


"""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    __a						( __lowerCAmelCase							):







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			SMALL_MODEL_IDENTIFIER
					lowerCAmelCase_      							=			'''pt'''
					lowerCAmelCase_      							=			'''tf'''







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			AutoModel.from_pretrained(self.test_model   )
					model_pt.save_pretrained(__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TFAutoModel.from_pretrained(self.test_model      ,	from_pt=__SCREAMING_SNAKE_CASE   )
					model_tf.save_pretrained(__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			'''mock_framework'''

					# Framework provided - return whatever the user provides
					lowerCAmelCase_      							=			FeaturesManager.determine_framework(self.test_model      ,	__SCREAMING_SNAKE_CASE   )
					self.assertEqual(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )

					# Local checkpoint and framework provided - return provided framework
					# PyTorch checkpoint
					with TemporaryDirectory() as local_pt_ckpt:
						self._setup_pt_ckpt(__SCREAMING_SNAKE_CASE   )
						lowerCAmelCase_      							=			FeaturesManager.determine_framework(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )
						self.assertEqual(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )

					# TensorFlow checkpoint
					with TemporaryDirectory() as local_tf_ckpt:
						self._setup_tf_ckpt(__SCREAMING_SNAKE_CASE   )
						lowerCAmelCase_      							=			FeaturesManager.determine_framework(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )
						self.assertEqual(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					with TemporaryDirectory() as local_pt_ckpt:
						self._setup_pt_ckpt(__SCREAMING_SNAKE_CASE   )
						lowerCAmelCase_      							=			FeaturesManager.determine_framework(__SCREAMING_SNAKE_CASE   )
						self.assertEqual(__SCREAMING_SNAKE_CASE      ,	self.framework_pt   )

					# TensorFlow checkpoint
					with TemporaryDirectory() as local_tf_ckpt:
						self._setup_tf_ckpt(__SCREAMING_SNAKE_CASE   )
						lowerCAmelCase_      							=			FeaturesManager.determine_framework(__SCREAMING_SNAKE_CASE   )
						self.assertEqual(__SCREAMING_SNAKE_CASE      ,	self.framework_tf   )

					# Invalid local checkpoint
					with TemporaryDirectory() as local_invalid_ckpt:
						with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
							lowerCAmelCase_      							=			FeaturesManager.determine_framework(__SCREAMING_SNAKE_CASE   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			MagicMock(return_value=__SCREAMING_SNAKE_CASE   )
					with patch('''transformers.onnx.features.is_tf_available'''      ,	__SCREAMING_SNAKE_CASE   ):
						lowerCAmelCase_      							=			FeaturesManager.determine_framework(self.test_model   )
						self.assertEqual(__SCREAMING_SNAKE_CASE      ,	self.framework_pt   )

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

					# Both in environment -> use PyTorch
					lowerCAmelCase_      							=			MagicMock(return_value=__SCREAMING_SNAKE_CASE   )
					lowerCAmelCase_      							=			MagicMock(return_value=__SCREAMING_SNAKE_CASE   )
					with patch('''transformers.onnx.features.is_tf_available'''      ,	__SCREAMING_SNAKE_CASE   ), patch(
					    '''transformers.onnx.features.is_torch_available'''      ,	__SCREAMING_SNAKE_CASE   ):
						lowerCAmelCase_      							=			FeaturesManager.determine_framework(self.test_model   )
						self.assertEqual(__SCREAMING_SNAKE_CASE      ,	self.framework_pt   )

					# Both not in environment -> raise error
					lowerCAmelCase_      							=			MagicMock(return_value=__SCREAMING_SNAKE_CASE   )
					lowerCAmelCase_      							=			MagicMock(return_value=__SCREAMING_SNAKE_CASE   )
					with patch('''transformers.onnx.features.is_tf_available'''      ,	__SCREAMING_SNAKE_CASE   ), patch(
					    '''transformers.onnx.features.is_torch_available'''      ,	__SCREAMING_SNAKE_CASE   ):
						with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
							lowerCAmelCase_      							=			FeaturesManager.determine_framework(self.test_model   )





'''simple docstring'''



from collections import deque







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	deque()
     __snake_case            =	[False for _ in range(_lowerCamelCase						)]
     __snake_case            =	[-1 for _ in range(_lowerCamelCase						)]
     __snake_case            =	index_of[:]

     def strong_connect(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str]						):
          __snake_case            =	index  # the number when this node is seen
          __snake_case            =	index  # lowest rank node reachable from here
          index += 1
          stack.append(_lowerCamelCase						)
          __snake_case            =	True

          for w in g[v]:
               if index_of[w] == -1:
                    __snake_case            =	strong_connect(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               __snake_case            =	[]
               __snake_case            =	stack.pop()
               __snake_case            =	False
               component.append(_lowerCamelCase						)
               while w != v:
                    __snake_case            =	stack.pop()
                    __snake_case            =	False
                    component.append(_lowerCamelCase						)
               components.append(_lowerCamelCase						)
          return index

     __snake_case            =	[]
     for v in range(_lowerCamelCase						):
          if index_of[v] == -1:
               strong_connect(_lowerCamelCase ,						0 ,						_lowerCamelCase						)

     return components







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
     for u, v in edges:
          g[u].append(_lowerCamelCase						)
     return g


if __name__ == "__main__":
     # Test
     UpperCAmelCase_							:	List[str]	     =		7
     UpperCAmelCase_							:	int	     =		[0, 0, 1, 2, 3, 3, 4, 4, 6]
     UpperCAmelCase_							:	Dict	     =		[1, 3, 2, 0, 1, 4, 5, 6, 5]
     UpperCAmelCase_							:	List[str]	     =		[(u, v) for u, v in zip(source, target)]
     UpperCAmelCase_							:	Tuple	     =		create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






