
import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
  lowercase						:      Union[str, Any]							=							[]

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


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase						:      Any							=							{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[Any]							=							["""GPT2TokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							[
      """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPT2DoubleHeadsModel""",
      """GPT2ForQuestionAnswering""",
      """GPT2ForSequenceClassification""",
      """GPT2ForTokenClassification""",
      """GPT2LMHeadModel""",
      """GPT2Model""",
      """GPT2PreTrainedModel""",
      """load_tf_weights_in_gpt2""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      Tuple							=							[
      """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFGPT2DoubleHeadsModel""",
      """TFGPT2ForSequenceClassification""",
      """TFGPT2LMHeadModel""",
      """TFGPT2MainLayer""",
      """TFGPT2Model""",
      """TFGPT2PreTrainedModel""",
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[str]							=							["""TFGPT2Tokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

  lowercase						:      Union[str, Any]							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
