






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()
snake_case__   :						Any													=   logging.get_logger(__name__)

snake_case__   :						Dict													=   """Hello world! cécé herlolip"""






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  FairseqRobertaModel.from_pretrained(__lowercase)
   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=514     ,   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:'     ,   __lowercase)

   UpperCamelCase_						=  XLMRobertaXLForSequenceClassification(__lowercase) if classification_head else XLMRobertaXLForMaskedLM(__lowercase)
   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(__lowercase).unsqueeze(0)  # batch of size 1

   UpperCamelCase_						=  model(__lowercase)[0]
   if classification_head:
      UpperCamelCase_						=  roberta.model.classification_heads['mnli'](roberta.extract_features(__lowercase))
   else:
      UpperCamelCase_						=  roberta.model(__lowercase)[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(__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__":
 snake_case__   :						str													=   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."""
 )
 snake_case__   :						int													=   parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )













from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_SCREAMING_SNAKE_CASE   =  2_99_79_24_58

# Symbols
_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   =  symbols("ct x y z")





def        _snake_case   (_snake_case				:       float)					->	float:
   if velocity > c:
      raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!')
   elif velocity < 1:
      # Usually the speed should be much higher than 1 (c order of magnitude)
      raise ValueError('Speed must be greater than or equal to 1!')

   return velocity / c





def        _snake_case   (_snake_case				:       float)					->	float:
   return 1 / sqrt(1 - beta(_snake_case) ** 2)





def        _snake_case   (_snake_case				:       float)					->	np.ndarray:
   return np.array(
       [
           [gamma(_snake_case), -gamma(_snake_case) * beta(_snake_case), 0, 0],
           [-gamma(_snake_case) * beta(_snake_case), gamma(_snake_case), 0, 0],
           [0, 0, 1, 0],
           [0, 0, 0, 1],
       ])





def        _snake_case   (_snake_case				:       float      ,    _snake_case				:       np.ndarray | None = None)					->	np.ndarray:
   # Ensure event is not empty
   if event is None:
      _lowercase              =np.array([ct, x, y, z])  # Symbolic four vector
   else:
      event[0] *= c  # x0 is ct (speed of light * time)

   return transformation_matrix(_snake_case) @ event


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example of symbolic vector:
    _SCREAMING_SNAKE_CASE   =  transform(29_97_92_45)
    print("Example of four vector: ")
    print(f'''ct\' = {four_vector[0]}''')
    print(f'''x\' = {four_vector[1]}''')
    print(f'''y\' = {four_vector[2]}''')
    print(f'''z\' = {four_vector[3]}''')

    # Substitute symbols with numerical values
    _SCREAMING_SNAKE_CASE   =  {ct: c, x: 1, y: 1, z: 1}
    _SCREAMING_SNAKE_CASE   =  [four_vector[i].subs(sub_dict) for i in range(4)]

    print(f'''\n{numerical_vector}''')


