


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__	:      int				=						{
    '''configuration_musicgen''': [
        '''MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MusicgenConfig''',
        '''MusicgenDecoderConfig''',
    ],
    '''processing_musicgen''': ['''MusicgenProcessor'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__	:      int				=						[
          '''MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''MusicgenForConditionalGeneration''',
          '''MusicgenForCausalLM''',
          '''MusicgenModel''',
          '''MusicgenPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_musicgen import (
          MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MusicgenConfig,
          MusicgenDecoderConfig,
      )
      from .processing_musicgen import MusicgenProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_musicgen import (
                MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
                MusicgenForCausalLM,
                MusicgenForConditionalGeneration,
                MusicgenModel,
                MusicgenPreTrainedModel,
            )

else:
      import sys

      A__	:      int				=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










'''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()
A__	:      List[str]				=						logging.get_logger(__name__)

A__	:      Union[str, Any]				=						"""Hello world! cécé herlolip"""



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       bool							)  ->	Optional[Any]:
     __lowerCamelCase     :			Any              =	FairseqRobertaModel.from_pretrained(UpperCAmelCase_							)
     roberta.eval()  # disable dropout
     __lowerCamelCase     :			Optional[int]              =	roberta.model.encoder.sentence_encoder
     __lowerCamelCase     :			Optional[Any]              =	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=5_14   ,					type_vocab_size=1   ,					layer_norm_eps=1e-5   ,					)
     if classification_head:
          __lowerCamelCase     :			Optional[Any]              =	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
     print('Our BERT config:'   ,					UpperCAmelCase_							)

     __lowerCamelCase     :			Tuple              =	RobertaForSequenceClassification(UpperCAmelCase_							) if classification_head else RobertaForMaskedLM(UpperCAmelCase_							)
     model.eval()

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

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

          # self attention
          __lowerCamelCase     :			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)							)
          )

          __lowerCamelCase     :			Optional[int]              =	roberta_layer.self_attn.q_proj.weight
          __lowerCamelCase     :			Any              =	roberta_layer.self_attn.q_proj.bias
          __lowerCamelCase     :			Tuple              =	roberta_layer.self_attn.k_proj.weight
          __lowerCamelCase     :			Dict              =	roberta_layer.self_attn.k_proj.bias
          __lowerCamelCase     :			Any              =	roberta_layer.self_attn.v_proj.weight
          __lowerCamelCase     :			List[Any]              =	roberta_layer.self_attn.v_proj.bias

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

          # intermediate
          __lowerCamelCase     :			BertIntermediate              =	layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          __lowerCamelCase     :			Union[str, Any]              =	roberta_layer.fca.weight
          __lowerCamelCase     :			Optional[Any]              =	roberta_layer.fca.bias

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

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

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

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






