



'''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


A_				:					List[Any]							=      logging.get_logger(__name__)

A_				:					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 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    """deberta"""







 def __init__(				self    ,a_=50_265    ,a_=768    ,a_=12    ,a_=12    ,a_=3_072    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=512    ,a_=0    ,a_=0.02    ,a_=1E-7    ,a_=False    ,a_=-1    ,a_=0    ,a_=True    ,a_=None    ,a_=0    ,a_="gelu"    ,**a_    ,)						->  Optional[Any]:
        super().__init__(**a_				)

        _UpperCAmelCase    :		Tuple     	=					hidden_size
        _UpperCAmelCase    :		List[str]     	=					num_hidden_layers
        _UpperCAmelCase    :		Dict     	=					num_attention_heads
        _UpperCAmelCase    :		Optional[Any]     	=					intermediate_size
        _UpperCAmelCase    :		Any     	=					hidden_act
        _UpperCAmelCase    :		List[str]     	=					hidden_dropout_prob
        _UpperCAmelCase    :		str     	=					attention_probs_dropout_prob
        _UpperCAmelCase    :		List[str]     	=					max_position_embeddings
        _UpperCAmelCase    :		List[str]     	=					type_vocab_size
        _UpperCAmelCase    :		Optional[Any]     	=					initializer_range
        _UpperCAmelCase    :		Dict     	=					relative_attention
        _UpperCAmelCase    :		int     	=					max_relative_positions
        _UpperCAmelCase    :		int     	=					pad_token_id
        _UpperCAmelCase    :		Any     	=					position_biased_input

        # Backwards compatibility
        if type(a_				) == str:
               _UpperCAmelCase    :		Optional[Any]     	=					[x.strip() for x in pos_att_type.lower().split("""|"""				)]

        _UpperCAmelCase    :		Optional[Any]     	=					pos_att_type
        _UpperCAmelCase    :		str     	=					vocab_size
        _UpperCAmelCase    :		List[Any]     	=					layer_norm_eps

        _UpperCAmelCase    :		Dict     	=					kwargs.get("""pooler_hidden_size"""    ,a_				)
        _UpperCAmelCase    :		List[str]     	=					pooler_dropout
        _UpperCAmelCase    :		Any     	=					pooler_hidden_act







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 @property
 def _snake_case					(				self				)						->  Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
               _UpperCAmelCase    :		str     	=					{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
               _UpperCAmelCase    :		Optional[int]     	=					{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 _snake_case					(				self				)						->  int:
        return 12







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










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()
A_        =  logging.get_logger(__name__)

A_        =  "Hello world! cécé herlolip"





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

					lowerCamelCase__      =							RobertaForSequenceClassification(a) if classification_head else RobertaForMaskedLM(a)
					model.eval()

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

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

										# self attention
										lowerCamelCase__      =							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__      =							roberta_layer.self_attn.q_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.q_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn.k_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.k_proj.bias
										lowerCamelCase__      =							roberta_layer.self_attn.v_proj.weight
										lowerCamelCase__      =							roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

					lowerCamelCase__      =							model(a)[0]
					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"](roberta.extract_features(a))
					else:
										lowerCamelCase__      =							roberta.model(a)[0]
					print(our_output.shape,  their_output.shape)
					lowerCamelCase__      =							torch.max(torch.abs(our_output - their_output)).item()
					print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
					lowerCamelCase__      =							torch.allclose(a,  a,  atol=1E-3)
					print("Do both models output the same tensors?",  "🔥" if success else "💩")
					if not success:
										raise Exception("Something went wRoNg")

					pathlib.Path(a).mkdir(parents=a,  exist_ok=a)
					print(f"Saving model to {pytorch_dump_folder_path}")
					model.save_pretrained(a)


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

