






'''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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
     raise Exception('''requires fairseq >= 1.0.0a''')

logging.set_verbosity_info()
__A    	=   logging.get_logger(__name__)

__A    	=   '''Hello world! cécé herlolip'''




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  ,		A  :       Tuple  ,		A  :       str  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= FairseqRobertaModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
   roberta.eval()  # disable dropout
   __snake_case				:      Tuple     							= roberta.model.encoder.sentence_encoder
   __snake_case				:      Dict     							= XLMRobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings  ,		hidden_size=roberta.cfg.model.encoder_embed_dim  ,		num_hidden_layers=roberta.cfg.model.encoder_layers  ,		num_attention_heads=roberta.cfg.model.encoder_attention_heads  ,		intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim  ,		max_position_embeddings=5_14  ,		type_vocab_size=1  ,		layer_norm_eps=1e-5  ,		)
   if classification_head:
      __snake_case				:      Tuple     							= roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

   print('Our RoBERTa config:'  ,		__SCREAMING_SNAKE_CASE  )

   __snake_case				:      Tuple     							= XLMRobertaXLForSequenceClassification(__SCREAMING_SNAKE_CASE  ) if classification_head else XLMRobertaXLForMaskedLM(__SCREAMING_SNAKE_CASE  )
   model.eval()

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

   __snake_case				:      int     							= roberta_sent_encoder.layer_norm.weight
   __snake_case				:      Optional[Any]     							= roberta_sent_encoder.layer_norm.bias

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

      __snake_case				:      List[Any]     							= layer.attention
      __snake_case				:      List[str]     							= roberta_layer.self_attn_layer_norm.weight
      __snake_case				:      Any     							= roberta_layer.self_attn_layer_norm.bias

      # self attention
      __snake_case				:      str     							= 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)  )
      )

      __snake_case				:      Any     							= roberta_layer.self_attn.q_proj.weight
      __snake_case				:      List[Any]     							= roberta_layer.self_attn.q_proj.bias
      __snake_case				:      List[str]     							= roberta_layer.self_attn.k_proj.weight
      __snake_case				:      List[str]     							= roberta_layer.self_attn.k_proj.bias
      __snake_case				:      Optional[Any]     							= roberta_layer.self_attn.v_proj.weight
      __snake_case				:      List[Any]     							= roberta_layer.self_attn.v_proj.bias

      # self-attention output
      __snake_case				:      int     							= layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      __snake_case				:      Optional[Any]     							= roberta_layer.self_attn.out_proj.weight
      __snake_case				:      Union[str, Any]     							= roberta_layer.self_attn.out_proj.bias

      # this one is final layer norm
      __snake_case				:      List[Any]     							= roberta_layer.final_layer_norm.weight
      __snake_case				:      Optional[int]     							= roberta_layer.final_layer_norm.bias

      # intermediate
      __snake_case				:      List[Any]     							= layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      __snake_case				:      Optional[Any]     							= roberta_layer.fca.weight
      __snake_case				:      Any     							= roberta_layer.fca.bias

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

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

   # Let's check that we get the same results.
   __snake_case				:      Union[str, Any]     							= roberta.encode(__SCREAMING_SNAKE_CASE  ).unsqueeze(0  )  # batch of size 1

   __snake_case				:      Optional[int]     							= model(__SCREAMING_SNAKE_CASE  )[0]
   if classification_head:
      __snake_case				:      int     							= roberta.model.classification_heads['mnli'](roberta.extract_features(__SCREAMING_SNAKE_CASE  )  )
   else:
      __snake_case				:      Union[str, Any]     							= roberta.model(__SCREAMING_SNAKE_CASE  )[0]
   print(our_output.shape  ,		their_output.shape  )
   __snake_case				:      Tuple     							= torch.max(torch.abs(our_output - their_output  )  ).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
   __snake_case				:      int     							= torch.allclose(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_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(__SCREAMING_SNAKE_CASE  ).mkdir(parents=__SCREAMING_SNAKE_CASE  ,		exist_ok=__SCREAMING_SNAKE_CASE  )
   print(F"""Saving model to {pytorch_dump_folder_path}"""  )
   model.save_pretrained(__SCREAMING_SNAKE_CASE  )


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







'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""vit_msn"""







   def __init__(self					,   __a=7_6_8					,   __a=1_2					,   __a=1_2					,   __a=3_0_7_2					,   __a="gelu"					,   __a=0.0					,   __a=0.0					,   __a=0.02					,   __a=1E-06					,   __a=2_2_4					,   __a=1_6					,   __a=3					,   __a=True					,   **__a					,   )			->      Any:
      """simple docstring"""






      super().__init__(**__a)

      __snake_case				:      List[str]     							= hidden_size
      __snake_case				:      Optional[int]     							= num_hidden_layers
      __snake_case				:      Optional[Any]     							= num_attention_heads
      __snake_case				:      str     							= intermediate_size
      __snake_case				:      List[str]     							= hidden_act
      __snake_case				:      List[Any]     							= hidden_dropout_prob
      __snake_case				:      Tuple     							= attention_probs_dropout_prob
      __snake_case				:      List[str]     							= initializer_range
      __snake_case				:      Optional[int]     							= layer_norm_eps
      __snake_case				:      Dict     							= image_size
      __snake_case				:      int     							= patch_size
      __snake_case				:      Dict     							= num_channels
      __snake_case				:      Tuple     							= qkv_bias