
import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Tuple						,   snake_case__:					List[str]					):


				'''simple docstring'''
				# Initialise PyTorch model
				lowercase_       =					AlbertConfig.from_json_file(snake_case__					)
				print(F'''Building PyTorch model from configuration: {config}'''					)
				lowercase_       =					AlbertForPreTraining(snake_case__					)

				# Load weights from tf checkpoint
				load_tf_weights_in_albert(snake_case__						,   snake_case__						,   snake_case__					)

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
				torch.save(model.state_dict()						,   snake_case__					)


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(
						    '--albert_config_file',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'The config json file corresponding to the pre-trained ALBERT model. \n'
						        'This 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.albert_config_file, args.pytorch_dump_path)






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


__a  			=					'sshleifer/bart-tiny-random'
__a  			=					'patrickvonplaten/t5-tiny-random'
@require_torch
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@cached_property
	def 						_lowercase  (       self  :			Any       )       ->     Tuple:
					return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=1       )
					self.assertEqual(student.config.num_hidden_layers       ,							1       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=SCREAMING_SNAKE_CASE_       )
					self.assertEqual(student.config.encoder_layers       ,							1       )
					self.assertEqual(student.config.decoder_layers       ,							self.teacher_config.encoder_layers       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=1       )
					self.assertEqual(student.config.encoder_layers       ,							1       )
					self.assertEqual(student.config.decoder_layers       ,							1       )




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
									create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=SCREAMING_SNAKE_CASE_       ,							d=SCREAMING_SNAKE_CASE_       )




