
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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
							raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
lowerCamelCase_  :       int					      =     logging.get_logger(__name__)

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







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[str]:
				UpperCamelCase_:							Tuple		=  FairseqRobertaModel.from_pretrained(lowerCamelCase  )
				roberta.eval()  # disable dropout
				UpperCamelCase_:							Union[str, Any]		=  roberta.model.encoder.sentence_encoder
				UpperCamelCase_:							Dict		=  RobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   ,			hidden_size=roberta.args.encoder_embed_dim   ,			num_hidden_layers=roberta.args.encoder_layers   ,			num_attention_heads=roberta.args.encoder_attention_heads   ,			intermediate_size=roberta.args.encoder_ffn_embed_dim   ,			max_position_embeddings=5_14   ,			type_vocab_size=1   ,			layer_norm_eps=1E-5   ,			)
				if classification_head:
								UpperCamelCase_:							Dict		=  roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
				print("""Our BERT config:"""   ,			lowerCamelCase  )

				UpperCamelCase_:							Dict		=  RobertaForSequenceClassification(lowerCamelCase  ) if classification_head else RobertaForMaskedLM(lowerCamelCase  )
				model.eval()

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

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

								# self attention
								UpperCamelCase_:							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)  )
								)

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

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

								# intermediate
								UpperCamelCase_:							BertIntermediate		=  layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								UpperCamelCase_:							Optional[int]		=  roberta_layer.fca.weight
								UpperCamelCase_:							Union[str, Any]		=  roberta_layer.fca.bias

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

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

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

				UpperCamelCase_:							Any		=  model(lowerCamelCase  )[0]
				if classification_head:
								UpperCamelCase_:							Optional[int]		=  roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase  )  )
				else:
								UpperCamelCase_:							Union[str, Any]		=  roberta.model(lowerCamelCase  )[0]
				print(our_output.shape   ,			their_output.shape  )
				UpperCamelCase_:							Any		=  torch.max(torch.abs(our_output - their_output  )  ).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
				UpperCamelCase_:							Optional[Any]		=  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__":
							lowerCamelCase_  :       int					      =     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."""
							)
							lowerCamelCase_  :       Optional[Any]					      =     parser.parse_args()
							convert_roberta_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)








from math import cos, sin







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_:							int		=  float(lowerCamelCase  )
				return [[scaling_factor * int(x == y  ) for x in range(2  )] for y in range(2  )]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				return [[c, -s], [s, c]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Any		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  c * s
				return [[c * c, cs], [cs, s * s]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							List[str]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							int		=  c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")





print(F"""{reflection(45) = }""")






