



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()
_snake_case		=  logging.get_logger(__name__)

_snake_case		=  "Hello world! cécé herlolip"






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    FairseqRobertaModel.from_pretrained(_lowerCamelCase  )
							roberta.eval()  # disable dropout
							_lowerCAmelCase     :							Optional[Any]    =    roberta.model.encoder.sentence_encoder
							_lowerCAmelCase     :							Dict    =    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     :							Union[str, Any]    =    roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

							print("Our RoBERTa config:"				, _lowerCamelCase  )

							_lowerCAmelCase     :							Tuple    =    XLMRobertaXLForSequenceClassification(_lowerCamelCase  ) if classification_head else XLMRobertaXLForMaskedLM(_lowerCamelCase  )
							model.eval()

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

							_lowerCAmelCase     :							List[Any]    =    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     :							BertLayer    =    model.roberta.encoder.layer[i]
														_lowerCAmelCase     :							TransformerSentenceEncoderLayer    =    roberta_sent_encoder.layers[i]

														_lowerCAmelCase     :							RobertaAttention    =    layer.attention
														_lowerCAmelCase     :							Optional[int]    =    roberta_layer.self_attn_layer_norm.weight
														_lowerCAmelCase     :							Tuple    =    roberta_layer.self_attn_layer_norm.bias

														# self attention
														_lowerCAmelCase     :							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)  )
														)

														_lowerCAmelCase     :							List[str]    =    roberta_layer.self_attn.q_proj.weight
														_lowerCAmelCase     :							Optional[Any]    =    roberta_layer.self_attn.q_proj.bias
														_lowerCAmelCase     :							Any    =    roberta_layer.self_attn.k_proj.weight
														_lowerCAmelCase     :							List[Any]    =    roberta_layer.self_attn.k_proj.bias
														_lowerCAmelCase     :							Optional[Any]    =    roberta_layer.self_attn.v_proj.weight
														_lowerCAmelCase     :							Optional[int]    =    roberta_layer.self_attn.v_proj.bias

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

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

														# intermediate
														_lowerCAmelCase     :							BertIntermediate    =    layer.intermediate
														assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
														_lowerCAmelCase     :							str    =    roberta_layer.fca.weight
														_lowerCAmelCase     :							int    =    roberta_layer.fca.bias

														# output
														_lowerCAmelCase     :							BertOutput    =    layer.output
														assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
														_lowerCAmelCase     :							int    =    roberta_layer.fca.weight
														_lowerCAmelCase     :							Tuple    =    roberta_layer.fca.bias
														# end of layer

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

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

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

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						str						=      """ctrl"""
    a__	:						Dict						=      ["""past_key_values"""]
    a__	:						Tuple						=      {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(	self , __lowercase=246_534 , __lowercase=256 , __lowercase=1_280 , __lowercase=8_192 , __lowercase=48 , __lowercase=16 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=1E-6 , __lowercase=0.02 , __lowercase=True , **__lowercase , )       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		vocab_size
        __UpperCamelCase     :Optional[Any]			  =		n_positions
        __UpperCamelCase     :Dict			  =		n_embd
        __UpperCamelCase     :Dict			  =		n_layer
        __UpperCamelCase     :List[Any]			  =		n_head
        __UpperCamelCase     :int			  =		dff
        __UpperCamelCase     :Union[str, Any]			  =		resid_pdrop
        __UpperCamelCase     :Optional[int]			  =		embd_pdrop
        __UpperCamelCase     :List[Any]			  =		layer_norm_epsilon
        __UpperCamelCase     :Dict			  =		initializer_range

        __UpperCamelCase     :Any			  =		use_cache

        super().__init__(**__lowercase)





