
'''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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
					raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
A			      =      logging.get_logger(__name__)

A			      =      'Hello world! cécé herlolip'





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							bool):
				lowerCamelCase					:			str									= FairseqRobertaModel.from_pretrained(lowerCAmelCase__)
				roberta.eval()  # disable dropout
				lowerCamelCase					:			List[Any]									= roberta.model.encoder.sentence_encoder
				lowerCamelCase					:			Any									= XLMRobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings       ,							hidden_size=roberta.cfg.model.encoder_embed_dim       ,							num_hidden_layers=roberta.cfg.model.encoder_layers       ,							num_attention_heads=roberta.cfg.model.encoder_attention_heads       ,							intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim       ,							max_position_embeddings=5_14       ,							type_vocab_size=1       ,							layer_norm_eps=1E-5       ,							)
				if classification_head:
								lowerCamelCase					:			Any									= roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

				print('Our RoBERTa config:'       ,							lowerCAmelCase__)

				lowerCamelCase					:			List[Any]									= XLMRobertaXLForSequenceClassification(lowerCAmelCase__) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__)
				model.eval()

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

				lowerCamelCase					:			Union[str, Any]									= roberta_sent_encoder.layer_norm.weight
				lowerCamelCase					:			List[Any]									= roberta_sent_encoder.layer_norm.bias

				for i in range(config.num_hidden_layers):
								# Encoder: start of layer
								lowerCamelCase					:			BertLayer									= model.roberta.encoder.layer[i]
								lowerCamelCase					:			TransformerSentenceEncoderLayer									= roberta_sent_encoder.layers[i]

								lowerCamelCase					:			RobertaAttention									= layer.attention
								lowerCamelCase					:			List[Any]									= roberta_layer.self_attn_layer_norm.weight
								lowerCamelCase					:			List[Any]									= roberta_layer.self_attn_layer_norm.bias

								# self attention
								lowerCamelCase					:			BertSelfAttention									= 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))
								)

								lowerCamelCase					:			List[str]									= roberta_layer.self_attn.q_proj.weight
								lowerCamelCase					:			Optional[int]									= roberta_layer.self_attn.q_proj.bias
								lowerCamelCase					:			Any									= roberta_layer.self_attn.k_proj.weight
								lowerCamelCase					:			Optional[Any]									= roberta_layer.self_attn.k_proj.bias
								lowerCamelCase					:			Any									= roberta_layer.self_attn.v_proj.weight
								lowerCamelCase					:			int									= roberta_layer.self_attn.v_proj.bias

								# self-attention output
								lowerCamelCase					:			BertSelfOutput									= layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								lowerCamelCase					:			Optional[Any]									= roberta_layer.self_attn.out_proj.weight
								lowerCamelCase					:			Union[str, Any]									= roberta_layer.self_attn.out_proj.bias

								# this one is final layer norm
								lowerCamelCase					:			str									= roberta_layer.final_layer_norm.weight
								lowerCamelCase					:			List[Any]									= roberta_layer.final_layer_norm.bias

								# intermediate
								lowerCamelCase					:			BertIntermediate									= layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								lowerCamelCase					:			Tuple									= roberta_layer.fca.weight
								lowerCamelCase					:			List[Any]									= roberta_layer.fca.bias

								# output
								lowerCamelCase					:			BertOutput									= layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								lowerCamelCase					:			Dict									= roberta_layer.fca.weight
								lowerCamelCase					:			Optional[Any]									= roberta_layer.fca.bias
								# end of layer

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

				# Let's check that we get the same results.
				lowerCamelCase					:			torch.Tensor									= roberta.encode(lowerCAmelCase__).unsqueeze(0)  # batch of size 1

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

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


if __name__ == "__main__":
					A			      =      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.'
					)
					A			      =      parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)





'''simple docstring'''






import numpy as np





def    UpperCAmelCase							(  UpperCAmelCase__							:							np.array):
				return 1 / (1 + np.exp(-vector))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
