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__	    =				logging.get_logger(__name__)

lowerCamelCase__	    =				'Hello world! cécé herlolip'



def 							_lowerCamelCase(				__snake_case						,						__snake_case						,						__snake_case	)  ->   Union[str, Any]:
		__snake_case		       =     FairseqRobertaModel.from_pretrained(__snake_case	)
		roberta.eval()  # disable dropout
		__snake_case		       =     roberta.model.encoder.sentence_encoder
		__snake_case		       =     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:
				__snake_case		       =     roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
		print("Our BERT config:"						,						__snake_case	)

		__snake_case		       =     RobertaForSequenceClassification(__snake_case	) if classification_head else RobertaForMaskedLM(__snake_case	)
		model.eval()

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

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

				# self attention
				__snake_case		       =     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)	)
				)

				__snake_case		       =     roberta_layer.self_attn.q_proj.weight
				__snake_case		       =     roberta_layer.self_attn.q_proj.bias
				__snake_case		       =     roberta_layer.self_attn.k_proj.weight
				__snake_case		       =     roberta_layer.self_attn.k_proj.bias
				__snake_case		       =     roberta_layer.self_attn.v_proj.weight
				__snake_case		       =     roberta_layer.self_attn.v_proj.bias

				# self-attention output
				__snake_case		       =     layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				__snake_case		       =     roberta_layer.self_attn.out_proj.weight
				__snake_case		       =     roberta_layer.self_attn.out_proj.bias
				__snake_case		       =     roberta_layer.self_attn_layer_norm.weight
				__snake_case		       =     roberta_layer.self_attn_layer_norm.bias

				# intermediate
				__snake_case		       =     layer.intermediate
				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
				__snake_case		       =     roberta_layer.fca.weight
				__snake_case		       =     roberta_layer.fca.bias

				# output
				__snake_case		       =     layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				__snake_case		       =     roberta_layer.fca.weight
				__snake_case		       =     roberta_layer.fca.bias
				__snake_case		       =     roberta_layer.final_layer_norm.weight
				__snake_case		       =     roberta_layer.final_layer_norm.bias
				# end of layer

		if classification_head:
				__snake_case		       =     roberta.model.classification_heads["mnli"].dense.weight
				__snake_case		       =     roberta.model.classification_heads["mnli"].dense.bias
				__snake_case		       =     roberta.model.classification_heads["mnli"].out_proj.weight
				__snake_case		       =     roberta.model.classification_heads["mnli"].out_proj.bias
		else:
				# LM Head
				__snake_case		       =     roberta.model.encoder.lm_head.dense.weight
				__snake_case		       =     roberta.model.encoder.lm_head.dense.bias
				__snake_case		       =     roberta.model.encoder.lm_head.layer_norm.weight
				__snake_case		       =     roberta.model.encoder.lm_head.layer_norm.bias
				__snake_case		       =     roberta.model.encoder.lm_head.weight
				__snake_case		       =     roberta.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		__snake_case		       =     roberta.encode(__snake_case	).unsqueeze(0	)  # batch of size 1

		__snake_case		       =     model(__snake_case	)[0]
		if classification_head:
				__snake_case		       =     roberta.model.classification_heads["mnli"](roberta.extract_features(__snake_case	)	)
		else:
				__snake_case		       =     roberta.model(__snake_case	)[0]
		print(our_output.shape						,						their_output.shape	)
		__snake_case		       =     torch.max(torch.abs(our_output - their_output	)	).item()
		print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
		__snake_case		       =     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__":
		lowerCamelCase__	    =				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__	    =				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
from dataclasses import dataclass
from typing import Any




class 					UpperCamelCase	:
					__UpperCamelCase						=   ("""_front""", """_back""", """_len""")




					@dataclass
					class 					UpperCamelCase	:
										__UpperCamelCase						=   None
										__UpperCamelCase						=   None
										__UpperCamelCase						=   None




					class 					UpperCamelCase	:
										__UpperCamelCase						=   ("""_cur""",)




										def __init__( self :					Dict      ,_lowerCAmelCase :					Deque._Node | None						):


												"""simple docstring"""




												__snake_case		       =     cur




										def __iter__( self :					Dict						):


												"""simple docstring"""




												return self







										def 							UpperCamelCase_						( self :					Optional[Any]						):


												"""simple docstring"""




												if self._cur is None:
														# finished iterating
														raise StopIteration
												__snake_case		       =     self._cur.val
												__snake_case		       =     self._cur.next_node

												return val




					def __init__( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any] | None = None						):


							"""simple docstring"""




							__snake_case		       =     None
							__snake_case		       =     None
							__snake_case		       =     0

							if iterable is not None:
									# append every value to the deque
									for val in iterable:
											self.append(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any						):


							"""simple docstring"""




							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
							if self.is_empty():
									# front = back
									__snake_case		       =     __snake_case		       =     node
									__snake_case		       =     1
							else:
									# connect nodes
									__snake_case		       =     node
									__snake_case		       =     self._back
									__snake_case		       =     node  # assign new back to the new node

									self._len += 1

									# make sure there were no errors
									assert not self.is_empty(), "Error on appending value."




					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Any						):


							"""simple docstring"""




							__snake_case		       =     self._Node(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
							if self.is_empty():
									# front = back
									__snake_case		       =     __snake_case		       =     node
									__snake_case		       =     1
							else:
									# connect nodes
									__snake_case		       =     self._front
									__snake_case		       =     node
									__snake_case		       =     node  # assign new front to the new node

									self._len += 1

									# make sure there were no errors
									assert not self.is_empty(), "Error on appending value."




					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Iterable[Any]						):


							"""simple docstring"""




							for val in iterable:
									self.append(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					Iterable[Any]						):


							"""simple docstring"""




							for val in iterable:
									self.appendleft(_lowerCAmelCase						)




					def 							UpperCamelCase_						( self :					List[Any]						):


							"""simple docstring"""




							assert not self.is_empty(), "Deque is empty."

							__snake_case		       =     self._back
							__snake_case		       =     self._back.prev_node  # set new back
							# drop the last node - python will deallocate memory automatically
							__snake_case		       =     None

							self._len -= 1

							return topop.val




					def 							UpperCamelCase_						( self :					List[str]						):


							"""simple docstring"""




							assert not self.is_empty(), "Deque is empty."

							__snake_case		       =     self._front
							__snake_case		       =     self._front.next_node  # set new front and drop the first node
							__snake_case		       =     None

							self._len -= 1

							return topop.val




					def 							UpperCamelCase_						( self :					int						):


							"""simple docstring"""




							return self._front is None




					def __len__( self :					Tuple						):


							"""simple docstring"""




							return self._len




					def __eq__( self :					Union[str, Any]      ,_lowerCAmelCase :					object						):


							"""simple docstring"""




							if not isinstance(_lowerCAmelCase      ,_lowerCAmelCase						):
									return NotImplemented

							__snake_case		       =     self._front
							__snake_case		       =     other._front

							# if the length of the dequeues are not the same, they are not equal
							if len(self						) != len(_lowerCAmelCase						):
									return False

							while me is not None and oth is not None:
									# compare every value
									if me.val != oth.val:
											return False
									__snake_case		       =     me.next_node
									__snake_case		       =     oth.next_node

							return True




					def __iter__( self :					Tuple						):


							"""simple docstring"""




							return Deque._Iterator(self._front						)







					def __repr__( self :					List[Any]						):


							"""simple docstring"""




							__snake_case		       =     []
							__snake_case		       =     self._front
							while aux is not None:
									# append the values in a list to display
									values_list.append(aux.val						)
									__snake_case		       =     aux.next_node

							return F"""[{", ".join(repr(_lowerCAmelCase						) for val in values_list						)}]"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()





