



"""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()
__snake_case	:				Optional[Any]  					=			logging.get_logger(__name__)

__snake_case	:				int  					=			'Hello world! cécé herlolip'



def   a_	(			__a		,							__a		,							__a      ):
     A__				=   FairseqRobertaModel.from_pretrained(__a      )
     roberta.eval()  # disable dropout
     A__				=   roberta.model.encoder.sentence_encoder
     A__				=   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:
          A__				=   roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
     print('''Our BERT config:'''		,							__a      )

     A__				=   RobertaForSequenceClassification(__a      ) if classification_head else RobertaForMaskedLM(__a      )
     model.eval()

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

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

          # self attention
          A__				=   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)      )
          )

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

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

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

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

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

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

     A__				=   model(__a      )[0]
     if classification_head:
          A__				=   roberta.model.classification_heads['''mnli'''](roberta.extract_features(__a      )      )
     else:
          A__				=   roberta.model(__a      )[0]
     print(our_output.shape		,							their_output.shape      )
     A__				=   torch.max(torch.abs(our_output - their_output      )      ).item()
     print(f'''max_absolute_diff = {max_absolute_diff}'''      )  # ~ 1e-7
     A__				=   torch.allclose(__a		,							__a		,							atol=1e-3      )
     print('''Do both models output the same tensors?'''		,							'''🔥''' if success else '''💩'''      )
     if not success:
          raise Exception('''Something went wRoNg'''      )

     pathlib.Path(__a      ).mkdir(parents=__a		,							exist_ok=__a      )
     print(f'''Saving model to {pytorch_dump_folder_path}'''      )
     model.save_pretrained(__a      )


if __name__ == "__main__":
 __snake_case	:				Optional[int]  					=			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	:				Union[str, Any]  					=			parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )








"""simple docstring"""







from typing import TYPE_CHECKING

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


__snake_case	:				List[Any]  					=			{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case	:				str  					=			[
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

 __snake_case	:				Union[str, Any]  					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



