


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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      int     		=		"switch_transformers"
   __A :      int     		=		["past_key_values"]
   __A :      Dict     		=		{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






   def __init__(		self						,				__A=3_2128						,				__A=768						,				__A=64						,				__A=2048						,				__A=64						,				__A=12						,				__A=3						,				__A=12						,				__A=3						,				__A=12						,				__A=8						,				__A=False						,				__A=0.01						,				__A="float32"						,				__A=False						,				__A=32						,				__A=128						,				__A=0.1						,				__A=1e-6						,				__A=0.001						,				__A=0.001						,				__A=1.0						,				__A="relu"						,				__A=True						,				__A=False						,				__A=True						,				__A=0						,				__A=1						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				vocab_size
     lowerCamelCase :					Any    =				d_model
     lowerCamelCase :					int    =				d_kv
     lowerCamelCase :					Union[str, Any]    =				d_ff

     lowerCamelCase :					str    =				num_sparse_encoder_layers

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

     lowerCamelCase :					Any    =				num_heads
     lowerCamelCase :					List[Any]    =				num_experts
     lowerCamelCase :					str    =				expert_capacity
     lowerCamelCase :					List[Any]    =				router_bias
     lowerCamelCase :					int    =				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 :					Tuple    =				router_dtype

     lowerCamelCase :					int    =				router_ignore_padding_tokens
     lowerCamelCase :					Union[str, Any]    =				relative_attention_num_buckets
     lowerCamelCase :					int    =				relative_attention_max_distance

     lowerCamelCase :					Tuple    =				dropout_rate
     lowerCamelCase :					List[str]    =				layer_norm_epsilon
     lowerCamelCase :					List[Any]    =				initializer_factor
     lowerCamelCase :					List[Any]    =				feed_forward_proj
     lowerCamelCase :					Optional[int]    =				use_cache
     lowerCamelCase :					int    =				add_router_probs

     lowerCamelCase :					List[Any]    =				router_z_loss_coef
     lowerCamelCase :					Union[str, Any]    =				router_aux_loss_coef

     lowerCamelCase :					Optional[int]    =				self.feed_forward_proj.split("-" )
     lowerCamelCase :					Tuple    =				act_info[-1]
     lowerCamelCase :					Union[str, Any]    =				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 :					Any    =				"gelu_new"

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







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if "model" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("model."      ,						""	)
  if "norm1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm1"      ,						"attention.output.LayerNorm"	)
  if "norm2" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("norm2"      ,						"output.LayerNorm"	)
  if "norm" in orig_key:
    lowerCamelCase :					Optional[Any]    =				orig_key.replace("norm"      ,						"LayerNorm"	)
  if "transformer" in orig_key:
    lowerCamelCase :					int    =				orig_key.split("."	)[0].split("_"	)[-1]
    lowerCamelCase :					Dict    =				orig_key.replace(f"""transformer_{layer_num}"""      ,						f"""encoder.layer.{layer_num}"""	)
  if "mha.attn" in orig_key:
    lowerCamelCase :					List[str]    =				orig_key.replace("mha.attn"      ,						"attention.self"	)
  if "mha" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mha"      ,						"attention"	)
  if "W_q" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("W_q"      ,						"self.query"	)
  if "W_k" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("W_k"      ,						"self.key"	)
  if "W_v" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("W_v"      ,						"self.value"	)
  if "ff1" in orig_key:
    lowerCamelCase :					Union[str, Any]    =				orig_key.replace("ff1"      ,						"intermediate.dense"	)
  if "ff2" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff2"      ,						"output.dense"	)
  if "ff" in orig_key:
    lowerCamelCase :					Optional[int]    =				orig_key.replace("ff"      ,						"output.dense"	)
  if "mlm_class" in orig_key:
    lowerCamelCase :					Dict    =				orig_key.replace("mlm.mlm_class"      ,						"cls.predictions.decoder"	)
  if "mlm" in orig_key:
    lowerCamelCase :					List[Any]    =				orig_key.replace("mlm"      ,						"cls.predictions.transform"	)
  if "cls" not in orig_key:
    lowerCamelCase :					int    =				"yoso." + orig_key

  return orig_key






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    lowerCamelCase :					List[str]    =				orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      lowerCamelCase :					Dict    =				val

  lowerCamelCase :					Dict    =				orig_state_dict["cls.predictions.decoder.bias"]
  lowerCamelCase :					Dict    =				torch.arange(SCREAMING_SNAKE_CASE_	).expand((1, -1)	) + 2

  return orig_state_dict






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					List[Any]    =				torch.load(SCREAMING_SNAKE_CASE_      ,						map_location="cpu"	)["model_state_dict"]
  lowerCamelCase :					List[str]    =				YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					Any    =				YosoForMaskedLM(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					List[Any]    =				convert_checkpoint_helper(config.max_position_embeddings      ,						SCREAMING_SNAKE_CASE_	)

  print(model.load_state_dict(SCREAMING_SNAKE_CASE_	)	)
  model.eval()
  model.save_pretrained(SCREAMING_SNAKE_CASE_	)

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The json file for YOSO model config.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _snake_case             =     parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



