"""simple docstring"""





import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)
class  _snake_case     (					unittest.TestCase		):






						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     ["a", "b", "c"]

													# Defaults to last layer if both are None
													__lowerCamelCase   ,  __lowerCamelCase					:		Any		   =     get_aligned_output_features_output_indices(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )
													self.assertEqual(UpperCAmelCase							,					["c"]    )
													self.assertEqual(UpperCAmelCase							,					[2]    )

													# Out indices set to match out features
													__lowerCamelCase   ,  __lowerCamelCase					:		Optional[int]		   =     get_aligned_output_features_output_indices(["a", "c"]							,					UpperCAmelCase							,					UpperCAmelCase    )
													self.assertEqual(UpperCAmelCase							,					["a", "c"]    )
													self.assertEqual(UpperCAmelCase							,					[0, 2]    )

													# Out features set to match out indices
													__lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     get_aligned_output_features_output_indices(UpperCAmelCase							,					[0, 2]							,					UpperCAmelCase    )
													self.assertEqual(UpperCAmelCase							,					["a", "c"]    )
													self.assertEqual(UpperCAmelCase							,					[0, 2]    )

													# Out features selected from negative indices
													__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     get_aligned_output_features_output_indices(UpperCAmelCase							,					[-3, -1]							,					UpperCAmelCase    )
													self.assertEqual(UpperCAmelCase							,					["a", "c"]    )
													self.assertEqual(UpperCAmelCase							,					[-3, -1]    )






						def       lowerCamelCase__     (       self      :			Any    ):
													# Stage names must be set
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["a", "b"]							,					(0, 1)							,					UpperCAmelCase    )

													# Out features must be a list
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(("a", "b")							,					(0, 1)							,					["a", "b"]    )

													# Out features must be a subset of stage names
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["a", "b"]							,					(0, 1)							,					["a"]    )

													# Out indices must be a list or tuple
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(UpperCAmelCase							,					0							,					["a", "b"]    )

													# Out indices must be a subset of stage names
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(UpperCAmelCase							,					(0, 1)							,					["a"]    )

													# Out features and out indices must be the same length
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["a", "b"]							,					(0,)							,					["a", "b", "c"]    )

													# Out features should match out indices
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["a", "b"]							,					(0, 2)							,					["a", "b", "c"]    )

													# Out features and out indices should be in order
													with self.assertRaises(UpperCAmelCase    ):
																				verify_out_features_out_indices(["b", "a"]							,					(0, 1)							,					["a", "b"]    )

													# Check passes with valid inputs
													verify_out_features_out_indices(["a", "b", "d"]							,					(0, 1, -1)							,					["a", "b", "c", "d"]    )







						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Tuple		   =     BackboneMixin()

													__lowerCamelCase					:		Any		   =     ["a", "b", "c"]
													__lowerCamelCase					:		Dict		   =     ["a", "c"]
													__lowerCamelCase					:		Optional[Any]		   =     [0, 2]

													# Check that the output features and indices are set correctly
													self.assertEqual(backbone.out_features							,					["a", "c"]    )
													self.assertEqual(backbone.out_indices							,					[0, 2]    )

													# Check out features and indices are updated correctly
													__lowerCamelCase					:		int		   =     ["a", "b"]
													self.assertEqual(backbone.out_features							,					["a", "b"]    )
													self.assertEqual(backbone.out_indices							,					[0, 1]    )

													__lowerCamelCase					:		List[Any]		   =     [-3, -1]
													self.assertEqual(backbone.out_features							,					["a", "c"]    )
													self.assertEqual(backbone.out_indices							,					[-3, -1]    )
"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
							model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)