





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 				=       logging.get_logger(__name__)

snake_case 				=       """Hello world! cécé herlolip"""

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :str			,							snake_case__  :bool     )      ->						List[Any]:
  _lowercase							= FairseqRobertaModel.from_pretrained(snake_case__     )
  roberta.eval()  # disable dropout
  _lowercase							= roberta.model.encoder.sentence_encoder
  _lowercase							= 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:
    _lowercase							= roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

  print('Our RoBERTa config:'			,							snake_case__     )

  _lowercase							= XLMRobertaXLForSequenceClassification(snake_case__     ) if classification_head else XLMRobertaXLForMaskedLM(snake_case__     )
  model.eval()

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

  _lowercase							= roberta_sent_encoder.layer_norm.weight
  _lowercase							= roberta_sent_encoder.layer_norm.bias

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

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

    # self attention
    _lowercase							= 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							= roberta_layer.self_attn.q_proj.weight
    _lowercase							= roberta_layer.self_attn.q_proj.bias
    _lowercase							= roberta_layer.self_attn.k_proj.weight
    _lowercase							= roberta_layer.self_attn.k_proj.bias
    _lowercase							= roberta_layer.self_attn.v_proj.weight
    _lowercase							= roberta_layer.self_attn.v_proj.bias

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

    # this one is final layer norm
    _lowercase							= roberta_layer.final_layer_norm.weight
    _lowercase							= roberta_layer.final_layer_norm.bias

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

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

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

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

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case 				=       {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""BertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BertForMaskedLM""",
          """BertForMultipleChoice""",
          """BertForNextSentencePrediction""",
          """BertForPreTraining""",
          """BertForQuestionAnswering""",
          """BertForSequenceClassification""",
          """BertForTokenClassification""",
          """BertLayer""",
          """BertLMHeadModel""",
          """BertModel""",
          """BertPreTrainedModel""",
          """load_tf_weights_in_bert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFBertEmbeddings""",
          """TFBertForMaskedLM""",
          """TFBertForMultipleChoice""",
          """TFBertForNextSentencePrediction""",
          """TFBertForPreTraining""",
          """TFBertForQuestionAnswering""",
          """TFBertForSequenceClassification""",
          """TFBertForTokenClassification""",
          """TFBertLMHeadModel""",
          """TFBertMainLayer""",
          """TFBertModel""",
          """TFBertPreTrainedModel""",
      ]
try:
      if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""TFBertTokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FlaxBertForCausalLM""",
          """FlaxBertForMaskedLM""",
          """FlaxBertForMultipleChoice""",
          """FlaxBertForNextSentencePrediction""",
          """FlaxBertForPreTraining""",
          """FlaxBertForQuestionAnswering""",
          """FlaxBertForSequenceClassification""",
          """FlaxBertForTokenClassification""",
          """FlaxBertModel""",
          """FlaxBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
      from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_fast import BertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert import (
                BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BertForMaskedLM,
                BertForMultipleChoice,
                BertForNextSentencePrediction,
                BertForPreTraining,
                BertForQuestionAnswering,
                BertForSequenceClassification,
                BertForTokenClassification,
                BertLayer,
                BertLMHeadModel,
                BertModel,
                BertPreTrainedModel,
                load_tf_weights_in_bert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bert import (
                TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFBertEmbeddings,
                TFBertForMaskedLM,
                TFBertForMultipleChoice,
                TFBertForNextSentencePrediction,
                TFBertForPreTraining,
                TFBertForQuestionAnswering,
                TFBertForSequenceClassification,
                TFBertForTokenClassification,
                TFBertLMHeadModel,
                TFBertMainLayer,
                TFBertModel,
                TFBertPreTrainedModel,
            )

      try:
            if not is_tensorflow_text_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_tf import TFBertTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bert import (
                FlaxBertForCausalLM,
                FlaxBertForMaskedLM,
                FlaxBertForMultipleChoice,
                FlaxBertForNextSentencePrediction,
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
                FlaxBertForTokenClassification,
                FlaxBertModel,
                FlaxBertPreTrainedModel,
            )

else:
      import sys

      snake_case 				=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)