





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


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							:      Optional[int]         =    {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Union[str, Any]  		=					"""switch_transformers"""
   _UpperCamelCase			:    Union[str, Any]  		=					["""past_key_values"""]
   _UpperCamelCase			:    List[str]  		=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





   def __init__(			self       ,      snake_case=32_128       ,      snake_case=768       ,      snake_case=64       ,      snake_case=2_048       ,      snake_case=64       ,      snake_case=12       ,      snake_case=3       ,      snake_case=12       ,      snake_case=3       ,      snake_case=12       ,      snake_case=8       ,      snake_case=False       ,      snake_case=0.01       ,      snake_case="float32"       ,      snake_case=False       ,      snake_case=32       ,      snake_case=128       ,      snake_case=0.1       ,      snake_case=1E-6       ,      snake_case=0.001       ,      snake_case=0.001       ,      snake_case=1.0       ,      snake_case="relu"       ,      snake_case=True       ,      snake_case=False       ,      snake_case=True       ,      snake_case=0       ,      snake_case=1       ,      **snake_case       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	vocab_size
      a__						:    Dict   		=	d_model
      a__						:    List[Any]   		=	d_kv
      a__						:    Tuple   		=	d_ff

      a__						:    Any   		=	num_sparse_encoder_layers

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

      a__						:    Optional[int]   		=	num_heads
      a__						:    Union[str, Any]   		=	num_experts
      a__						:    Any   		=	expert_capacity
      a__						:    Tuple   		=	router_bias
      a__						:    Union[str, 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}"""   )
      a__						:    Optional[int]   		=	router_dtype

      a__						:    str   		=	router_ignore_padding_tokens
      a__						:    Any   		=	relative_attention_num_buckets
      a__						:    Optional[int]   		=	relative_attention_max_distance

      a__						:    int   		=	dropout_rate
      a__						:    str   		=	layer_norm_epsilon
      a__						:    int   		=	initializer_factor
      a__						:    Dict   		=	feed_forward_proj
      a__						:    Dict   		=	use_cache
      a__						:    Optional[Any]   		=	add_router_probs

      a__						:    List[str]   		=	router_z_loss_coef
      a__						:    str   		=	router_aux_loss_coef

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

      if len(snake_case   ) > 1 and act_info[0] != "gated" or len(snake_case   ) > 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":
         a__						:    Union[str, Any]   		=	"gelu_new"

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








import argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

