



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
	)


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


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "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			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

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

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

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {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 						__lowerCamelCase				(  self    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs
