





'''simple docstring'''







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()
snake_case_  :							List[str]            =	logging.get_logger(__name__)

snake_case_  :							Optional[Any]            =	"Hello world! cécé herlolip"





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      bool )			->						List[str]:
						UpperCAmelCase_      :   Dict      =	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
						roberta.eval()  # disable dropout
						UpperCAmelCase_      :   Optional[Any]      =	roberta.model.encoder.sentence_encoder
						UpperCAmelCase_      :   Optional[int]      =	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:
												UpperCAmelCase_      :   Optional[Any]      =	roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
						print('''Our BERT config:''',      SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Optional[Any]      =	RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__ )
						model.eval()

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

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

												# self attention
												UpperCAmelCase_      :   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) )
												)

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

												# self-attention output
												UpperCAmelCase_      :   BertSelfOutput      =	layer.attention.output
												assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn.out_proj.weight
												UpperCAmelCase_      :   Dict      =	roberta_layer.self_attn.out_proj.bias
												UpperCAmelCase_      :   Tuple      =	roberta_layer.self_attn_layer_norm.weight
												UpperCAmelCase_      :   List[Any]      =	roberta_layer.self_attn_layer_norm.bias

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

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

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

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

						UpperCAmelCase_      :   Optional[int]      =	model(SCREAMING_SNAKE_CASE__ )[0]
						if classification_head:
												UpperCAmelCase_      :   List[Any]      =	roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE__ ) )
						else:
												UpperCAmelCase_      :   Tuple      =	roberta.model(SCREAMING_SNAKE_CASE__ )[0]
						print(our_output.shape,      their_output.shape )
						UpperCAmelCase_      :   Tuple      =	torch.max(torch.abs(our_output - their_output ) ).item()
						print(F"""max_absolute_diff = {max_absolute_diff}""" )  # ~ 1e-7
						UpperCAmelCase_      :   List[Any]      =	torch.allclose(SCREAMING_SNAKE_CASE__,      SCREAMING_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(SCREAMING_SNAKE_CASE__ ).mkdir(parents=SCREAMING_SNAKE_CASE__,      exist_ok=SCREAMING_SNAKE_CASE__ )
						print(F"""Saving model to {pytorch_dump_folder_path}""" )
						model.save_pretrained(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
					snake_case_  :							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."
					)
					snake_case_  :							Dict            =	parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)









"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						List[Any]     =		'deberta'


     def __init__(   self     ,					__UpperCAmelCase=50265     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=0.02     ,					__UpperCAmelCase=1e-7     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=-1     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase="gelu"     ,					**__UpperCAmelCase     ,					)							->				Any:
          super().__init__(**__UpperCAmelCase				)

          _a     						=					hidden_size
          _a     						=					num_hidden_layers
          _a     						=					num_attention_heads
          _a     						=					intermediate_size
          _a     						=					hidden_act
          _a     						=					hidden_dropout_prob
          _a     						=					attention_probs_dropout_prob
          _a     						=					max_position_embeddings
          _a     						=					type_vocab_size
          _a     						=					initializer_range
          _a     						=					relative_attention
          _a     						=					max_relative_positions
          _a     						=					pad_token_id
          _a     						=					position_biased_input

          # Backwards compatibility
          if type(__UpperCAmelCase				) == str:
               _a     						=					[x.strip() for x in pos_att_type.lower().split('''|'''				)]

          _a     						=					pos_att_type
          _a     						=					vocab_size
          _a     						=					layer_norm_eps

          _a     						=					kwargs.get('''pooler_hidden_size'''     ,					__UpperCAmelCase				)
          _a     						=					pooler_dropout
          _a     						=					pooler_hidden_act







class      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _a     						=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
               _a     						=					{0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]				)
          else:
               return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          return 12





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = -1     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = 40     ,					__UpperCAmelCase = None     ,					)							->				Mapping[str, Any]:
          _a     						=					super().generate_dummy_inputs(preprocessor=__UpperCAmelCase     ,					framework=__UpperCAmelCase				)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
          return dummy_inputs