



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     ['''image_processor''', '''tokenizer''']
   SCREAMING_SNAKE_CASE__							=     '''AutoImageProcessor'''
   SCREAMING_SNAKE_CASE__							=     '''AutoTokenizer'''





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  self.image_processor





   def __call__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):

         if text is None and images is None:
               raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if images is not None:
               _lowerCAmelCase 			=  self.image_processor(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if text is not None and images is not None:
               _lowerCAmelCase 			=  image_features.pixel_values
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)       ,     tensor_type=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         return ["input_ids", "attention_mask", "pixel_values"]




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()
UpperCAmelCase_									=							logging.get_logger(__name__)

UpperCAmelCase_									=							"Hello world! cécé herlolip"
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	bool				)->List[Any]:
      _lowerCAmelCase 			=  FairseqRobertaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
      roberta.eval()  # disable dropout
      _lowerCAmelCase 			=  roberta.model.encoder.sentence_encoder
      _lowerCAmelCase 			=  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_1_4     ,				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 RoBERTa config:'''     ,				_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  XLMRobertaXLForSequenceClassification(_SCREAMING_SNAKE_CASE				) if classification_head else XLMRobertaXLForMaskedLM(_SCREAMING_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.layer_norm.weight
      _lowerCAmelCase 			=  roberta_sent_encoder.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]

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

            # 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

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

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


if __name__ == "__main__":
    UpperCAmelCase_									=							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."
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )