



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 :				Optional[int]							=logging.get_logger(__name__)

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



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase):
					UpperCamelCase_			= FairseqRobertaModel.from_pretrained(_lowerCAmelCase)
					roberta.eval()  # disable dropout
					UpperCamelCase_			= roberta.model.encoder.sentence_encoder
					UpperCamelCase_			= 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_			= roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:" ,						_lowerCAmelCase)

					UpperCamelCase_			= RobertaForSequenceClassification(_lowerCAmelCase) if classification_head else RobertaForMaskedLM(_lowerCAmelCase)
					model.eval()

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

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

										# self attention
										UpperCamelCase_			= 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_			= roberta_layer.self_attn.q_proj.weight
										UpperCamelCase_			= roberta_layer.self_attn.q_proj.bias
										UpperCamelCase_			= roberta_layer.self_attn.k_proj.weight
										UpperCamelCase_			= roberta_layer.self_attn.k_proj.bias
										UpperCamelCase_			= roberta_layer.self_attn.v_proj.weight
										UpperCamelCase_			= roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

					UpperCamelCase_			= model(_lowerCAmelCase)[0]
					if classification_head:
										UpperCamelCase_			= roberta.model.classification_heads["mnli"](roberta.extract_features(_lowerCAmelCase))
					else:
										UpperCamelCase_			= roberta.model(_lowerCAmelCase)[0]
					print(our_output.shape ,						their_output.shape)
					UpperCamelCase_			= torch.max(torch.abs(our_output - their_output)).item()
					print(f"""max_absolute_diff = {max_absolute_diff}""")  # ~ 1e-7
					UpperCamelCase_			= 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 :				List[Any]							=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 :				List[Any]							=parser.parse_args()
	convert_roberta_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)



'''simple docstring'''


from __future__ import annotations

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

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



class        lowercase    (	Generic[T]			):


							def __init__(      self     ,      _snake_case)				->					None:
								UpperCAmelCase_		:   T													=       value
								UpperCAmelCase_		:   SkewNode[T] | None													=       None
								UpperCAmelCase_		:   SkewNode[T] | None													=       None


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





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

								if not roota:
									return roota

								if roota.value > roota.value:
									UpperCAmelCase_						,     UpperCAmelCase_		:   Union[str, Any]													=       roota, roota

								UpperCAmelCase_		:   Dict													=       roota
								UpperCAmelCase_		:   Dict													=       roota.right
								UpperCAmelCase_		:   Optional[int]													=       roota.left
								UpperCAmelCase_		:   Dict													=       SkewNode.merge(_snake_case     ,      _snake_case)

								return result



class        lowercase    (	Generic[T]			):


							def __init__(      self     ,      _snake_case = ())				->					None:
								UpperCAmelCase_		:   SkewNode[T] | None													=       None
								if data:
									for item in data:
										self.insert(_snake_case)


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


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

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

								return iter(_snake_case)


							def     _snake_case				(      self     ,      _snake_case)				->					None:
								UpperCAmelCase_		:   Any													=       SkewNode.merge(self._root     ,      SkewNode(_snake_case))


							def     _snake_case				(      self)				->					T | None:
								UpperCAmelCase_		:   Union[str, Any]													=       self.top()
								UpperCAmelCase_		:   Tuple													=       (
								    SkewNode.merge(self._root.left     ,      self._root.right) if self._root else None
								)

								return result


							def     _snake_case				(      self)				->					T:
								if not self._root:
									raise IndexError('Can\'t get top element for the empty heap.')
								return self._root.value





							def     _snake_case				(      self)				->					None:
								UpperCAmelCase_		:   Union[str, Any]													=       None


if __name__ == "__main__":
						import doctest

						doctest.testmod()






