


"""simple docstring"""


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


__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					{
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    '''switch_transformers'''
   _a   :       Dict									=    ['''past_key_values''']
   _a   :       List[str]									=    {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


   def __init__(						self ,     lowerCamelCase__=3_2128 ,     lowerCamelCase__=768 ,     lowerCamelCase__=64 ,     lowerCamelCase__=2048 ,     lowerCamelCase__=64 ,     lowerCamelCase__=12 ,     lowerCamelCase__=3 ,     lowerCamelCase__=12 ,     lowerCamelCase__=3 ,     lowerCamelCase__=12 ,     lowerCamelCase__=8 ,     lowerCamelCase__=False ,     lowerCamelCase__=0.01 ,     lowerCamelCase__="float32" ,     lowerCamelCase__=False ,     lowerCamelCase__=32 ,     lowerCamelCase__=128 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=1E-6 ,     lowerCamelCase__=0.001 ,     lowerCamelCase__=0.001 ,     lowerCamelCase__=1.0 ,     lowerCamelCase__="relu" ,     lowerCamelCase__=True ,     lowerCamelCase__=False ,     lowerCamelCase__=True ,     lowerCamelCase__=0 ,     lowerCamelCase__=1 ,     **lowerCamelCase__ ,     )      ->							int:
    lowercase__       :       str				=						vocab_size
    lowercase__       :       Dict				=						d_model
    lowercase__       :       Optional[int]				=						d_kv
    lowercase__       :       str				=						d_ff

    lowercase__       :       Union[str, Any]				=						num_sparse_encoder_layers

    lowercase__       :       Optional[Any]				=						num_layers
    lowercase__       :       Tuple				=						(
        num_decoder_layers if num_decoder_layers is not None else self.num_layers
    )  # default = symmetry
    lowercase__       :       Union[str, Any]				=						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:
     lowercase__       :       List[str]				=						self.num_layers // self.num_sparse_encoder_layers
    else:
     lowercase__       :       Any				=						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:
     lowercase__       :       Tuple				=						self.num_decoder_layers // self.num_sparse_decoder_layers
    else:
     lowercase__       :       Any				=						self.num_decoder_layers  # HACK: this will create 0 sparse layers

    lowercase__       :       Union[str, Any]				=						num_heads
    lowercase__       :       Tuple				=						num_experts
    lowercase__       :       Tuple				=						expert_capacity
    lowercase__       :       str				=						router_bias
    lowercase__       :       Optional[Any]				=						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}'''						)
    lowercase__       :       Union[str, Any]				=						router_dtype

    lowercase__       :       Any				=						router_ignore_padding_tokens
    lowercase__       :       Any				=						relative_attention_num_buckets
    lowercase__       :       Optional[Any]				=						relative_attention_max_distance

    lowercase__       :       int				=						dropout_rate
    lowercase__       :       Optional[int]				=						layer_norm_epsilon
    lowercase__       :       Dict				=						initializer_factor
    lowercase__       :       List[str]				=						feed_forward_proj
    lowercase__       :       List[str]				=						use_cache
    lowercase__       :       Tuple				=						add_router_probs

    lowercase__       :       str				=						router_z_loss_coef
    lowercase__       :       Optional[int]				=						router_aux_loss_coef

    lowercase__       :       Dict				=						self.feed_forward_proj.split("""-"""						)
    lowercase__       :       List[Any]				=						act_info[-1]
    lowercase__       :       Dict				=						act_info[0] == """gated"""

    if len(lowerCamelCase__						) > 1 and act_info[0] != "gated" or len(lowerCamelCase__						) > 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":
     lowercase__       :       Optional[Any]				=						"""gelu_new"""

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



"""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()
__snake_case    					=					logging.get_logger(__name__)

__snake_case    					=					'Hello world! cécé herlolip'


def    _lowerCamelCase   (     lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							bool  ):
 lowercase__       :       int				=						FairseqRobertaModel.from_pretrained(lowerCamelCase__  )
 roberta.eval()  # disable dropout
 lowercase__       :       Tuple				=						roberta.model.encoder.sentence_encoder
 lowercase__       :       Tuple				=						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:
  lowercase__       :       Any				=						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

 print("""Our RoBERTa config:"""       ,		lowerCamelCase__  )

 lowercase__       :       List[Any]				=						XLMRobertaXLForSequenceClassification(lowerCamelCase__  ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase__  )
 model.eval()

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

 lowercase__       :       int				=						roberta_sent_encoder.layer_norm.weight
 lowercase__       :       List[Any]				=						roberta_sent_encoder.layer_norm.bias

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

  lowercase__       :       RobertaAttention				=						layer.attention
  lowercase__       :       str				=						roberta_layer.self_attn_layer_norm.weight
  lowercase__       :       Union[str, Any]				=						roberta_layer.self_attn_layer_norm.bias

  # self attention
  lowercase__       :       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)  )
  )

  lowercase__       :       Optional[Any]				=						roberta_layer.self_attn.q_proj.weight
  lowercase__       :       str				=						roberta_layer.self_attn.q_proj.bias
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.k_proj.weight
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.k_proj.bias
  lowercase__       :       int				=						roberta_layer.self_attn.v_proj.weight
  lowercase__       :       Union[str, Any]				=						roberta_layer.self_attn.v_proj.bias

  # self-attention output
  lowercase__       :       BertSelfOutput				=						layer.attention.output
  assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  lowercase__       :       Any				=						roberta_layer.self_attn.out_proj.weight
  lowercase__       :       Optional[int]				=						roberta_layer.self_attn.out_proj.bias

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

  # intermediate
  lowercase__       :       BertIntermediate				=						layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  lowercase__       :       Dict				=						roberta_layer.fca.weight
  lowercase__       :       Any				=						roberta_layer.fca.bias

  # output
  lowercase__       :       BertOutput				=						layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  lowercase__       :       Union[str, Any]				=						roberta_layer.fca.weight
  lowercase__       :       Optional[Any]				=						roberta_layer.fca.bias
  # end of layer

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

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

 lowercase__       :       Any				=						model(lowerCamelCase__  )[0]
 if classification_head:
  lowercase__       :       Optional[Any]				=						roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase__  )  )
 else:
  lowercase__       :       Tuple				=						roberta.model(lowerCamelCase__  )[0]
 print(our_output.shape       ,		their_output.shape  )
 lowercase__       :       Tuple				=						torch.max(torch.abs(our_output - their_output  )  ).item()
 print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
 lowercase__       :       int				=						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__":
   __snake_case    					=					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    					=					parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )