


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			=							logging.get_logger(__name__)

__lowerCAmelCase			=							'''Hello world! cécé herlolip'''




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    List[str]:
       lowercase__:       List[Any]        =       FairseqRobertaModel.from_pretrained(snake_case					)
       roberta.eval()  # disable dropout
       lowercase__:       Dict        =       roberta.model.encoder.sentence_encoder
       lowercase__:       Tuple        =       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:
              lowercase__:       Tuple        =       roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

       print('Our RoBERTa config:'      ,							snake_case					)

       lowercase__:       Dict        =       XLMRobertaXLForSequenceClassification(snake_case					) if classification_head else XLMRobertaXLForMaskedLM(snake_case					)
       model.eval()

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

       lowercase__:       List[Any]        =       roberta_sent_encoder.layer_norm.weight
       lowercase__:       Optional[Any]        =       roberta_sent_encoder.layer_norm.bias

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

              lowercase__:       RobertaAttention        =       layer.attention
              lowercase__:       Any        =       roberta_layer.self_attn_layer_norm.weight
              lowercase__:       int        =       roberta_layer.self_attn_layer_norm.bias

              # self attention
              lowercase__:       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)					)
              )

              lowercase__:       List[str]        =       roberta_layer.self_attn.q_proj.weight
              lowercase__:       int        =       roberta_layer.self_attn.q_proj.bias
              lowercase__:       Optional[int]        =       roberta_layer.self_attn.k_proj.weight
              lowercase__:       Union[str, Any]        =       roberta_layer.self_attn.k_proj.bias
              lowercase__:       Optional[int]        =       roberta_layer.self_attn.v_proj.weight
              lowercase__:       Union[str, Any]        =       roberta_layer.self_attn.v_proj.bias

              # self-attention output
              lowercase__:       BertSelfOutput        =       layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              lowercase__:       int        =       roberta_layer.self_attn.out_proj.weight
              lowercase__:       Optional[Any]        =       roberta_layer.self_attn.out_proj.bias

              # this one is final layer norm
              lowercase__:       List[Any]        =       roberta_layer.final_layer_norm.weight
              lowercase__:       int        =       roberta_layer.final_layer_norm.bias

              # intermediate
              lowercase__:       BertIntermediate        =       layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              lowercase__:       Union[str, Any]        =       roberta_layer.fca.weight
              lowercase__:       Optional[int]        =       roberta_layer.fca.bias

              # output
              lowercase__:       BertOutput        =       layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              lowercase__:       Tuple        =       roberta_layer.fca.weight
              lowercase__:       Tuple        =       roberta_layer.fca.bias
              # end of layer

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

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

       lowercase__:       Optional[Any]        =       model(snake_case					)[0]
       if classification_head:
              lowercase__:       Any        =       roberta.model.classification_heads['mnli'](roberta.extract_features(snake_case					)					)
       else:
              lowercase__:       Any        =       roberta.model(snake_case					)[0]
       print(our_output.shape      ,							their_output.shape					)
       lowercase__:       Any        =       torch.max(torch.abs(our_output - their_output					)					).item()
       print(f'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
       lowercase__:       str        =       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__":
 __lowerCAmelCase			=							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			=							parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = False					)  ->    list[float]:
       if radian_mode:
              return [magnitude * cos(snake_case					), magnitude * sin(snake_case					)]
       return [magnitude * cos(radians(snake_case					)					), magnitude * sin(radians(snake_case					)					)]




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = 10**-1					)  ->    bool:
       lowercase__:       NDArray[floataa]        =       cross(snake_case      ,							snake_case					)
       lowercase__:       float        =       sum(snake_case					)
       return abs(snake_case					) < eps


if __name__ == "__main__":
 # Test to check if it works
 __lowerCAmelCase			=							array(
     [
         polar_force(718.4, 1_80 - 30),
         polar_force(879.54, 45),
         polar_force(1_00, -90),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 __lowerCAmelCase			=							array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(2_15, 1_80 - 45),
         polar_force(2_64, 90 - 30),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 __lowerCAmelCase			=							array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

 __lowerCAmelCase			=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()


