






"""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()
_lowerCAmelCase  :  Union[str, Any]   =			logging.get_logger(__name__)

_lowerCAmelCase  :  str   =			"""Hello world! cécé herlolip"""


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   str ,				snake_case					:   str ,				snake_case					:   bool							)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__	:						Dict       =       FairseqRobertaModel.from_pretrained(snake_case							)
     roberta.eval()  # disable dropout
     UpperCAmelCase__	:						List[Any]       =       roberta.model.encoder.sentence_encoder
     UpperCAmelCase__	:						List[str]       =       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:
          UpperCAmelCase__	:						Optional[int]       =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
     print("Our BERT config:" ,				snake_case							)

     UpperCAmelCase__	:						int       =       RobertaForSequenceClassification(snake_case							) if classification_head else RobertaForMaskedLM(snake_case							)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCAmelCase__	:						int       =       roberta_sent_encoder.embed_tokens.weight
     UpperCAmelCase__	:						Optional[int]       =       roberta_sent_encoder.embed_positions.weight
     UpperCAmelCase__	:						Any       =       torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
     UpperCAmelCase__	:						List[Any]       =       roberta_sent_encoder.emb_layer_norm.weight
     UpperCAmelCase__	:						Dict       =       roberta_sent_encoder.emb_layer_norm.bias

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

          # self attention
          UpperCAmelCase__	:						BertSelfAttention       =       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__	:						Optional[int]       =       roberta_layer.self_attn.q_proj.weight
          UpperCAmelCase__	:						int       =       roberta_layer.self_attn.q_proj.bias
          UpperCAmelCase__	:						Any       =       roberta_layer.self_attn.k_proj.weight
          UpperCAmelCase__	:						Optional[int]       =       roberta_layer.self_attn.k_proj.bias
          UpperCAmelCase__	:						Dict       =       roberta_layer.self_attn.v_proj.weight
          UpperCAmelCase__	:						Dict       =       roberta_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase__	:						BertSelfOutput       =       layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          UpperCAmelCase__	:						List[Any]       =       roberta_layer.self_attn.out_proj.weight
          UpperCAmelCase__	:						Any       =       roberta_layer.self_attn.out_proj.bias
          UpperCAmelCase__	:						Optional[int]       =       roberta_layer.self_attn_layer_norm.weight
          UpperCAmelCase__	:						List[str]       =       roberta_layer.self_attn_layer_norm.bias

          # intermediate
          UpperCAmelCase__	:						BertIntermediate       =       layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase__	:						Tuple       =       roberta_layer.fca.weight
          UpperCAmelCase__	:						Any       =       roberta_layer.fca.bias

          # output
          UpperCAmelCase__	:						BertOutput       =       layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase__	:						List[Any]       =       roberta_layer.fca.weight
          UpperCAmelCase__	:						Optional[int]       =       roberta_layer.fca.bias
          UpperCAmelCase__	:						Tuple       =       roberta_layer.final_layer_norm.weight
          UpperCAmelCase__	:						int       =       roberta_layer.final_layer_norm.bias
          # end of layer

     if classification_head:
          UpperCAmelCase__	:						Dict       =       roberta.model.classification_heads["mnli"].dense.weight
          UpperCAmelCase__	:						Union[str, Any]       =       roberta.model.classification_heads["mnli"].dense.bias
          UpperCAmelCase__	:						List[str]       =       roberta.model.classification_heads["mnli"].out_proj.weight
          UpperCAmelCase__	:						Dict       =       roberta.model.classification_heads["mnli"].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase__	:						Any       =       roberta.model.encoder.lm_head.dense.weight
          UpperCAmelCase__	:						Optional[Any]       =       roberta.model.encoder.lm_head.dense.bias
          UpperCAmelCase__	:						str       =       roberta.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase__	:						int       =       roberta.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase__	:						Union[str, Any]       =       roberta.model.encoder.lm_head.weight
          UpperCAmelCase__	:						Any       =       roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     UpperCAmelCase__	:						torch.Tensor       =       roberta.encode(snake_case							).unsqueeze(0							)  # batch of size 1

     UpperCAmelCase__	:						Union[str, Any]       =       model(snake_case							)[0]
     if classification_head:
          UpperCAmelCase__	:						Optional[int]       =       roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case							)							)
     else:
          UpperCAmelCase__	:						Dict       =       roberta.model(snake_case							)[0]
     print(our_output.shape ,				their_output.shape							)
     UpperCAmelCase__	:						List[Any]       =       torch.max(torch.abs(our_output - their_output							)							).item()
     print(f'max_absolute_diff = {max_absolute_diff}'							)  # ~ 1e-7
     UpperCAmelCase__	:						Optional[int]       =       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__":
 _lowerCAmelCase  :  Dict   =			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."""
 )
 _lowerCAmelCase  :  Dict   =			parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )










"""simple docstring"""



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCAmelCase  :  Optional[Any]   =			{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  str   =			[
     """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """ConvNextV2ForImageClassification""",
     """ConvNextV2Model""",
     """ConvNextV2PreTrainedModel""",
     """ConvNextV2Backbone""",
 ]


if TYPE_CHECKING:
 from .configuration_convnextva import (
     CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConvNextVaConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convnextva import (
      CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvNextVaBackbone,
      ConvNextVaForImageClassification,
      ConvNextVaModel,
      ConvNextVaPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase  :  List[str]   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


