



"""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 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()
_lowerCAmelCase    : Any							=							logging.get_logger(__name__)

_lowerCAmelCase    : Any							=							"Hello world! cécé herlolip"

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						bool    )  ->       List[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   FairseqRobertaModel.from_pretrained(lowercase__    )
     roberta.eval()  # disable dropout
     _UpperCAmelCase      :     Union[str, Any]        =   roberta.model.encoder.sentence_encoder
     _UpperCAmelCase      :     Any        =   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:
          _UpperCAmelCase      :     Optional[Any]        =   roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("Our RoBERTa config:"						,  lowercase__    )

     _UpperCAmelCase      :     Any        =   XLMRobertaXLForSequenceClassification(lowercase__    ) if classification_head else XLMRobertaXLForMaskedLM(lowercase__    )
     model.eval()

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

     _UpperCAmelCase      :     Optional[int]        =   roberta_sent_encoder.layer_norm.weight
     _UpperCAmelCase      :     List[Any]        =   roberta_sent_encoder.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]

          _UpperCAmelCase      :     RobertaAttention        =   layer.attention
          _UpperCAmelCase      :     List[Any]        =   roberta_layer.self_attn_layer_norm.weight
          _UpperCAmelCase      :     List[Any]        =   roberta_layer.self_attn_layer_norm.bias

          # 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      :     Optional[int]        =   roberta_layer.self_attn.q_proj.weight
          _UpperCAmelCase      :     Optional[int]        =   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      :     List[str]        =   roberta_layer.self_attn.v_proj.weight
          _UpperCAmelCase      :     int        =   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      :     List[str]        =   roberta_layer.self_attn.out_proj.weight
          _UpperCAmelCase      :     Any        =   roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          _UpperCAmelCase      :     Tuple        =   roberta_layer.final_layer_norm.weight
          _UpperCAmelCase      :     Dict        =   roberta_layer.final_layer_norm.bias

          # intermediate
          _UpperCAmelCase      :     BertIntermediate        =   layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          _UpperCAmelCase      :     List[str]        =   roberta_layer.fca.weight
          _UpperCAmelCase      :     Optional[int]        =   roberta_layer.fca.bias

          # output
          _UpperCAmelCase      :     BertOutput        =   layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          _UpperCAmelCase      :     Dict        =   roberta_layer.fca.weight
          _UpperCAmelCase      :     Any        =   roberta_layer.fca.bias
          # end of layer

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

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

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

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


if __name__ == "__main__":
 _lowerCAmelCase    : Optional[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."
 )
 _lowerCAmelCase    : Optional[int]							=							parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_lowerCAmelCase    : List[str]							=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase    : int							=							{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCAmelCase    : List[Any]							=							{
    "google/electra-small-generator": 5_12,
    "google/electra-base-generator": 5_12,
    "google/electra-large-generator": 5_12,
    "google/electra-small-discriminator": 5_12,
    "google/electra-base-discriminator": 5_12,
    "google/electra-large-discriminator": 5_12,
}

_lowerCAmelCase    : Optional[Any]							=							{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE						:       Any								=			PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_INIT_CONFIGURATION
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE						:       Tuple								=			ElectraTokenizer



      def __init__(					self					:     Dict					,     A					:     Dict=None					,     A					:     Optional[int]=None					,     A					:     Dict=True					,     A					:     Optional[Any]="[UNK]"					,     A					:     Any="[SEP]"					,     A					:     str="[PAD]"					,     A					:     Tuple="[CLS]"					,     A					:     Optional[Any]="[MASK]"					,     A					:     Any=True					,     A					:     Tuple=None					,     **A					:     Any					,     ):
           super().__init__(
               A					,     tokenizer_file=A					,     do_lower_case=A					,     unk_token=A					,     sep_token=A					,     pad_token=A					,     cls_token=A					,     mask_token=A					,     tokenize_chinese_chars=A					,     strip_accents=A					,     **A					,     )

           _UpperCAmelCase      :     List[str]        =   json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
           if (
               normalizer_state.get("lowercase"					,     A						) != do_lower_case
               or normalizer_state.get("strip_accents"					,     A						) != strip_accents
               or normalizer_state.get("handle_chinese_chars"					,     A						) != tokenize_chinese_chars
           ):
                _UpperCAmelCase      :     Union[str, Any]        =   getattr(A					,     normalizer_state.pop("type"						)						)
                _UpperCAmelCase      :     Dict        =   do_lower_case
                _UpperCAmelCase      :     Optional[int]        =   strip_accents
                _UpperCAmelCase      :     Any        =   tokenize_chinese_chars
                _UpperCAmelCase      :     Optional[Any]        =   normalizer_class(**A						)

           _UpperCAmelCase      :     int        =   do_lower_case



      def 						snake_case_ (					self					:     Tuple					,     A					:     str					,     A					:     int=None						):
           _UpperCAmelCase      :     Dict        =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

           if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

           return output



      def 						snake_case_ (					self					:     Any					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     Any        =   [self.sep_token_id]
           _UpperCAmelCase      :     Dict        =   [self.cls_token_id]
           if token_ids_a is None:
                return len(cls + token_ids_a + sep						) * [0]
           return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]



      def 						snake_case_ (					self					:     Any					,     A					:     str					,     A					:     Optional[str] = None						):
           _UpperCAmelCase      :     List[Any]        =   self._tokenizer.model.save(A					,     name=A						)
           return tuple(A						)
