

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    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			Any:



  """simple docstring"""






  _snake_case		=							FairseqRobertaModel.from_pretrained(_UpperCamelCase  )
  roberta.eval()  # disable dropout
  _snake_case		=							roberta.model.encoder.sentence_encoder
  _snake_case		=							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:
    _snake_case		=							roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
  print('''Our BERT config:'''				, _UpperCamelCase  )

  _snake_case		=							RobertaForSequenceClassification(_UpperCamelCase  ) if classification_head else RobertaForMaskedLM(_UpperCamelCase  )
  model.eval()

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

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

    # self attention
    _snake_case		=							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)  )
    )

    _snake_case		=							roberta_layer.self_attn.q_proj.weight
    _snake_case		=							roberta_layer.self_attn.q_proj.bias
    _snake_case		=							roberta_layer.self_attn.k_proj.weight
    _snake_case		=							roberta_layer.self_attn.k_proj.bias
    _snake_case		=							roberta_layer.self_attn.v_proj.weight
    _snake_case		=							roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

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

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


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
 )




__A    	=			{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  return c.isdigit()



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							[]

  # iterate over the string in reverse order
  for c in expression.split()[::-1]:
    # push operand to stack
    if is_operand(_UpperCamelCase  ):
      stack.append(int(_UpperCamelCase  )  )

    else:
      # pop values from stack can calculate the result
      # push the result onto the stack again
      _snake_case		=							stack.pop()
      _snake_case		=							stack.pop()
      stack.append(calc[c](_UpperCamelCase				, _UpperCamelCase  )  )

  return stack.pop()


# Driver code
if __name__ == "__main__":
 __A    	=			'''+ 9 * 2 6'''
 print(evaluate(test_expression))

 __A    	=			'''/ * 10 2 + 4 1 '''
 print(evaluate(test_expression))

