"""simple docstring"""







import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						argparse.ArgumentParser(
						    description=(
						        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
						        ' Distillation'
						    )
						)
						parser.add_argument('--model_type', default='bert', choices=['bert'])
						parser.add_argument('--model_name', default='bert-base-uncased', type=str)
						parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
						parser.add_argument('--vocab_transform', action='store_true')
						SCREAMING_SNAKE_CASE									=						parser.parse_args()

						if args.model_type == "bert":
												SCREAMING_SNAKE_CASE									=						BertForMaskedLM.from_pretrained(args.model_name)
												SCREAMING_SNAKE_CASE									=						'bert'
						else:
												raise ValueError('args.model_type should be "bert".')

						SCREAMING_SNAKE_CASE									=						model.state_dict()
						SCREAMING_SNAKE_CASE									=						{}

						for w in ["word_embeddings", "position_embeddings"]:
												SCREAMING_SNAKE_CASE									=						state_dict[f"""{prefix}.embeddings.{w}.weight"""]
						for w in ["weight", "bias"]:
												SCREAMING_SNAKE_CASE									=						state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

						SCREAMING_SNAKE_CASE									=						0
						for teacher_idx in [0, 2, 4, 7, 9, 11]:
												for w in ["weight", "bias"]:
																		SCREAMING_SNAKE_CASE									=						state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
																		]
																		SCREAMING_SNAKE_CASE									=						state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
																		]
																		SCREAMING_SNAKE_CASE									=						state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
																		]

																		SCREAMING_SNAKE_CASE									=						state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
																		]
																		SCREAMING_SNAKE_CASE									=						state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
																		]

																		SCREAMING_SNAKE_CASE									=						state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
																		]
																		SCREAMING_SNAKE_CASE									=						state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
																		]
																		SCREAMING_SNAKE_CASE									=						state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
																		]
												std_idx += 1

						SCREAMING_SNAKE_CASE									=						state_dict['cls.predictions.decoder.weight']
						SCREAMING_SNAKE_CASE									=						state_dict['cls.predictions.bias']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		SCREAMING_SNAKE_CASE									=						state_dict[f"""cls.predictions.transform.dense.{w}"""]
																		SCREAMING_SNAKE_CASE									=						state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

						print(f"""N layers selected for distillation: {std_idx}""")
						print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

						print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
						torch.save(compressed_sd, args.dump_checkpoint)




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    str							=							[0 for i in range(len(__UpperCAmelCase			)			)]

	# initialize interval's left pointer and right pointer
	_lowercase				,      _lowercase :    str							=							0, 0

	for i in range(1     ,len(__UpperCAmelCase			)			):
		# case when current index is inside the interval
		if i <= right_pointer:
			_lowercase :    Union[str, Any]							=							min(right_pointer - i + 1     ,z_result[i - left_pointer]			)
			_lowercase :    Any							=							min_edge

		while go_next(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):
			z_result[i] += 1

		# if new index's result gives us more right interval,
		# we've to update left_pointer and right_pointer
		if i + z_result[i] - 1 > right_pointer:
			_lowercase				,      _lowercase :    Optional[int]							=							i, i + z_result[i] - 1

	return z_result


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	return i + z_result[i] < len(__UpperCAmelCase			) and s[z_result[i]] == s[i + z_result[i]]


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							0
	# concatenate 'pattern' and 'input_str' and call z_function
	# with concatenated string
	_lowercase :    List[str]							=							z_function(pattern + input_str			)

	for val in z_result:
		# if value is greater then length of the pattern string
		# that means this index is starting position of substring
		# which is equal to pattern string
		if val >= len(__UpperCAmelCase			):
			answer += 1

	return answer


if __name__ == "__main__":
						import doctest

						doctest.testmod()



