
"""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()
UpperCAmelCase__   :					Tuple												=				logging.get_logger(__name__)

UpperCAmelCase__   :					Optional[int]												=				'Hello world! cécé herlolip'

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     FairseqRobertaModel.from_pretrained(__SCREAMING_SNAKE_CASE		)
							roberta.eval()  # disable dropout
							SCREAMING_SNAKE_CASE__		: Tuple     =     roberta.model.encoder.sentence_encoder
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     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:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

							print("""Our RoBERTa config:"""     ,__SCREAMING_SNAKE_CASE		)

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     XLMRobertaXLForSequenceClassification(__SCREAMING_SNAKE_CASE		) if classification_head else XLMRobertaXLForMaskedLM(__SCREAMING_SNAKE_CASE		)
							model.eval()

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

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     roberta_sent_encoder.layer_norm.weight
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     roberta_sent_encoder.layer_norm.bias

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

														SCREAMING_SNAKE_CASE__		: str     =     layer.attention
														SCREAMING_SNAKE_CASE__		: List[str]     =     roberta_layer.self_attn_layer_norm.weight
														SCREAMING_SNAKE_CASE__		: List[Any]     =     roberta_layer.self_attn_layer_norm.bias

														# self attention
														SCREAMING_SNAKE_CASE__		: List[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)		)
														)

														SCREAMING_SNAKE_CASE__		: int     =     roberta_layer.self_attn.q_proj.weight
														SCREAMING_SNAKE_CASE__		: Any     =     roberta_layer.self_attn.q_proj.bias
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     roberta_layer.self_attn.k_proj.weight
														SCREAMING_SNAKE_CASE__		: Any     =     roberta_layer.self_attn.k_proj.bias
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     roberta_layer.self_attn.v_proj.weight
														SCREAMING_SNAKE_CASE__		: int     =     roberta_layer.self_attn.v_proj.bias

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

														# this one is final layer norm
														SCREAMING_SNAKE_CASE__		: Dict     =     roberta_layer.final_layer_norm.weight
														SCREAMING_SNAKE_CASE__		: str     =     roberta_layer.final_layer_norm.bias

														# intermediate
														SCREAMING_SNAKE_CASE__		: Any     =     layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__		: Tuple     =     roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__		: str     =     roberta_layer.fca.bias

														# output
														SCREAMING_SNAKE_CASE__		: Tuple     =     layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														SCREAMING_SNAKE_CASE__		: Any     =     roberta_layer.fca.weight
														SCREAMING_SNAKE_CASE__		: List[Any]     =     roberta_layer.fca.bias
														# end of layer

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

							# Let's check that we get the same results.
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     roberta.encode(__SCREAMING_SNAKE_CASE		).unsqueeze(0		)  # batch of size 1

							SCREAMING_SNAKE_CASE__		: List[str]     =     model(__SCREAMING_SNAKE_CASE		)[0]
							if classification_head:
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     roberta.model.classification_heads["""mnli"""](roberta.extract_features(__SCREAMING_SNAKE_CASE		)		)
							else:
														SCREAMING_SNAKE_CASE__		: str     =     roberta.model(__SCREAMING_SNAKE_CASE		)[0]
							print(our_output.shape     ,their_output.shape		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     torch.max(torch.abs(our_output - their_output		)		).item()
							print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
							SCREAMING_SNAKE_CASE__		: Any     =     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__":
							UpperCAmelCase__   :					Optional[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.'
							)
							UpperCAmelCase__   :					Any												=				parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)






"""simple docstring"""




def lowercase_						(     _snake_case     ,_snake_case		):
							return int((input_a, input_a).count(1		) != 0		)

def lowercase_						(     ):
							assert or_gate(0     ,0		) == 0
							assert or_gate(0     ,1		) == 1
							assert or_gate(1     ,0		) == 1
							assert or_gate(1     ,1		) == 1


if __name__ == "__main__":
					print(or_gate(0, 1))
					print(or_gate(1, 0))
					print(or_gate(0, 0))
					print(or_gate(1, 1))




