





'''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_   :





     def 							lowercase__      ( self		:					Optional[Any]	,      lowercase		:					List[str]	,      lowercase		:					Optional[int]	,      lowercase		:					List[Any]						):





        """simple docstring"""




        return None







class       a_   :





     def 							lowercase__      ( self		:					int	,      lowercase		:					List[str]	,      lowercase		:					int	,      lowercase		:					Any	,      lowercase		:					Tuple						):





        """simple docstring"""




        return None







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





     @require_tf
     @slow
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




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





     @require_torch
     @slow
     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




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





     @require_torch
     @slow
     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        from transformers import BertModel

        lowercase_			:str    			= ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
        with NamedTemporaryFile(mode="w+t"						) as vocab_file:
           vocab_file.write("\n".join(lowercase						)						)
           vocab_file.flush()
           lowercase_			:List[str]    			= BertTokenizerFast(vocab_file.name						)

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





     @require_tf
     @slow
     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           lowercase_			:str    			= self._test_export(lowercase	,      "tf"	,      12	,      **lowercase						)
           lowercase_			:Dict    			= quantize(Path(lowercase						)						)

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





     @require_torch
     @slow
     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
           lowercase_			:Tuple    			= self._test_export(lowercase	,      "pt"	,      12	,      **lowercase						)
           lowercase_			:Dict    			= quantize(lowercase						)

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





     def 							lowercase__      ( self		:					Optional[int]	,      lowercase		:					int	,      lowercase		:					Any	,      lowercase		:					Dict	,      lowercase		:					Any=None	,      **lowercase		:					Dict						):





        """simple docstring"""




        try:
           # Compute path
           with TemporaryDirectory() as tempdir:
              lowercase_			:str    			= Path(lowercase						).joinpath("model.onnx"						)

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

           # Export
           convert(lowercase	,      lowercase	,      lowercase	,      lowercase	,      lowercase	,      **lowercase						)

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





     @require_torch
     @require_tokenizers
     @slow
     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        from transformers import BertModel

        lowercase_			:List[str]    			= BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"						)						)
        lowercase_			:Optional[int]    			= BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"						)
        self._test_infer_dynamic_axis(lowercase	,      lowercase	,      "pt"						)





     @require_tf
     @require_tokenizers
     @slow
     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        from transformers import TFBertModel

        lowercase_			:str    			= TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"						)						)
        lowercase_			:str    			= BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"						)
        self._test_infer_dynamic_axis(lowercase	,      lowercase	,      "tf"						)





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Tuple	,      lowercase		:					List[Any]	,      lowercase		:					Any						):





        """simple docstring"""




        lowercase_			:List[Any]    			= FeatureExtractionPipeline(lowercase	,      lowercase						)

        lowercase_			:str    			= ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Tuple    			= infer_shapes(lowercase	,      lowercase						)

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

        # 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 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= ["input_ids", "attention_mask", "token_type_ids"]
        lowercase_			:Optional[Any]    			= {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
        lowercase_			,							lowercase_			:Optional[int]    			= ensure_valid_input(FuncContiguousArgs()	,      lowercase	,      lowercase						)

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

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

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

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        lowercase_			,							lowercase_			:str    			= ensure_valid_input(FuncNonContiguousArgs()	,      lowercase	,      lowercase						)

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

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







     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Dict    			= 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'''


import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):
   monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"	,set() )
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   class       a_   :





        def __init__( self		:					int	,      lowercase		:					int						):





           """simple docstring"""




           lowercase_			:Optional[Any]    			= metric_id







   class       a_   :
        __A      =			[MetricMock(_lowerCAmelCase							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]





        def 							lowercase__      ( self		:					Union[str, Any]						):





           """simple docstring"""




           return self._metrics







   monkeypatch.setattr("datasets.inspect.huggingface_hub"	,HfhMock() )


@pytest.mark.parametrize(
    "func, args"	,[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Tuple ):
   if "tmp_path" in args:
      lowercase_			:Union[str, Any]    			= tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
   with pytest.warns(__lowerCamelCase	,match="https://huggingface.co/docs/evaluate" ):
      func(*__lowerCamelCase )


