
"""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()
_snake_case          =						logging.get_logger(__name__)

_snake_case          =						'''Hello world! cécé herlolip'''






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					bool						):






							"""simple docstring"""



							_lowerCAmelCase  	=			FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE						)
							roberta.eval()  # disable dropout
							_lowerCAmelCase  	=			roberta.model.encoder.sentence_encoder
							_lowerCAmelCase  	=			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:
														_lowerCAmelCase  	=			roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

							print('Our RoBERTa config:'     ,  SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE						) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE						)
							model.eval()

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

							_lowerCAmelCase  	=			roberta_sent_encoder.layer_norm.weight
							_lowerCAmelCase  	=			roberta_sent_encoder.layer_norm.bias

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

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

														# self attention
														_lowerCAmelCase  	=			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  	=			roberta_layer.self_attn.q_proj.weight
														_lowerCAmelCase  	=			roberta_layer.self_attn.q_proj.bias
														_lowerCAmelCase  	=			roberta_layer.self_attn.k_proj.weight
														_lowerCAmelCase  	=			roberta_layer.self_attn.k_proj.bias
														_lowerCAmelCase  	=			roberta_layer.self_attn.v_proj.weight
														_lowerCAmelCase  	=			roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

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

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

							pathlib.Path(SCREAMING_SNAKE_CASE						).mkdir(parents=SCREAMING_SNAKE_CASE     ,  exist_ok=SCREAMING_SNAKE_CASE						)
							print(f"""Saving model to {pytorch_dump_folder_path}"""						)
							model.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
	_snake_case          =						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.'''
	)
	_snake_case          =						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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase							)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					field(default="image-classification"      ,				metadata={"include_in_asdict_even_if_is_default": True}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"image": Image()}							)
				SCREAMING_SNAKE_CASE_:							ClassVar[Features]							=					Features({"labels": ClassLabel}							)
				SCREAMING_SNAKE_CASE_:							str							=					"image"
				SCREAMING_SNAKE_CASE_:							str							=					"labels"


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			Optional[Any]  )		->  Dict:







											"""simple docstring"""




											if self.label_column not in features:
																		raise ValueError(F"""Column {self.label_column} is not present in features."""  )
											if not isinstance(features[self.label_column]				,     UpperCAmelCase_  ):
																		raise ValueError(F"""Column {self.label_column} is not a ClassLabel."""  )
											_lowerCAmelCase  	=			copy.deepcopy(self  )
											_lowerCAmelCase  	=			self.label_schema.copy()
											_lowerCAmelCase  	=			features[self.label_column]
											_lowerCAmelCase  	=			label_schema
											return task_template







				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict[str, str]:







											"""simple docstring"""




											return {
											    self.image_column: "image",
											    self.label_column: "labels",
											}






