



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()
__UpperCAmelCase    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  '''Hello world! cécé herlolip'''





def     __lowerCamelCase							(   __magic_name__						:	str  ,			__magic_name__						:	str  ,			__magic_name__						:	bool    ):
							a__:							Union[str, Any]									=FairseqRobertaModel.from_pretrained(__lowerCamelCase    )
							roberta.eval()  # disable dropout
							a__:							Any									=roberta.model.encoder.sentence_encoder
							a__:							str									=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=514  ,			type_vocab_size=1  ,			layer_norm_eps=1e-5  ,			)
							if classification_head:
														a__:							Optional[Any]									=roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
							print("Our BERT config:"  ,			__lowerCamelCase    )

							a__:							List[str]									=RobertaForSequenceClassification(__lowerCamelCase    ) if classification_head else RobertaForMaskedLM(__lowerCamelCase    )
							model.eval()

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

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

														# self attention
														a__:							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)    )
														)

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

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

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

														# output
														a__:							BertOutput									=layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														a__:							str									=roberta_layer.fca.weight
														a__:							List[str]									=roberta_layer.fca.bias
														a__:							Tuple									=roberta_layer.final_layer_norm.weight
														a__:							str									=roberta_layer.final_layer_norm.bias
														# end of layer

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

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

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










from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_lowerCamelCase         =	TypeVar("""T""", bound=bool)

class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     T							=						value
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None





     @property
     def 						__snake_case     (							self			:				Dict					)->T:
      return self._value






     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				SkewNode[T] | None  ,	UpperCamelCase			:				SkewNode[T] | None					)->SkewNode[T] | None:
      if not roota:
       return roota

      if not roota:
       return roota

      if roota.value > roota.value:
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						roota, roota

      __SCREAMING_SNAKE_CASE							:     int							=						roota
      __SCREAMING_SNAKE_CASE							:     str							=						roota.right
      __SCREAMING_SNAKE_CASE							:     int							=						roota.left
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						SkewNode.merge(UpperCamelCase  ,	UpperCamelCase					)

      return result






class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				Iterable[T] | None = ()					)->None:
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      if data:
       for item in data:
        self.insert(UpperCamelCase					)





     def __bool__(							self			:				str					)->bool:
      return self._root is not None





     def __iter__(							self			:				Any					)->Iterator[T]:
      __SCREAMING_SNAKE_CASE							:     list[Any]							=						[]
      while self:
       result.append(self.pop()					)

      # Pushing items back to the heap not to clear it.
      for item in result:
       self.insert(UpperCamelCase					)

      return iter(UpperCamelCase					)





     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     str							=						SkewNode.merge(self._root  ,	SkewNode(UpperCamelCase					)					)





     def 						__snake_case     (							self			:				List[Any]					)->T | None:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.top()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						(
          SkewNode.merge(self._root.left  ,	self._root.right					) if self._root else None
      )

      return result





     def 						__snake_case     (							self			:				Optional[int]					)->T:
      if not self._root:
       raise IndexError("Can't get top element for the empty heap."					)
      return self._root.value






     def 						__snake_case     (							self			:				Dict					)->None:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						None


if __name__ == "__main__":
      import doctest

      doctest.testmod()






