






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__a:     Dict   					=					['''small''', '''medium''', '''large''']

__a:     int   					=					'''lm_head.decoder.weight'''
__a:     Dict   					=					'''lm_head.weight'''




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						int:
       _UpperCAmelCase							=    torch.load(__snake_case )
       _UpperCAmelCase							=    d.pop(__snake_case )
       os.makedirs(__snake_case   ,      exist_ok=__snake_case )
       torch.save(__snake_case   ,      os.path.join(__snake_case   ,      __snake_case ) )


if __name__ == "__main__":
  __a:     Optional[Any]   					=					argparse.ArgumentParser()
  parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
  __a:     str   					=					parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    __a:     Optional[int]   					=					os.path.join(args.dialogpt_path, F"{MODEL}_ft.pkl")
    __a:     Dict   					=					F"./DialoGPT-{MODEL}"
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




"""simple docstring"""







import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)






class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					["a", "b", "c"]

				# Defaults to last layer if both are None
				A__							,							A__											=					get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	["c"]		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[2]		)

				# Out indices set to match out features
				A__							,							A__											=					get_aligned_output_features_output_indices(["a", "c"]						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	["a", "c"]		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[0, 2]		)

				# Out features set to match out indices
				A__							,							A__											=					get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__						,	[0, 2]						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	["a", "c"]		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[0, 2]		)

				# Out features selected from negative indices
				A__							,							A__											=					get_aligned_output_features_output_indices(SCREAMING_SNAKE_CASE__						,	[-3, -1]						,	SCREAMING_SNAKE_CASE__		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	["a", "c"]		)
				self.assertEqual(SCREAMING_SNAKE_CASE__						,	[-3, -1]		)


			def   snake_case__	(      self		)      ->	Dict:
				# Stage names must be set
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					verify_out_features_out_indices(["a", "b"]						,	(0, 1)						,	SCREAMING_SNAKE_CASE__		)

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

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

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

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

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

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

				# Out features and out indices should be in order
				with self.assertRaises(SCREAMING_SNAKE_CASE__		):
					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   snake_case__	(      self		)      ->	List[Any]:
				A__											=					BackboneMixin()

				A__											=					["a", "b", "c"]
				A__											=					["a", "c"]
				A__											=					[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
				A__											=					["a", "b"]
				self.assertEqual(backbone.out_features						,	["a", "b"]		)
				self.assertEqual(backbone.out_indices						,	[0, 1]		)

				A__											=					[-3, -1]
				self.assertEqual(backbone.out_features						,	["a", "c"]		)
				self.assertEqual(backbone.out_indices						,	[-3, -1]		)




