
'''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       UpperCamelCase__  (		UpperCamelCase_     ):







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = SMALL_MODEL_IDENTIFIER
				a__     = '''pt'''
				a__     = '''tf'''







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				a__     = AutoModel.from_pretrained(self.test_model		)
				model_pt.save_pretrained(lowerCamelCase		)







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   int		):




				'''simple docstring'''
				a__     = TFAutoModel.from_pretrained(self.test_model , from_pt=lowerCamelCase		)
				model_tf.save_pretrained(lowerCamelCase		)







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = '''mock_framework'''

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

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

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







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				# PyTorch checkpoint
				with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(lowerCamelCase		)
							a__     = FeaturesManager.determine_framework(lowerCamelCase		)
							self.assertEqual(lowerCamelCase , self.framework_pt		)

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

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







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = MagicMock(return_value=lowerCamelCase		)
				with patch("transformers.onnx.features.is_tf_available" , lowerCamelCase		):
							a__     = FeaturesManager.determine_framework(self.test_model		)
							self.assertEqual(lowerCamelCase , self.framework_pt		)

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

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

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










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[list[float]]:
			a__     = []
			for data in source_data:
						for i, el in enumerate(__lowerCamelCase     ):
									if len(__lowerCamelCase     ) < i + 1:
												data_lists.append([]     )
									data_lists[i].append(float(__lowerCamelCase     )     )
			return data_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = []
			for dlist, weight in zip(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = min(__lowerCamelCase     )
						a__     = max(__lowerCamelCase     )

						a__     = []
						# for weight 0 score is 1 - actual score
						if weight == 0:
									for item in dlist:
												try:
															score.append(1 - ((item - mind) / (maxd - mind))     )
												except ZeroDivisionError:
															score.append(1     )

						elif weight == 1:
									for item in dlist:
												try:
															score.append((item - mind) / (maxd - mind)     )
												except ZeroDivisionError:
															score.append(0     )

        # weight not 0 or 1
						else:
									a__     = f'''Invalid weight of {weight:f} provided'''
									raise ValueError(__lowerCamelCase     )

						score_lists.append(__lowerCamelCase     )

			return score_lists







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]     )   ->    list[float]:
			a__     = [0 for i in range(len(score_lists[0]     )     )]

			for slist in score_lists:
						for j, ele in enumerate(__lowerCamelCase     ):
									a__     = final_scores[j] + ele

			return final_scores







def    _lowerCamelCase  (__lowerCamelCase  :     list[list[float]]					,  __lowerCamelCase  :     list[int]     )   ->    list[list[float]]:
			a__     = get_data(__lowerCamelCase     )
			a__     = calculate_each_score(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = generate_final_scores(__lowerCamelCase     )

			# append scores to source data
			for i, ele in enumerate(__lowerCamelCase     ):
						source_data[i].append(__lowerCamelCase     )

			return source_data



