





'''simple docstring'''




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class 					SCREAMING_SNAKE_CASE       (a__				):


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			SMALL_MODEL_IDENTIFIER
          __A		:					Optional[Any]						=			'pt'
          __A		:					Optional[int]						=			'tf'


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[Any]						=			AutoModel.from_pretrained(self.test_model)
          model_pt.save_pretrained(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			TFAutoModel.from_pretrained(self.test_model		,      from_pt=_UpperCAmelCase)
          model_tf.save_pretrained(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					str						=			'mock_framework'

          # Framework provided - return whatever the user provides
          __A		:					int						=			FeaturesManager.determine_framework(self.test_model		,      _UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase		,      _UpperCAmelCase)

          # Local checkpoint and framework provided - return provided framework
          # PyTorch checkpoint
          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A		:					List[str]						=			FeaturesManager.determine_framework(_UpperCAmelCase		,      _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase		,      _UpperCAmelCase)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A		:					str						=			FeaturesManager.determine_framework(_UpperCAmelCase		,      _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A		:					str						=			FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase		,      self.framework_pt)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A		:					Union[str, Any]						=			FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase		,      self.framework_tf)

          # Invalid local checkpoint
          with TemporaryDirectory() as local_invalid_ckpt:
               with self.assertRaises(_UpperCAmelCase):
                    __A		:					Tuple						=			FeaturesManager.determine_framework(_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'		,      _UpperCAmelCase):
               __A		:					Union[str, Any]						=			FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase		,      self.framework_pt)

          # PyTorch not in environment -> use TensorFlow
          __A		:					int						=			MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_torch_available'		,      _UpperCAmelCase):
               __A		:					Any						=			FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase		,      self.framework_tf)

          # Both in environment -> use PyTorch
          __A		:					List[Any]						=			MagicMock(return_value=_UpperCAmelCase)
          __A		:					Tuple						=			MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'		,      _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'		,      _UpperCAmelCase):
               __A		:					Union[str, Any]						=			FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase		,      self.framework_pt)

          # Both not in environment -> raise error
          __A		:					Tuple						=			MagicMock(return_value=_UpperCAmelCase)
          __A		:					Tuple						=			MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'		,      _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'		,      _UpperCAmelCase):
               with self.assertRaises(_UpperCAmelCase):
                    __A		:					Optional[int]						=			FeaturesManager.determine_framework(self.test_model)






'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()