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()
SCREAMING_SNAKE_CASE__		:					Tuple						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					List[str]						=					"""Hello world! cécé herlolip"""







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str     ,  lowercase__  :			bool ):







					'''simple docstring'''


					SCREAMING_SNAKE_CASE__				:				str			=		FairseqRobertaModel.from_pretrained(snake_case_ )
					roberta.eval()  # disable dropout
					SCREAMING_SNAKE_CASE__				:				Tuple			=		roberta.model.encoder.sentence_encoder
					SCREAMING_SNAKE_CASE__				:				str			=		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:
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

					print('Our RoBERTa config:'     ,  snake_case_ )

					SCREAMING_SNAKE_CASE__				:				List[str]			=		XLMRobertaXLForSequenceClassification(snake_case_ ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_ )
					model.eval()

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

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

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

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

										# self attention
										SCREAMING_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) )
										)

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

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

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

										# intermediate
										SCREAMING_SNAKE_CASE__				:				BertIntermediate			=		layer.intermediate
										assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
										SCREAMING_SNAKE_CASE__				:				Dict			=		roberta_layer.fca.weight
										SCREAMING_SNAKE_CASE__				:				Any			=		roberta_layer.fca.bias

										# output
										SCREAMING_SNAKE_CASE__				:				BertOutput			=		layer.output
										assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
										SCREAMING_SNAKE_CASE__				:				Dict			=		roberta_layer.fca.weight
										SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		roberta_layer.fca.bias
										# end of layer

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

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

					SCREAMING_SNAKE_CASE__				:				Any			=		model(snake_case_ )[0]
					if classification_head:
										SCREAMING_SNAKE_CASE__				:				Tuple			=		roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case_ ) )
					else:
										SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		roberta.model(snake_case_ )[0]
					print(our_output.shape     ,  their_output.shape )
					SCREAMING_SNAKE_CASE__				:				Dict			=		torch.max(torch.abs(our_output - their_output ) ).item()
					print(f'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
					SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.allclose(snake_case_     ,  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(snake_case_ ).mkdir(parents=snake_case_     ,  exist_ok=snake_case_ )
					print(f'''Saving model to {pytorch_dump_folder_path}''' )
					model.save_pretrained(snake_case_ )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE__		:					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."
						)
						SCREAMING_SNAKE_CASE__		:					List[Any]						=					parser.parse_args()
						convert_xlm_roberta_xl_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)




def     _a				(      lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     return int((input_a, input_a).count(0 ) != 0 )







def     _a				(      ):







     '''simple docstring'''


     assert nand_gate(0     ,  0 ) == 1
     assert nand_gate(0     ,  1 ) == 1
     assert nand_gate(1     ,  0 ) == 1
     assert nand_gate(1     ,  1 ) == 0


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



