



'''simple docstring'''



import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
				raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase       :						List[str]						=logging.get_logger(__name__)

_lowercase       :						Any						="""Hello world! cécé herlolip"""


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						Dict:
				snake_case__      :   Union[str, Any]      =    FairseqRobertaModel.from_pretrained(UpperCamelCase_			)
				roberta.eval()  # disable dropout
				snake_case__      :   Tuple      =    roberta.model.encoder.sentence_encoder
				snake_case__      :   Any      =    RobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,				hidden_size=roberta.args.encoder_embed_dim   ,				num_hidden_layers=roberta.args.encoder_layers   ,				num_attention_heads=roberta.args.encoder_attention_heads   ,				intermediate_size=roberta.args.encoder_ffn_embed_dim   ,				max_position_embeddings=514   ,				type_vocab_size=1   ,				layer_norm_eps=1e-5   ,				)
				if classification_head:
								snake_case__      :   str      =    roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase_			)

				snake_case__      :   List[Any]      =    RobertaForSequenceClassification(UpperCamelCase_			) if classification_head else RobertaForMaskedLM(UpperCamelCase_			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Tuple      =    roberta_sent_encoder.embed_tokens.weight
				snake_case__      :   List[Any]      =    roberta_sent_encoder.embed_positions.weight
				snake_case__      :   str      =    torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c RoBERTa doesn't use them.
				snake_case__      :   List[str]      =    roberta_sent_encoder.emb_layer_norm.weight
				snake_case__      :   List[str]      =    roberta_sent_encoder.emb_layer_norm.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								snake_case__      :   Optional[Any]      =    model.roberta.encoder.layer[i]
								snake_case__      :   Tuple      =    roberta_sent_encoder.layers[i]

								# self attention
								snake_case__      :   Union[str, Any]      =    layer.attention.self
								assert (
								    roberta_layer.self_attn.k_proj.weight.data.shape
								    == roberta_layer.self_attn.q_proj.weight.data.shape
								    == roberta_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)			)
								)

								snake_case__      :   int      =    roberta_layer.self_attn.q_proj.weight
								snake_case__      :   Tuple      =    roberta_layer.self_attn.q_proj.bias
								snake_case__      :   Optional[Any]      =    roberta_layer.self_attn.k_proj.weight
								snake_case__      :   Any      =    roberta_layer.self_attn.k_proj.bias
								snake_case__      :   Tuple      =    roberta_layer.self_attn.v_proj.weight
								snake_case__      :   Tuple      =    roberta_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__      :   Dict      =    layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								snake_case__      :   int      =    roberta_layer.self_attn.out_proj.weight
								snake_case__      :   int      =    roberta_layer.self_attn.out_proj.bias
								snake_case__      :   List[Any]      =    roberta_layer.self_attn_layer_norm.weight
								snake_case__      :   str      =    roberta_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   int      =    layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								snake_case__      :   Tuple      =    roberta_layer.fca.weight
								snake_case__      :   int      =    roberta_layer.fca.bias

								# output
								snake_case__      :   List[str]      =    layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								snake_case__      :   List[Any]      =    roberta_layer.fca.weight
								snake_case__      :   Optional[int]      =    roberta_layer.fca.bias
								snake_case__      :   Optional[Any]      =    roberta_layer.final_layer_norm.weight
								snake_case__      :   Union[str, Any]      =    roberta_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Optional[Any]      =    roberta.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   int      =    roberta.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   int      =    roberta.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   Union[str, Any]      =    roberta.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   Union[str, Any]      =    roberta.model.encoder.lm_head.dense.weight
								snake_case__      :   Tuple      =    roberta.model.encoder.lm_head.dense.bias
								snake_case__      :   Any      =    roberta.model.encoder.lm_head.layer_norm.weight
								snake_case__      :   int      =    roberta.model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    roberta.model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__      :   Tuple      =    roberta.encode(UpperCamelCase_			).unsqueeze(0			)  # batch of size 1

				snake_case__      :   Optional[int]      =    model(UpperCamelCase_			)[0]
				if classification_head:
								snake_case__      :   Union[str, Any]      =    roberta.model.classification_heads['''mnli'''](roberta.extract_features(UpperCamelCase_			)			)
				else:
								snake_case__      :   Tuple      =    roberta.model(UpperCamelCase_			)[0]
				print(our_output.shape   ,				their_output.shape			)
				snake_case__      :   Any      =    torch.max(torch.abs(our_output - their_output			)			).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
				snake_case__      :   Any      =    torch.allclose(UpperCamelCase_   ,				UpperCamelCase_   ,				atol=1e-3			)
				print('''Do both models output the same tensors?'''   ,				'''🔥''' if success else '''💩'''			)
				if not success:
								raise Exception('''Something went wRoNg'''			)

				pathlib.Path(UpperCamelCase_			).mkdir(parents=UpperCamelCase_   ,				exist_ok=UpperCamelCase_			)
				print(F'''Saving model to {pytorch_dump_folder_path}'''			)
				model.save_pretrained(UpperCamelCase_			)


if __name__ == "__main__":
				_lowercase       :						Union[str, Any]						=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--classification_head", action="store_true", help="Whether to convert a final classification head."
				)
				_lowercase       :						Optional[int]						=parser.parse_args()
				convert_roberta_checkpoint_to_pytorch(
				    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)












from __future__ import annotations







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]		)	-> list[int]:

						"""simple docstring"""



						if not list_of_ints:
												return []

						if min(UpperCamelCase_		) < 0:
												raise ValueError('All numbers must be positive'		)

						lowerCAmelCase__         =    max(len(bin(UpperCamelCase_		)[2:]		) for x in list_of_ints		)
						return _msd_radix_sort(UpperCamelCase_     ,							UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]     ,							UpperCamelCase_							:       int		)	-> list[int]:

						"""simple docstring"""



						if bit_position == 0 or len(UpperCamelCase_		) in [0, 1]:
												return list_of_ints

						lowerCAmelCase__         =    []
						lowerCAmelCase__         =    []
						# Split numbers based on bit at bit_position from the right
						for number in list_of_ints:
												if (number >> (bit_position - 1)) & 1:
																		# number has a one at bit bit_position
																		ones.append(UpperCamelCase_		)
												else:
																		# number has a zero at bit bit_position
																		zeros.append(UpperCamelCase_		)

    # recursively split both lists further
						lowerCAmelCase__         =    _msd_radix_sort(UpperCamelCase_     ,							bit_position - 1		)
						lowerCAmelCase__         =    _msd_radix_sort(UpperCamelCase_     ,							bit_position - 1		)

						# recombine lists
						lowerCAmelCase__         =    zeros
						res.extend(UpperCamelCase_		)

						return res







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    len(UpperCamelCase_		)
						if not list_of_ints or length == 1:
												return

						if min(UpperCamelCase_		) < 0:
												raise ValueError('All numbers must be positive'		)

						lowerCAmelCase__         =    max(len(bin(UpperCamelCase_		)[2:]		) for x in list_of_ints		)
						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							0     ,							UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       int		)	-> int:

						"""simple docstring"""



						if bit_position == 0 or end_index - begin_index <= 1:
												return

						bit_position -= 1

						lowerCAmelCase__         =    begin_index
						lowerCAmelCase__         =    end_index - 1
						while i <= j:
												lowerCAmelCase__         =    False
												if not (list_of_ints[i] >> bit_position) & 1:
																		# found zero at the beginning
																		i += 1
																		lowerCAmelCase__         =    True
												if (list_of_ints[j] >> bit_position) & 1:
																		# found one at the end
																		j -= 1
																		lowerCAmelCase__         =    True

												if changed:
																		continue

												lowerCAmelCase__    ,							lowerCAmelCase__         =    list_of_ints[j], list_of_ints[i]
												j -= 1
												if j != i:
																		i += 1

						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)
						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





