






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       :






  """simple docstring"""





  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Optional[Any]:
     return None




class 						_a       :






  """simple docstring"""





  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[str]:
     return None




class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  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    _UpperCAmelCase     (     self )			-> Dict:
     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
        self._test_export(_UpperCAmelCase		,		'tf'		,		12		,		**_UpperCAmelCase )




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




  @require_torch
  @slow
  def    _UpperCAmelCase     (     self )			-> Dict:
     from transformers import BertModel

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

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




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

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




  @require_tf
  @require_tokenizers
  @slow
  def    _UpperCAmelCase     (     self )			-> int:
     from transformers import TFBertModel

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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     FeatureExtractionPipeline(_UpperCAmelCase		,		_UpperCAmelCase )

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













import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


snake_case__      :	Optional[int]       	=      pytest.mark.integration






@pytest.mark.parametrize('path'    ,      ['paws', 'csv'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_dataset(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning')
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
@pytest.mark.parametrize('path'    ,      ['accuracy'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_metric(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.parametrize(
    'path, config_name, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_info(__lowercase    ,      config_name=__lowercase)
   assert info.config_name == config_name
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_config_info(__lowercase    ,      config_name=__lowercase)






@pytest.mark.parametrize(
    'path, expected'    ,      [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_names(__lowercase)
   assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'    ,      [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert list(infos.keys()) == expected_configs
   UpperCamelCase_									=     expected_configs[0]
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_split_names(__lowercase    ,      config_name=__lowercase)





