'''simple docstring'''





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

SCREAMING_SNAKE_CASE_:					str  =typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
SCREAMING_SNAKE_CASE_:					int  =typing.Union[np.floataa, int, float]  # noqa: UP007





def      lowerCAmelCase_    ( snake_case_	:			Vector ,   snake_case_	:			Vector      )   ->				VectorOut:


     '''simple docstring'''




     return np.sqrt(np.sum((np.asarray(_lowercase      ) - np.asarray(_lowercase      )) ** 2      )      )





def      lowerCAmelCase_    ( snake_case_	:			Vector ,   snake_case_	:			Vector      )   ->				VectorOut:


     '''simple docstring'''




     return sum((va - va) ** 2 for va, va in zip(_lowercase ,   _lowercase      )      ) ** (1 / 2)


if __name__ == "__main__":





    def      lowerCAmelCase_    ( )   ->				None:


         '''simple docstring'''




         from timeit import timeit

         print("Without Numpy"      )
         print(
             timeit(
                 "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])" ,   number=1_00_00 ,   globals=globals() ,   )      )
         print("With Numpy"      )
         print(
             timeit(
                 "euclidean_distance([1, 2, 3], [4, 5, 6])" ,   number=1_00_00 ,   globals=globals() ,   )      )






    benchmark()




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

SCREAMING_SNAKE_CASE_:					Tuple  ='Hello world! cécé herlolip'





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			str ,   snake_case_	:			bool      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       FairseqRobertaModel.from_pretrained(snake_case_      )
     roberta.eval()  # disable dropout
     UpperCAmelCase_	   =       roberta.model.encoder.sentence_encoder
     UpperCAmelCase_	   =       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=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 RoBERTa config:" ,   snake_case_      )

     UpperCAmelCase_	   =       XLMRobertaXLForSequenceClassification(snake_case_      ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_      )
     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.layer_norm.weight
     UpperCAmelCase_	   =       roberta_sent_encoder.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]

          UpperCAmelCase_	   =       layer.attention
          UpperCAmelCase_	   =       roberta_layer.self_attn_layer_norm.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn_layer_norm.bias

          # 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

          # this one is final layer norm
          UpperCAmelCase_	   =       roberta_layer.final_layer_norm.weight
          UpperCAmelCase_	   =       roberta_layer.final_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
          # 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(snake_case_      ).unsqueeze(0      )  # batch of size 1

     UpperCAmelCase_	   =       model(snake_case_      )[0]
     if classification_head:
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case_      )      )
     else:
          UpperCAmelCase_	   =       roberta.model(snake_case_      )[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(snake_case_ ,   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(snake_case_      ).mkdir(parents=snake_case_ ,   exist_ok=snake_case_      )
     print(f"""Saving model to {pytorch_dump_folder_path}"""      )
     model.save_pretrained(snake_case_      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[int]  =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.'
    )
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )




