


'''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       				=	logging.get_logger(__name__)

__lowerCamelCase       				=	'''Hello world! cécé herlolip'''


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

   UpperCAmelCase__  :List[Any]            =			RobertaForSequenceClassification(UpperCamelCase_	) if classification_head else RobertaForMaskedLM(UpperCamelCase_	)
   model.eval()

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

      # intermediate
      UpperCAmelCase__  :BertIntermediate            =			layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      UpperCAmelCase__  :str            =			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__  :Optional[int]            =			roberta_layer.fca.weight
      UpperCAmelCase__  :Optional[Any]            =			roberta_layer.fca.bias
      UpperCAmelCase__  :List[Any]            =			roberta_layer.final_layer_norm.weight
      UpperCAmelCase__  :str            =			roberta_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      UpperCAmelCase__  :Optional[Any]            =			roberta.model.classification_heads['''mnli'''].dense.weight
      UpperCAmelCase__  :Union[str, Any]            =			roberta.model.classification_heads['''mnli'''].dense.bias
      UpperCAmelCase__  :Optional[int]            =			roberta.model.classification_heads['''mnli'''].out_proj.weight
      UpperCAmelCase__  :Optional[Any]            =			roberta.model.classification_heads['''mnli'''].out_proj.bias
   else:
      # LM Head
      UpperCAmelCase__  :Optional[int]            =			roberta.model.encoder.lm_head.dense.weight
      UpperCAmelCase__  :Union[str, Any]            =			roberta.model.encoder.lm_head.dense.bias
      UpperCAmelCase__  :Any            =			roberta.model.encoder.lm_head.layer_norm.weight
      UpperCAmelCase__  :List[str]            =			roberta.model.encoder.lm_head.layer_norm.bias
      UpperCAmelCase__  :List[str]            =			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(UpperCamelCase_	).unsqueeze(0	)  # batch of size 1

   UpperCAmelCase__  :str            =			model(UpperCamelCase_	)[0]
   if classification_head:
      UpperCAmelCase__  :Tuple            =			roberta.model.classification_heads['''mnli'''](roberta.extract_features(UpperCamelCase_	)	)
   else:
      UpperCAmelCase__  :Optional[int]            =			roberta.model(UpperCamelCase_	)[0]
   print(our_output.shape,      their_output.shape	)
   UpperCAmelCase__  :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(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__":
 __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
 )







'''simple docstring'''





from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

__lowerCamelCase       				=	logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):


    def 					a__    (				cls	:			Optional[int]	):
       return cls


def 					a__    (				UpperCamelCase_	:			Union[Tuple, List],      UpperCamelCase_	:			Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,      UpperCamelCase_	:			Optional["torch.device"] = None,      UpperCamelCase_	:			Optional["torch.dtype"] = None,      UpperCamelCase_	:			Optional["torch.layout"] = None,      ):
   UpperCAmelCase__  :Union[str, Any]            =			device
   UpperCAmelCase__  :List[Any]            =			shape[0]

   UpperCAmelCase__  :Tuple            =			layout or torch.strided
   UpperCAmelCase__  :Union[str, Any]            =			device or torch.device('''cpu'''	)

   if generator is not None:
      UpperCAmelCase__  :str            =			generator.device.type if not isinstance(UpperCamelCase_,      UpperCamelCase_	) else generator[0].device.type
      if gen_device_type != device.type and gen_device_type == "cpu":
         UpperCAmelCase__  :Tuple            =			'''cpu'''
         if device != "mps":
            logger.info(
                F'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
                F''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
                F''' slighly speed up this function by passing a generator that was created on the {device} device.'''	)
      elif gen_device_type != device.type and gen_device_type == "cuda":
         raise ValueError(F'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''	)

   if isinstance(UpperCamelCase_,      UpperCamelCase_	):
      UpperCAmelCase__  :List[str]            =			(1,) + shape[1:]
      UpperCAmelCase__  :Optional[int]            =			[
          torch.randn(UpperCamelCase_,      generator=generator[i],      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	)
          for i in range(UpperCamelCase_	)
      ]
      UpperCAmelCase__  :int            =			torch.cat(UpperCamelCase_,      dim=0	).to(UpperCamelCase_	)
   else:
      UpperCAmelCase__  :Optional[int]            =			torch.randn(UpperCamelCase_,      generator=UpperCamelCase_,      device=UpperCamelCase_,      dtype=UpperCamelCase_,      layout=UpperCamelCase_	).to(UpperCamelCase_	)

   return latents


def 					a__    (				UpperCamelCase_	:			Dict	):
   if is_torch_version('''<''',      '''2.0.0'''	) or not hasattr(UpperCamelCase_,      '''_dynamo'''	):
      return False
   return isinstance(UpperCamelCase_,      torch._dynamo.eval_frame.OptimizedModule	)



