



"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE				:     Optional[int]          =	logging.get_logger(__name__)


class       __lowerCamelCase	(   __lowercase    ):







   def __init__(self       ,	*lowerCamelCase       ,	**lowerCamelCase				):




      '''simple docstring'''




      warnings.warn(
          """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use MobileNetV2ImageProcessor instead."""       ,	lowerCamelCase       ,	)
      super().__init__(*lowerCamelCase       ,	**lowerCamelCase				)




"""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 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()
SCREAMING_SNAKE_CASE				:     Tuple          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[Any]          =	'''Hello world! cécé herlolip'''






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  str   ,	snake_case_  :  bool   )							->	Optional[int]:






   """simple docstring"""


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

   _lowerCAmelCase					     =		RobertaForSequenceClassification(snake_case_   ) if classification_head else RobertaForMaskedLM(snake_case_   )
   model.eval()

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

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

      # self attention
      _lowerCAmelCase					     =		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)   )
      )

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

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

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

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

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

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

   _lowerCAmelCase					     =		model(snake_case_   )[0]
   if classification_head:
      _lowerCAmelCase					     =		roberta.model.classification_heads["""mnli"""](roberta.extract_features(snake_case_   )   )
   else:
      _lowerCAmelCase					     =		roberta.model(snake_case_   )[0]
   print(our_output.shape   ,	their_output.shape   )
   _lowerCAmelCase					     =		torch.max(torch.abs(our_output - their_output   )   ).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
   _lowerCAmelCase					     =		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				:     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.'''
  )
  SCREAMING_SNAKE_CASE				:     Any          =	parser.parse_args()
  convert_roberta_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )