





import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")


def      lowerCamelCase_						(   lowerCAmelCase:						Optional[int]					)->		List[str]:
  _snake_case :					Any								=						test_file.split(os.path.sep					)
  if components[0:2] != ["tests", "models"]:
    raise ValueError(
        '`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
        F"""{test_file} instead."""					)
  _snake_case :					int								=						components[-1]
  if not test_fn.endswith('py'					):
    raise ValueError(F"""`test_file` should be a python file. Got {test_fn} instead."""					)
  if not test_fn.startswith('test_modeling_'					):
    raise ValueError(
        F"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""					)

  _snake_case :					Any								=						components[:-1] + [test_fn.replace('.py'     ,				''					)]
  _snake_case :					List[str]								=						".".join(SCREAMING_SNAKE_CASE__					)

  return test_module_path


def      lowerCamelCase_						(   lowerCAmelCase:						List[Any]					)->		Dict:
  _snake_case :					Optional[int]								=						get_module_path(SCREAMING_SNAKE_CASE__					)
  _snake_case :					List[Any]								=						importlib.import_module(SCREAMING_SNAKE_CASE__					)

  return test_module


def      lowerCamelCase_						(   lowerCAmelCase:						Union[str, Any]					)->		List[str]:
  _snake_case :					Union[str, Any]								=						[]
  _snake_case :					str								=						get_test_module(SCREAMING_SNAKE_CASE__					)
  for attr in dir(SCREAMING_SNAKE_CASE__					):
    if attr.endswith('ModelTester'					):
      tester_classes.append(getattr(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__					)					)

    # sort with class names
  return sorted(SCREAMING_SNAKE_CASE__     ,				key=lambda lowerCAmelCase					: x.__name__					)


def      lowerCamelCase_						(   lowerCAmelCase:						int					)->		Optional[int]:
  _snake_case :					Union[str, Any]								=						[]
  _snake_case :					Tuple								=						get_test_module(SCREAMING_SNAKE_CASE__					)
  for attr in dir(SCREAMING_SNAKE_CASE__					):
    _snake_case :					List[Any]								=						getattr(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__					)
    # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
    # `all_model_classes` is not empty (which also excludes other special classes).
    _snake_case :					Optional[Any]								=						getattr(SCREAMING_SNAKE_CASE__     ,				'all_model_classes'     ,				[]					)
    if len(SCREAMING_SNAKE_CASE__					) > 0:
      test_classes.append(SCREAMING_SNAKE_CASE__					)

    # sort with class names
  return sorted(SCREAMING_SNAKE_CASE__     ,				key=lambda lowerCAmelCase					: x.__name__					)


def      lowerCamelCase_						(   lowerCAmelCase:						int					)->		int:
  _snake_case :					Union[str, Any]								=						get_test_classes(SCREAMING_SNAKE_CASE__					)
  _snake_case :					Tuple								=						set()
  for test_class in test_classes:
    model_classes.update(test_class.all_model_classes					)

  # sort with class names
  return sorted(SCREAMING_SNAKE_CASE__     ,				key=lambda lowerCAmelCase					: x.__name__					)


def      lowerCamelCase_						(   lowerCAmelCase:						Tuple					)->		Any:
  _snake_case :					Optional[Any]								=						test_class()
  if hasattr(SCREAMING_SNAKE_CASE__     ,				'setUp'					):
    test.setUp()

  _snake_case :					int								=						None
  if hasattr(SCREAMING_SNAKE_CASE__     ,				'model_tester'					):
    # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
    if test.model_tester is not None:
      _snake_case :					List[str]								=						test.model_tester.__class__

  return model_tester


def      lowerCamelCase_						(   lowerCAmelCase:						Optional[int]     ,				lowerCAmelCase:						int					)->		Union[str, Any]:
  _snake_case :					str								=						get_test_classes(SCREAMING_SNAKE_CASE__					)

  _snake_case :					Optional[int]								=						[]
  for test_class in test_classes:
    if model_class in test_class.all_model_classes:
      target_test_classes.append(SCREAMING_SNAKE_CASE__					)

    # sort with class names
  return sorted(SCREAMING_SNAKE_CASE__     ,				key=lambda lowerCAmelCase					: x.__name__					)


def      lowerCamelCase_						(   lowerCAmelCase:						Optional[int]     ,				lowerCAmelCase:						List[Any]					)->		Optional[Any]:
  _snake_case :					str								=						get_test_classes_for_model(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__					)

  _snake_case :					int								=						[]
  for test_class in test_classes:
    _snake_case :					Optional[Any]								=						get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__					)
    if tester_class is not None:
      tester_classes.append(SCREAMING_SNAKE_CASE__					)

    # sort with class names
  return sorted(SCREAMING_SNAKE_CASE__     ,				key=lambda lowerCAmelCase					: x.__name__					)


def      lowerCamelCase_						(   lowerCAmelCase:						Any					)->		int:
  _snake_case :					Optional[Any]								=						get_test_classes(SCREAMING_SNAKE_CASE__					)
  _snake_case :					List[Any]								=						{test_class: get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__					) for test_class in test_classes}
  return test_tester_mapping


def      lowerCamelCase_						(   lowerCAmelCase:						Optional[int]					)->		Dict:
  _snake_case :					Optional[Any]								=						get_model_classes(SCREAMING_SNAKE_CASE__					)
  _snake_case :					int								=						{
      model_class: get_test_classes_for_model(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__					) for model_class in model_classes
  }
  return model_test_mapping


def      lowerCamelCase_						(   lowerCAmelCase:						Optional[int]					)->		Tuple:
  _snake_case :					Tuple								=						get_model_classes(SCREAMING_SNAKE_CASE__					)
  _snake_case :					Union[str, Any]								=						{
      model_class: get_tester_classes_for_model(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__					) for model_class in model_classes
  }
  return model_to_tester_mapping







def      lowerCamelCase_						(   lowerCAmelCase:						str					)->		List[str]:

  if isinstance(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__					):
    return o
  elif isinstance(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__					):
    return o.__name__
  elif isinstance(SCREAMING_SNAKE_CASE__     ,				(list, tuple)					):
    return [to_json(SCREAMING_SNAKE_CASE__					) for x in o]
  elif isinstance(SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__					):
    return {to_json(SCREAMING_SNAKE_CASE__					): to_json(SCREAMING_SNAKE_CASE__					) for k, v in o.items()}
  else:
    return o









"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase							:   Optional[int]				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{
    "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       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					'roberta'



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

           _UpperCAmelCase     :      str														=       vocab_size
           _UpperCAmelCase     :      Dict														=       hidden_size
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      Any														=       num_attention_heads
           _UpperCAmelCase     :      List[str]														=       hidden_act
           _UpperCAmelCase     :      Any														=       intermediate_size
           _UpperCAmelCase     :      Union[str, Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Tuple														=       max_position_embeddings
           _UpperCAmelCase     :      Union[str, Any]														=       type_vocab_size
           _UpperCAmelCase     :      str														=       initializer_range
           _UpperCAmelCase     :      int														=       layer_norm_eps
           _UpperCAmelCase     :      Tuple														=       position_embedding_type
           _UpperCAmelCase     :      int														=       use_cache
           _UpperCAmelCase     :      Dict														=       classifier_dropout







class       UpperCAmelCase_    (							_UpperCamelCase		):



      @property
      def 		snake_case_   (	self	:      Tuple		):
           if self.task == "multiple-choice":
                _UpperCAmelCase     :      Optional[Any]														=       {0: "batch", 1: "choice", 2: "sequence"}
           else:
                _UpperCAmelCase     :      List[Any]														=       {0: "batch", 1: "sequence"}
           return OrderedDict(
               [
                   ("input_ids", dynamic_axis),
                   ("attention_mask", dynamic_axis),
               ]		)
