
import os

try:
   from .build_directory_md import good_file_paths
except ImportError:
   from build_directory_md import good_file_paths  # type: ignore

_A						      =				list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

_A						      =				[file for file in filepaths if file != file.lower()]
if upper_files:
   print(f"{len(upper_files)} files contain uppercase characters:")
   print("\n".join(upper_files) + "\n")

_A						      =				[file for file in filepaths if " " in file]
if space_files:
   print(f"{len(space_files)} files contain space characters:")
   print("\n".join(space_files) + "\n")

_A						      =				[file for file in filepaths if "-" in file]
if hyphen_files:
   print(f"{len(hyphen_files)} files contain hyphen characters:")
   print("\n".join(hyphen_files) + "\n")

_A						      =				[file for file in filepaths if os.sep not in file]
if nodir_files:
   print(f"{len(nodir_files)} files are not in a directory:")
   print("\n".join(nodir_files) + "\n")

_A						      =				len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
   import sys

   sys.exit(bad_files)



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Optional[int]     =	"roberta"





     def __init__(self   :					Union[str, Any]       ,					_A   :					List[Any]=5_0_2_6_5       ,					_A   :					Dict=7_6_8       ,					_A   :					Tuple=1_2       ,					_A   :					Optional[Any]=1_2       ,					_A   :					int=3_0_7_2       ,					_A   :					List[str]="gelu"       ,					_A   :					Tuple=0.1       ,					_A   :					Dict=0.1       ,					_A   :					Optional[int]=5_1_2       ,					_A   :					Dict=2       ,					_A   :					Optional[Any]=0.02       ,					_A   :					Optional[Any]=1E-12       ,					_A   :					str=1       ,					_A   :					Dict=0       ,					_A   :					Optional[int]=2       ,					_A   :					int="absolute"       ,					_A   :					Any=True       ,					_A   :					Union[str, Any]=None       ,					**_A   :					Optional[int]       ,					)		->		Tuple:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      vocab_size
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      hidden_act
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      max_position_embeddings
         snake_case				  =      type_vocab_size
         snake_case				  =      initializer_range
         snake_case				  =      layer_norm_eps
         snake_case				  =      position_embedding_type
         snake_case				  =      use_cache
         snake_case				  =      classifier_dropout







class   lowerCamelCase	(						A_						):





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
             snake_case				  =      {0: "batch", 1: "choice", 2: "sequence"}
         else:
             snake_case				  =      {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]     )

