"""simple docstring"""




import datasets


__lowerCamelCase							 =     "\\n@InProceedings{conneau2018xnli,\n  author = \"Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin\",\n  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  location = \"Brussels, Belgium\",\n}\n"

__lowerCamelCase							 =     "\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"

__lowerCamelCase							 =     "\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    'accuracy': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric(\"xnli\")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n"






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							return (preds == labels).mean()


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION       )
class 			UpperCamelCase__(	datasets.Metric       ):





						def  snake_case__		(				self   )    -> Dict:
													return datasets.MetricInfo(
													    description=_DESCRIPTION						,citation=_CITATION						,inputs_description=_KWARGS_DESCRIPTION						,features=datasets.Features(
													        {
													            'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'   ),
													            'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'   ),
													        }   )						,codebase_urls=[]						,reference_urls=[]						,format='numpy'						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													return {"accuracy": simple_accuracy(__UpperCAmelCase						,__UpperCAmelCase   )}






"""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__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													return None






class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													return None






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





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							['[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()
																				A__		   =							BertTokenizerFast(vocab_file.name   )

													with TemporaryDirectory() as bert_save_dir:
																				A__		   =							BertModel(BertConfig(vocab_size=len(__UpperCAmelCase   )   )   )
																				model.save_pretrained(__UpperCAmelCase   )
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,__UpperCAmelCase   )





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )
																				A__		   =							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  snake_case__		(				self   )    -> str:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )
																				A__		   =							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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Union[str, Any]:
													try:
																				# Compute path
																				with TemporaryDirectory() as tempdir:
																											A__		   =							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  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

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





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

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





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													A__		   =							FeatureExtractionPipeline(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
													A__   ,     A__   ,     A__   ,     A__		   =							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  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							['input_ids', 'attention_mask', 'token_type_ids']
													A__		   =							{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
													A__   ,     A__		   =							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)
													A__   ,     A__		   =							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  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							generate_identified_filename(Path('/home/something/my_fake_model.onnx'   )						,'-test'   )
													self.assertEqual('/home/something/my_fake_model-test.onnx'						,generated.as_posix()   )





