
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()
lowercase__		:int   =    logging.get_logger(__name__)

lowercase__		:Union[str, Any]   =    "Hello world! cécé herlolip"



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						FairseqRobertaModel.from_pretrained(lowerCAmelCase__		)
					roberta.eval()  # disable dropout
					lowercase							=						roberta.model.encoder.sentence_encoder
					lowercase							=						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:
										lowercase							=						roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

					print('''Our RoBERTa config:''' ,			lowerCAmelCase__		)

					lowercase							=						XLMRobertaXLForSequenceClassification(lowerCAmelCase__		) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase__		)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowercase							=						roberta_sent_encoder.embed_tokens.weight
					lowercase							=						roberta_sent_encoder.embed_positions.weight
					lowercase							=						torch.zeros_like(
					    model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c RoBERTa doesn't use them.

					lowercase							=						roberta_sent_encoder.layer_norm.weight
					lowercase							=						roberta_sent_encoder.layer_norm.bias

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

										lowercase							=						layer.attention
										lowercase							=						roberta_layer.self_attn_layer_norm.weight
										lowercase							=						roberta_layer.self_attn_layer_norm.bias

										# self attention
										lowercase							=						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)		)
										)

										lowercase							=						roberta_layer.self_attn.q_proj.weight
										lowercase							=						roberta_layer.self_attn.q_proj.bias
										lowercase							=						roberta_layer.self_attn.k_proj.weight
										lowercase							=						roberta_layer.self_attn.k_proj.bias
										lowercase							=						roberta_layer.self_attn.v_proj.weight
										lowercase							=						roberta_layer.self_attn.v_proj.bias

										# self-attention output
										lowercase							=						layer.attention.output
										assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
										lowercase							=						roberta_layer.self_attn.out_proj.weight
										lowercase							=						roberta_layer.self_attn.out_proj.bias

										# this one is final layer norm
										lowercase							=						roberta_layer.final_layer_norm.weight
										lowercase							=						roberta_layer.final_layer_norm.bias

										# intermediate
										lowercase							=						layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										lowercase							=						roberta_layer.fca.weight
										lowercase							=						roberta_layer.fca.bias

										# output
										lowercase							=						layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										lowercase							=						roberta_layer.fca.weight
										lowercase							=						roberta_layer.fca.bias
										# end of layer

					if classification_head:
										lowercase							=						roberta.model.classification_heads['''mnli'''].dense.weight
										lowercase							=						roberta.model.classification_heads['''mnli'''].dense.bias
										lowercase							=						roberta.model.classification_heads['''mnli'''].out_proj.weight
										lowercase							=						roberta.model.classification_heads['''mnli'''].out_proj.bias
					else:
										# LM Head
										lowercase							=						roberta.model.encoder.lm_head.dense.weight
										lowercase							=						roberta.model.encoder.lm_head.dense.bias
										lowercase							=						roberta.model.encoder.lm_head.layer_norm.weight
										lowercase							=						roberta.model.encoder.lm_head.layer_norm.bias
										lowercase							=						roberta.model.encoder.lm_head.weight
										lowercase							=						roberta.model.encoder.lm_head.bias

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

					lowercase							=						model(lowerCAmelCase__		)[0]
					if classification_head:
										lowercase							=						roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCAmelCase__		)		)
					else:
										lowercase							=						roberta.model(lowerCAmelCase__		)[0]
					print(our_output.shape ,			their_output.shape		)
					lowercase							=						torch.max(torch.abs(our_output - their_output		)		).item()
					print(f'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
					lowercase							=						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__":
							lowercase__		:Dict   =    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_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)










from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




