




"""simple docstring"""




import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
  __A       :						List[str]												=			[]

  __A       :						int												=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
  __A       :						List[Any]												=			glob.glob(pattern)
  # TODO: deal with TF/Flax too
  __A       :						int												=			[
      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:
    __A       :						Optional[int]												=			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:
        __A       :						Dict												=			tester_class(parent=None)
      except Exception:
        continue
      if hasattr(tester, '''get_config'''):
        __A       :						Optional[Any]												=			tester.get_config()
        for k, v in config.to_dict().items():
          if isinstance(v, int):
            __A       :						Tuple												=			None
            if k in ["vocab_size"]:
              __A       :						Optional[int]												=			100
            elif k in ["max_position_embeddings"]:
              __A       :						str												=			128
            elif k in ["hidden_size", "d_model"]:
              __A       :						Optional[Any]												=			40
            elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
              __A       :						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))












import copy

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


__A          = logging.get_logger(__name__)

__A          = {}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt_decoder"

							def __init__(     self:      List[Any]						,   __A:      Dict=0						,   __A:      str=0      )			-> Optional[int]:
												super().__init__()
												_A									=       vocab_size
												_A									=       bos_token_id





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "fsmt"
							A_       =    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(     self:      Optional[Any]						,   __A:      Optional[Any]=["en", "de"]						,   __A:      List[str]=4_20_24						,   __A:      Any=4_20_24						,   __A:      Optional[int]="relu"						,   __A:      List[str]=10_24						,   __A:      str=2_00						,   __A:      Tuple=10_24						,   __A:      List[str]=40_96						,   __A:      List[Any]=12						,   __A:      Union[str, Any]=16						,   __A:      List[str]=0.0						,   __A:      int=40_96						,   __A:      Union[str, Any]=12						,   __A:      List[str]=16						,   __A:      Union[str, Any]=0.0						,   __A:      int=0.0						,   __A:      str=0.1						,   __A:      int=0.0						,   __A:      Union[str, Any]=0.02						,   __A:      Tuple=2						,   __A:      int=True						,   __A:      Dict=True						,   __A:      int=False						,   __A:      Any=5						,   __A:      Tuple=1.0						,   __A:      Optional[int]=False						,   __A:      List[Any]=True						,   __A:      Tuple=1						,   __A:      List[Any]=0						,   __A:      Any=2						,   __A:      Optional[Any]=2						,   **__A:      Optional[Any]						,   )			-> Tuple:
												_A									=       langs
												_A									=       src_vocab_size
												_A									=       tgt_vocab_size
												_A									=       d_model  # encoder_embed_dim and decoder_embed_dim

												_A									=       encoder_ffn_dim
												_A									=       _A									=       encoder_layers
												_A									=       encoder_attention_heads
												_A									=       encoder_layerdrop
												_A									=       decoder_layerdrop
												_A									=       decoder_ffn_dim
												_A									=       decoder_layers
												_A									=       decoder_attention_heads
												_A									=       max_position_embeddings
												_A									=       init_std  # Normal(0, this parameter)
												_A									=       activation_function

												_A									=       DecoderConfig(vocab_size=__A						,   bos_token_id=__A      )
												if "decoder" in common_kwargs:
																	del common_kwargs["decoder"]

												_A									=       scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												_A									=       attention_dropout
												_A									=       activation_dropout
												_A									=       dropout

												_A									=       use_cache
												super().__init__(
												    pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   decoder_start_token_id=__A						,   is_encoder_decoder=__A						,   tie_word_embeddings=__A						,   forced_eos_token_id=__A						,   max_length=__A						,   num_beams=__A						,   length_penalty=__A						,   early_stopping=__A						,   **__A						,   )



							def 					__A       (     self:      Optional[int]      )			-> List[Any]:
												_A									=       copy.deepcopy(self.__dict__      )
												_A									=       self.decoder.to_dict()
												_A									=       self.__class__.model_type
												return output



