
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        =logging.get_logger(__name__)

_lowerCamelCase        ="""Hello world! cécé herlolip"""





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Dict          =				FairseqRobertaModel.from_pretrained(lowerCamelCase							)
 roberta.eval()  # disable dropout
 lowerCamelCase		:     Any          =				roberta.model.encoder.sentence_encoder
 lowerCamelCase		:     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:
  lowerCamelCase		:     Tuple          =				roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
 print("""Our BERT config:""",     lowerCamelCase							)

 lowerCamelCase		:     List[str]          =				RobertaForSequenceClassification(lowerCamelCase							) if classification_head else RobertaForMaskedLM(lowerCamelCase							)
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 lowerCamelCase		:     int          =				roberta_sent_encoder.embed_tokens.weight
 lowerCamelCase		:     Any          =				roberta_sent_encoder.embed_positions.weight
 lowerCamelCase		:     int          =				torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight							)  # just zero them out b/c RoBERTa doesn't use them.
 lowerCamelCase		:     Union[str, 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		:     Tuple          =				roberta_layer.self_attn.q_proj.weight
  lowerCamelCase		:     Dict          =				roberta_layer.self_attn.q_proj.bias
  lowerCamelCase		:     Tuple          =				roberta_layer.self_attn.k_proj.weight
  lowerCamelCase		:     int          =				roberta_layer.self_attn.k_proj.bias
  lowerCamelCase		:     Union[str, Any]          =				roberta_layer.self_attn.v_proj.weight
  lowerCamelCase		:     int          =				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		:     Tuple          =				roberta_layer.self_attn.out_proj.weight
  lowerCamelCase		:     List[str]          =				roberta_layer.self_attn.out_proj.bias
  lowerCamelCase		:     Union[str, Any]          =				roberta_layer.self_attn_layer_norm.weight
  lowerCamelCase		:     int          =				roberta_layer.self_attn_layer_norm.bias

  # intermediate
  lowerCamelCase		:     BertIntermediate          =				layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  lowerCamelCase		:     List[Any]          =				roberta_layer.fca.weight
  lowerCamelCase		:     Dict          =				roberta_layer.fca.bias

  # output
  lowerCamelCase		:     BertOutput          =				layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  lowerCamelCase		:     Dict          =				roberta_layer.fca.weight
  lowerCamelCase		:     Optional[Any]          =				roberta_layer.fca.bias
  lowerCamelCase		:     Dict          =				roberta_layer.final_layer_norm.weight
  lowerCamelCase		:     Any          =				roberta_layer.final_layer_norm.bias
  # end of layer

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

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

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

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


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






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				args.serialization_dir
 lowerCamelCase		:     List[Any]          =				args.pruning_method
 lowerCamelCase		:     List[Any]          =				args.threshold

 lowerCamelCase		:     str          =				torch.load(os.path.join(lowerCamelCase,     """pytorch_model.bin"""							),     map_location="""cpu"""							)

 lowerCamelCase		:     Optional[int]          =				0  # Number of remaining (not pruned) params in the encoder
 lowerCamelCase		:     Dict          =				0  # Number of params in the encoder

 print("""name""".ljust(60,     """ """							),     """Remaining Weights %""",     """Remaining Weight"""							)
 for name, param in st.items():
  if "encoder" not in name:
   continue

  if "mask_scores" in name:
   if pruning_method == "topK":
    lowerCamelCase		:     List[str]          =				TopKBinarizer.apply(lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "sigmoied_threshold":
    lowerCamelCase		:     Optional[int]          =				ThresholdBinarizer.apply(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "l0":
    lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				-0.1, 1.1
    lowerCamelCase		:     Any          =				torch.sigmoid(lowerCamelCase							)
    lowerCamelCase		:     str          =				s * (r - l) + l
    lowerCamelCase		:     str          =				s_bar.clamp(min=0.0,     max=1.0							)
    lowerCamelCase		:     Union[str, Any]          =				(mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""							)
   remaining_count += mask_ones
   print(name.ljust(60,     """ """							),     str(round(100 * mask_ones / param.numel(),     3							)							).ljust(20,     """ """							),     str(lowerCamelCase							)							)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""							)
 print("""Remaining Weights (global) %: """,     100 * remaining_count / encoder_count							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()

      parser.add_argument(
          """--pruning_method""",
          choices=["""l0""", """topK""", """sigmoied_threshold"""],
          type=str,
          required=True,
          help=(
              """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
              """ pruning)"""
          ),
      )
      parser.add_argument(
          """--threshold""",
          type=float,
          required=False,
          help=(
              """For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
              """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
              """Not needed for `l0`"""
          ),
      )
      parser.add_argument(
          """--serialization_dir""",
          type=str,
          required=True,
          help="""Folder containing the model that was previously fine-pruned""",
      )

      _lowerCamelCase        =parser.parse_args()

      main(args)




