
			from transformers import BertModel

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

			with TemporaryDirectory() as bert_save_dir:
				__lowercase													=				BertModel(BertConfig(vocab_size=len(__a    )    )    )
				model.save_pretrained(__a    )
				self._test_export(__a      ,'''pt'''      ,1_2      ,__a    )






		@require_tf
		@slow
		def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
			for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
				__lowercase													=				self._test_export(__a      ,'''tf'''      ,1_2      ,**__a    )
				__lowercase													=				quantize(Path(__a    )    )

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






		@require_torch
		@slow
		def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
			for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
				__lowercase													=				self._test_export(__a      ,'''pt'''      ,1_2      ,**__a    )
				__lowercase													=				quantize(__a    )

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






		def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Optional[Any]=None      ,**lowercase__     :  Optional[int]    ):
			try:
				# Compute path
				with TemporaryDirectory() as tempdir:
					__lowercase													=				Path(__a    ).joinpath('''model.onnx'''    )

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

				# Export
				convert(__a      ,__a      ,__a      ,__a      ,__a      ,**__a    )

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






		@require_torch
		@require_tokenizers
		@slow
		def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
			from transformers import BertModel

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






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

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






		def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Optional[Any]      ,lowercase__     :  str    ):
			__lowercase													=				FeatureExtractionPipeline(__a      ,__a    )

			__lowercase													=				["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
			__lowercase													=				infer_shapes(__a      ,__a    )

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

			# 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       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
			__lowercase													=				["""input_ids""", """attention_mask""", """token_type_ids"""]
			__lowercase													=				{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
			__lowercase													=				ensure_valid_input(FuncContiguousArgs()      ,__a      ,__a    )

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

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

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

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

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

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






		def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
			__lowercase													=				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 	_A  (					A__   ):






 """simple docstring"""



 if not nums:  # Makes sure that the list is not empty
  raise ValueError('''List is empty'''   )

 __lowercase													=				sum(A__   ) / len(A__   )  # Calculate the average
 return sum(abs(x - average   ) for x in nums   ) / len(A__   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

