
'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/electra-small-generator": "https://huggingface.co/google/electra-small-generator/resolve/main/config.json",
    "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/config.json",
    "google/electra-large-generator": "https://huggingface.co/google/electra-large-generator/resolve/main/config.json",
    "google/electra-small-discriminator": (
        "https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"
    ),
    "google/electra-base-discriminator": (
        "https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"
    ),
    "google/electra-large-discriminator": (
        "https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"
    ),
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''electra'''




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3_05_22,     SCREAMING_SNAKE_CASE_						:					Tuple=1_28,     SCREAMING_SNAKE_CASE_						:					int=2_56,     SCREAMING_SNAKE_CASE_						:					List[Any]=12,     SCREAMING_SNAKE_CASE_						:					Optional[int]=4,     SCREAMING_SNAKE_CASE_						:					List[str]=10_24,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Optional[int]=0.1,     SCREAMING_SNAKE_CASE_						:					Dict=0.1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=5_12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=2,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					int=1e-12,     SCREAMING_SNAKE_CASE_						:					str="first",     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					str=0.1,     SCREAMING_SNAKE_CASE_						:					int=0,     SCREAMING_SNAKE_CASE_						:					List[str]="absolute",     SCREAMING_SNAKE_CASE_						:					List[Any]=True,     SCREAMING_SNAKE_CASE_						:					Dict=None,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )

										snake_case							:  Dict			=     vocab_size
										snake_case							:  Optional[Any]			=     embedding_size
										snake_case							:  Optional[Any]			=     hidden_size
										snake_case							:  Tuple			=     num_hidden_layers
										snake_case							:  Union[str, Any]			=     num_attention_heads
										snake_case							:  str			=     intermediate_size
										snake_case							:  Any			=     hidden_act
										snake_case							:  Optional[Any]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Union[str, Any]			=     max_position_embeddings
										snake_case							:  str			=     type_vocab_size
										snake_case							:  List[Any]			=     initializer_range
										snake_case							:  int			=     layer_norm_eps

										snake_case							:  Any			=     summary_type
										snake_case							:  Tuple			=     summary_use_proj
										snake_case							:  Optional[Any]			=     summary_activation
										snake_case							:  Optional[int]			=     summary_last_dropout
										snake_case							:  List[str]			=     position_embedding_type
										snake_case							:  List[Any]			=     use_cache
										snake_case							:  List[str]			=     classifier_dropout




class     a (					__magic_name__     ):




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


'''simple docstring'''





import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
      UpperCamelCase       :     List[Any]			=    []

      UpperCamelCase       :     Tuple			=    os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
      UpperCamelCase       :     List[str]			=    glob.glob(pattern)
      # TODO: deal with TF/Flax too
      UpperCamelCase       :     List[Any]			=    [
          x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
      ]

      for test_file in test_files:
            UpperCamelCase       :     List[str]			=    get_tester_classes(test_file)
            for tester_class in tester_classes:
                  # A few tester classes don't have `parent` parameter in `__init__`.
                  # TODO: deal this better
                  try:
                        UpperCamelCase       :     int			=    tester_class(parent=None)
                  except Exception:
                        continue
                  if hasattr(tester, """get_config"""):
                        UpperCamelCase       :     Union[str, Any]			=    tester.get_config()
                        for k, v in config.to_dict().items():
                              if isinstance(v, int):
                                    UpperCamelCase       :     List[Any]			=    None
                                    if k in ["vocab_size"]:
                                          UpperCamelCase       :     List[Any]			=    100
                                    elif k in ["max_position_embeddings"]:
                                          UpperCamelCase       :     Tuple			=    128
                                    elif k in ["hidden_size", "d_model"]:
                                          UpperCamelCase       :     Any			=    40
                                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                          UpperCamelCase       :     Tuple			=    5
                                    if target is not None and v > target:
                                          failures.append(
                                              f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                              f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                              f''' than {target}.'''
                                          )

      if len(failures) > 0:
            raise Exception(f'''There were {len(failures)} failures:\n''' + """\n""".join(failures))

