


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			:  List[Any]     =logging.get_logger(__name__)

_UpperCAmelCase			:  List[Any]     ="""Hello world! cécé herlolip"""



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				FairseqRobertaModel.from_pretrained(A__ )
				roberta.eval()  # disable dropout
				lowerCAmelCase_ :      List[Any]    				=				roberta.model.encoder.sentence_encoder
				lowerCAmelCase_ :      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=514 ,   type_vocab_size=1 ,   layer_norm_eps=1e-5 ,   )
				if classification_head:
								lowerCAmelCase_ :      Tuple    				=				roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

				lowerCAmelCase_ :      Union[str, Any]    				=				XLMRobertaXLForSequenceClassification(A__ ) if classification_head else XLMRobertaXLForMaskedLM(A__ )
				model.eval()

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

				lowerCAmelCase_ :      str    				=				roberta_sent_encoder.layer_norm.weight
				lowerCAmelCase_ :      Dict    				=				roberta_sent_encoder.layer_norm.bias

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

								lowerCAmelCase_ :      Any    				=				layer.attention
								lowerCAmelCase_ :      Any    				=				roberta_layer.self_attn_layer_norm.weight
								lowerCAmelCase_ :      str    				=				roberta_layer.self_attn_layer_norm.bias

								# self attention
								lowerCAmelCase_ :      Tuple    				=				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_ :      Tuple    				=				roberta_layer.self_attn.q_proj.weight
								lowerCAmelCase_ :      Optional[Any]    				=				roberta_layer.self_attn.q_proj.bias
								lowerCAmelCase_ :      Optional[Any]    				=				roberta_layer.self_attn.k_proj.weight
								lowerCAmelCase_ :      Optional[Any]    				=				roberta_layer.self_attn.k_proj.bias
								lowerCAmelCase_ :      List[Any]    				=				roberta_layer.self_attn.v_proj.weight
								lowerCAmelCase_ :      str    				=				roberta_layer.self_attn.v_proj.bias

								# self-attention output
								lowerCAmelCase_ :      Any    				=				layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								lowerCAmelCase_ :      int    				=				roberta_layer.self_attn.out_proj.weight
								lowerCAmelCase_ :      Dict    				=				roberta_layer.self_attn.out_proj.bias

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

								# intermediate
								lowerCAmelCase_ :      List[str]    				=				layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								lowerCAmelCase_ :      Optional[Any]    				=				roberta_layer.fca.weight
								lowerCAmelCase_ :      List[str]    				=				roberta_layer.fca.bias

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

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

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

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

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


if __name__ == "__main__":
						_UpperCAmelCase			:  Tuple     =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			:  Union[str, Any]     =parser.parse_args()
						convert_xlm_roberta_xl_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)



from __future__ import annotations



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   )-> tuple:
				if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
								raise ValueError('''You cannot supply more or less than 2 values''' )
				elif electron_conc < 0:
								raise ValueError('''Electron concentration cannot be negative in a semiconductor''' )
				elif hole_conc < 0:
								raise ValueError('''Hole concentration cannot be negative in a semiconductor''' )
				elif intrinsic_conc < 0:
								raise ValueError(
								    '''Intrinsic concentration cannot be negative in a semiconductor''' )
				elif electron_conc == 0:
								return (
								    "electron_conc",
								    intrinsic_conc**2 / hole_conc,
								)
				elif hole_conc == 0:
								return (
								    "hole_conc",
								    intrinsic_conc**2 / electron_conc,
								)
				elif intrinsic_conc == 0:
								return (
								    "intrinsic_conc",
								    (electron_conc * hole_conc) ** 0.5,
								)
				else:
								return (-1, -1)


if __name__ == "__main__":
						import doctest

						doctest.testmod()