


'''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   : str             =			logging.get_logger(__name__)

A   : str             =			'Hello world! cécé herlolip'





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__						)   ->Any:
						_snake_case:   Any  					=		FairseqRobertaModel.from_pretrained(lowercase__						)
						roberta.eval()  # disable dropout
						_snake_case:   List[Any]  					=		roberta.model.encoder.sentence_encoder
						_snake_case:   Dict  					=		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=514				,  type_vocab_size=1				,  layer_norm_eps=1e-5				,  )
						if classification_head:
												_snake_case:   Optional[Any]  					=		roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

						print('Our RoBERTa config:'				,  lowercase__						)

						_snake_case:   str  					=		XLMRobertaXLForSequenceClassification(lowercase__						) if classification_head else XLMRobertaXLForMaskedLM(lowercase__						)
						model.eval()

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

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

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

												_snake_case:   RobertaAttention  					=		layer.attention
												_snake_case:   List[str]  					=		roberta_layer.self_attn_layer_norm.weight
												_snake_case:   Union[str, Any]  					=		roberta_layer.self_attn_layer_norm.bias

												# self attention
												_snake_case:   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)						)
												)

												_snake_case:   Union[str, Any]  					=		roberta_layer.self_attn.q_proj.weight
												_snake_case:   List[str]  					=		roberta_layer.self_attn.q_proj.bias
												_snake_case:   Tuple  					=		roberta_layer.self_attn.k_proj.weight
												_snake_case:   Dict  					=		roberta_layer.self_attn.k_proj.bias
												_snake_case:   str  					=		roberta_layer.self_attn.v_proj.weight
												_snake_case:   List[Any]  					=		roberta_layer.self_attn.v_proj.bias

												# self-attention output
												_snake_case:   BertSelfOutput  					=		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:   List[str]  					=		roberta_layer.self_attn.out_proj.bias

												# this one is final layer norm
												_snake_case:   Optional[int]  					=		roberta_layer.final_layer_norm.weight
												_snake_case:   Optional[int]  					=		roberta_layer.final_layer_norm.bias

												# intermediate
												_snake_case:   BertIntermediate  					=		layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												_snake_case:   List[str]  					=		roberta_layer.fca.weight
												_snake_case:   Optional[int]  					=		roberta_layer.fca.bias

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

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

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

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

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


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












'''simple docstring'''






from ..utils import DummyObject, requires_backends







class 			lowerCamelCase					(						metaclass=__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    ["torch", "scipy"]


					def __init__(					self	:  Tuple       ,     *__snake_case	:  List[Any]       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





											requires_backends(self       ,     ['torch', 'scipy']       )


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Optional[int]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





											requires_backends(cls       ,     ['torch', 'scipy']       )




					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  List[Any]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  str       ):





											'''simple docstring'''





											requires_backends(cls       ,     ['torch', 'scipy']       )





