
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 			_lowerCAmelCase      :




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							return None





class 			_lowerCAmelCase      :




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Any:
							return None





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




				@require_tf
				@slow
				def   __a		(      self  )       ->   Tuple:
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
										self._test_export(_UpperCamelCase       ,       "tf"       ,       12       ,       **_UpperCamelCase  )




				@require_torch
				@slow
				def   __a		(      self  )       ->   Optional[int]:
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
										self._test_export(_UpperCamelCase       ,       "pt"       ,       12       ,       **_UpperCamelCase  )




				@require_torch
				@slow
				def   __a		(      self  )       ->   Tuple:
							from transformers import BertModel

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

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




				@require_tf
				@slow
				def   __a		(      self  )       ->   str:
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
										lowerCAmelCase_      			=			self._test_export(_UpperCamelCase       ,       "tf"       ,       12       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			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   __a		(      self  )       ->   Any:
							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
										lowerCAmelCase_      			=			self._test_export(_UpperCamelCase       ,       "pt"       ,       12       ,       **_UpperCamelCase  )
										lowerCAmelCase_      			=			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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       **_UpperCamelCase  )       ->   Any:
							try:
										# Compute path
										with TemporaryDirectory() as tempdir:
													lowerCAmelCase_      			=			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   __a		(      self  )       ->   Dict:
							from transformers import BertModel

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




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

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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			FeatureExtractionPipeline(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			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   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			["input_ids", "attention_mask", "token_type_ids"]
							lowerCAmelCase_      			=			{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
							lowerCAmelCase_			,						lowerCAmelCase_      			=			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)
							lowerCAmelCase_			,						lowerCAmelCase_      			=			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   __a		(      self  )       ->   List[str]:
							lowerCAmelCase_      			=			generate_identified_filename(Path("/home/something/my_fake_model.onnx"  )       ,       "-test"  )
							self.assertEqual("/home/something/my_fake_model-test.onnx"       ,       generated.as_posix()  )





from string import ascii_lowercase, ascii_uppercase






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			if not sentence:
						return ""
			lowerCAmelCase_      			=			dict(zip(__lowerCAmelCase					,							__lowerCAmelCase       )       )
			return lower_to_upper.get(sentence[0]					,							sentence[0]       ) + sentence[1:]


if __name__ == "__main__":
			from doctest import testmod

			testmod()



