




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


lowerCamelCase    :		List[Any]			   =       logging.get_logger(__name__)

lowerCamelCase    :		List[Any]			   =       {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''switch_transformers'''
 UpperCamelCase						       =						['''past_key_values''']
 UpperCamelCase						       =						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

 def __init__(    self :  str						,							A_ :  int=32128						,							A_ :  Any=768						,							A_ :  List[Any]=64						,							A_ :  Optional[int]=2048						,							A_ :  Tuple=64						,							A_ :  Optional[Any]=12						,							A_ :  Dict=3						,							A_ :  Tuple=12						,							A_ :  int=3						,							A_ :  Any=12						,							A_ :  str=8						,							A_ :  int=False						,							A_ :  Union[str, Any]=0.01						,							A_ :  int="float32"						,							A_ :  Optional[Any]=False						,							A_ :  Union[str, Any]=32						,							A_ :  Any=128						,							A_ :  List[str]=0.1						,							A_ :  Dict=1E-6						,							A_ :  Optional[Any]=0.001						,							A_ :  Dict=0.001						,							A_ :  Any=1.0						,							A_ :  Optional[Any]="relu"						,							A_ :  Any=True						,							A_ :  Dict=False						,							A_ :  Tuple=True						,							A_ :  Any=0						,							A_ :  Union[str, Any]=1						,							**A_ :  List[str]						,							)       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   d_model
       lowerCamelCase_							=   d_kv
       lowerCamelCase_							=   d_ff

       lowerCamelCase_							=   num_sparse_encoder_layers

       lowerCamelCase_							=   num_layers
       lowerCamelCase_							=   (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       lowerCamelCase_							=   num_sparse_decoder_layers

       # This tells us, each how many encoder layer we'll have to set a sparse layer.
       if self.num_sparse_encoder_layers > 0:
             lowerCamelCase_							=   self.num_layers // self.num_sparse_encoder_layers
       else:
             lowerCamelCase_							=   self.num_layers  # HACK: this will create 0 sparse layers

       # This tells us, each how many encoder layer we'll have to set a sparse layer.
       if self.num_sparse_decoder_layers > 0:
             lowerCamelCase_							=   self.num_decoder_layers // self.num_sparse_decoder_layers
       else:
             lowerCamelCase_							=   self.num_decoder_layers  # HACK: this will create 0 sparse layers

       lowerCamelCase_							=   num_heads
       lowerCamelCase_							=   num_experts
       lowerCamelCase_							=   expert_capacity
       lowerCamelCase_							=   router_bias
       lowerCamelCase_							=   router_jitter_noise
       if router_dtype not in ["float32", "float16", "bfloat16"]:
             raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""  )
       lowerCamelCase_							=   router_dtype

       lowerCamelCase_							=   router_ignore_padding_tokens
       lowerCamelCase_							=   relative_attention_num_buckets
       lowerCamelCase_							=   relative_attention_max_distance

       lowerCamelCase_							=   dropout_rate
       lowerCamelCase_							=   layer_norm_epsilon
       lowerCamelCase_							=   initializer_factor
       lowerCamelCase_							=   feed_forward_proj
       lowerCamelCase_							=   use_cache
       lowerCamelCase_							=   add_router_probs

       lowerCamelCase_							=   router_z_loss_coef
       lowerCamelCase_							=   router_aux_loss_coef

       lowerCamelCase_							=   self.feed_forward_proj.split('-'  )
       lowerCamelCase_							=   act_info[-1]
       lowerCamelCase_							=   act_info[0] == 'gated'

       if len(A_  ) > 1 and act_info[0] != "gated" or len(A_  ) > 2:
             raise ValueError(
                 f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                 'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                 '\'gated-gelu\' or \'relu\''  )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
             lowerCamelCase_							=   'gelu_new'

       super().__init__(
           pad_token_id=A_						,							eos_token_id=A_						,							is_encoder_decoder=A_						,							**A_						,							)









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()
lowerCamelCase    :		Tuple			   =       logging.get_logger(__name__)

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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	str     ,							lowercase     :	bool					):


      '''simple docstring'''



      lowerCamelCase_							=   FairseqRobertaModel.from_pretrained(lowercase					)
      roberta.eval()  # disable dropout
      lowerCamelCase_							=   roberta.model.encoder.sentence_encoder
      lowerCamelCase_							=   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:
            lowerCamelCase_							=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

      print('Our RoBERTa config:'     ,							lowercase					)

      lowerCamelCase_							=   XLMRobertaXLForSequenceClassification(lowercase					) if classification_head else XLMRobertaXLForMaskedLM(lowercase					)
      model.eval()

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

      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.weight
      lowerCamelCase_							=   roberta_sent_encoder.layer_norm.bias

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

            lowerCamelCase_							=   layer.attention
            lowerCamelCase_							=   roberta_layer.self_attn_layer_norm.weight
            lowerCamelCase_							=   roberta_layer.self_attn_layer_norm.bias

            # self attention
            lowerCamelCase_							=   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_							=   roberta_layer.self_attn.q_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.q_proj.bias
            lowerCamelCase_							=   roberta_layer.self_attn.k_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.k_proj.bias
            lowerCamelCase_							=   roberta_layer.self_attn.v_proj.weight
            lowerCamelCase_							=   roberta_layer.self_attn.v_proj.bias

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

            # this one is final layer norm
            lowerCamelCase_							=   roberta_layer.final_layer_norm.weight
            lowerCamelCase_							=   roberta_layer.final_layer_norm.bias

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

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

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

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

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

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


if __name__ == "__main__":
 lowerCamelCase    :		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."
 )
 lowerCamelCase    :		List[Any]			   =       parser.parse_args()
 convert_xlm_roberta_xl_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )



