


from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
    "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE      :			int      				=   [
     "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXJapaneseForCausalLM",
     "GPTNeoXJapaneseLayer",
     "GPTNeoXJapaneseModel",
     "GPTNeoXJapanesePreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 _SCREAMING_SNAKE_CASE      :			Tuple      				=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








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


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'mgp-str'
    def __init__(	self      , __snake_case=[3_2, 1_2_8]      , __snake_case=4      , __snake_case=3      , __snake_case=2_7      , __snake_case=3_8      , __snake_case=5_0_2_5_7      , __snake_case=3_0_5_2_2      , __snake_case=7_6_8      , __snake_case=1_2      , __snake_case=1_2      , __snake_case=4.0      , __snake_case=True      , __snake_case=False      , __snake_case=1E-5      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=False      , __snake_case=0.02      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    image_size
         snake_case					=    patch_size
         snake_case					=    num_channels
         snake_case					=    max_token_length
         snake_case					=    num_character_labels
         snake_case					=    num_bpe_labels
         snake_case					=    num_wordpiece_labels
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    mlp_ratio
         snake_case					=    distilled
         snake_case					=    layer_norm_eps
         snake_case					=    drop_rate
         snake_case					=    qkv_bias
         snake_case					=    attn_drop_rate
         snake_case					=    drop_path_rate
         snake_case					=    output_aa_attentions
         snake_case					=    initializer_range




