
import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCamelCase					   ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowerCamelCase					   =direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowerCamelCase					   =transformers.models.auto.configuration_auto.CONFIG_MAPPING

_lowerCamelCase					   ={
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase		:	Any							=      False
 for attribute in attributes:
  for modeling_source in source_strings:
   # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
   if (
       F'''config.{attribute}''' in modeling_source
       or F'''getattr(config, "{attribute}"''' in modeling_source
       or F'''getattr(self.config, "{attribute}"''' in modeling_source
   ):
    lowerCamelCase		:	Optional[int]							=      True
   # Deal with multi-line cases
   elif (
       re.search(
           RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"''',	lowerCamelCase,	)
       is not None
   ):
    lowerCamelCase		:	str							=      True
   # `SequenceSummary` is called with `SequenceSummary(config)`
   elif attribute in [
       "summary_type",
       "summary_use_proj",
       "summary_activation",
       "summary_last_dropout",
       "summary_proj_to_labels",
       "summary_first_dropout",
   ]:
    if "SequenceSummary" in modeling_source:
     lowerCamelCase		:	Union[str, Any]							=      True
   if attribute_used:
    break
  if attribute_used:
   break

    # common and important attributes, even if they do not always appear in the modeling files
 lowerCamelCase		:	Tuple							=      [
     """bos_index""",
     """eos_index""",
     """pad_index""",
     """unk_index""",
     """mask_index""",
     """image_size""",
     """use_cache""",
     """out_features""",
     """out_indices""",
 ]
 lowerCamelCase		:	Optional[int]							=      ["""encoder_no_repeat_ngram_size"""]

 # Special cases to be allowed
 lowerCamelCase		:	Any							=      True
 if not attribute_used:
  lowerCamelCase		:	List[str]							=      False
  for attribute in attributes:
   # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
   if attribute in ["is_encoder_decoder"] and default_value is True:
    lowerCamelCase		:	List[str]							=      True
   elif attribute in ["tie_word_embeddings"] and default_value is False:
    lowerCamelCase		:	Union[str, Any]							=      True

   # Allow cases without checking the default value in the configuration class
   elif attribute in attributes_to_allow + attributes_used_in_generation:
    lowerCamelCase		:	List[Any]							=      True
   elif attribute.endswith("""_token_id"""							):
    lowerCamelCase		:	str							=      True

   # configuration class specific cases
   if not case_allowed:
    lowerCamelCase		:	Union[str, Any]							=      SPECIAL_CASES_TO_ALLOW.get(config_class.__name__,	[]							)
    lowerCamelCase		:	List[Any]							=      allowed_cases is True or attribute in allowed_cases

 return attribute_used or case_allowed





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dict(inspect.signature(config_class.__init__							).parameters							)
 lowerCamelCase		:	Any							=      [x for x in list(signature.keys()							) if x not in ["""self""", """kwargs"""]]
 lowerCamelCase		:	List[Any]							=      [signature[param].default for param in parameter_names]

 # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
 # as one variant is used, the test should pass
 lowerCamelCase		:	Optional[int]							=      {}
 if len(config_class.attribute_map							) > 0:
  lowerCamelCase		:	Union[str, Any]							=      {v: k for k, v in config_class.attribute_map.items()}

 # Get the path to modeling source files
 lowerCamelCase		:	Optional[Any]							=      inspect.getsourcefile(lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      os.path.dirname(lowerCamelCase							)
 # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
 lowerCamelCase		:	Optional[Any]							=      [os.path.join(lowerCamelCase,	lowerCamelCase							) for fn in os.listdir(lowerCamelCase							) if fn.startswith("""modeling_"""							)]

 # Get the source code strings
 lowerCamelCase		:	int							=      []
 for path in modeling_paths:
  if os.path.isfile(lowerCamelCase							):
   with open(lowerCamelCase							) as fp:
    modeling_sources.append(fp.read()							)

 lowerCamelCase		:	Union[str, Any]							=      []
 for config_param, default_value in zip(lowerCamelCase,	lowerCamelCase							):
  # `attributes` here is all the variant names for `config_param`
  lowerCamelCase		:	Optional[int]							=      [config_param]
  # some configuration classes have non-empty `attribute_map`, and both names could be used in the
  # corresponding modeling files. As long as one of them appears, it is fine.
  if config_param in reversed_attribute_map:
   attributes.append(reversed_attribute_map[config_param]							)

  if not check_attribute_being_used(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
   unused_attributes.append(attributes[0]							)

 return sorted(lowerCamelCase							)





def     _a					(						):
 lowerCamelCase		:	Tuple							=      {}
 for _config_class in list(CONFIG_MAPPING.values()							):
  # Skip deprecated models
  if "models.deprecated" in _config_class.__module__:
   continue
  # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
  lowerCamelCase		:	Optional[int]							=      [
      cls
      for name, cls in inspect.getmembers(
          inspect.getmodule(_config_class							),	lambda lowerCamelCase							: inspect.isclass(lowerCamelCase							)
          and issubclass(lowerCamelCase,	lowerCamelCase							)
          and inspect.getmodule(lowerCamelCase							) == inspect.getmodule(_config_class							),	)
  ]
  for config_class in config_classes_in_module:
   lowerCamelCase		:	str							=      check_config_attributes_being_used(lowerCamelCase							)
   if len(lowerCamelCase							) > 0:
    lowerCamelCase		:	Dict							=      unused_attributes

 if len(lowerCamelCase							) > 0:
  lowerCamelCase		:	Tuple							=      """The following configuration classes contain unused attributes in the corresponding modeling files:\n"""
  for name, attributes in configs_with_unused_attributes.items():
   error += F'''{name}: {attributes}\n'''

  raise ValueError(lowerCamelCase							)


if __name__ == "__main__":
      check_config_attributes()






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    List[Any]           =			"""gpt_neo"""
      _UpperCAmelCase				:    Union[str, Any]           =			["""past_key_values"""]
      _UpperCAmelCase				:    List[Any]           =			{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
      def __init__(      self    ,  __magic_name__=5_0_2_5_7    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_4    ,  __magic_name__=[[["global", "local"], 1_2]]    ,  __magic_name__=1_6    ,  __magic_name__=None    ,  __magic_name__=2_5_6    ,  __magic_name__="gelu_new"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=1e-5    ,  __magic_name__=0.02    ,  __magic_name__=True    ,  __magic_name__=5_0_2_5_6    ,  __magic_name__=5_0_2_5_6    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	List[Any]							=      vocab_size
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Optional[int]							=      num_layers
       lowerCamelCase		:	str							=      num_heads
       lowerCamelCase		:	Optional[Any]							=      intermediate_size
       lowerCamelCase		:	List[Any]							=      window_size
       lowerCamelCase		:	int							=      activation_function
       lowerCamelCase		:	Union[str, Any]							=      resid_dropout
       lowerCamelCase		:	List[Any]							=      embed_dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Dict							=      classifier_dropout
       lowerCamelCase		:	Any							=      layer_norm_epsilon
       lowerCamelCase		:	Dict							=      initializer_range
       lowerCamelCase		:	Dict							=      use_cache

       lowerCamelCase		:	Optional[Any]							=      bos_token_id
       lowerCamelCase		:	int							=      eos_token_id

       lowerCamelCase		:	List[Any]							=      attention_types
       lowerCamelCase		:	Optional[Any]							=      self.expand_attention_types_params(__magic_name__      )

       if len(self.attention_layers      ) != self.num_layers:
        raise ValueError(
            """Configuration for convolutional module is incorrect. """
            """It is required that `len(config.attention_layers)` == `config.num_layers` """
            F'''but is `len(config.attention_layers) = {len(self.attention_layers      )}`, '''
            F'''`config.num_layers = {self.num_layers}`. '''
            """`config.attention_layers` is prepared using `config.attention_types`. """
            """Please verify the value of `config.attention_types` argument."""      )

       super().__init__(bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )






      @staticmethod
      def 						UpperCamelCase__							(      __magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      []
       for item in attention_types:
        for _ in range(item[1]      ):
         attentions.extend(item[0]      )
       return attentions





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	Any							=      input.size()
 lowerCamelCase		:	List[Any]							=      len(lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      shape[dimension]

 lowerCamelCase		:	Optional[int]							=      torch.arange(0,	lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	Dict							=      torch.div(sizedim - size,	lowerCamelCase,	rounding_mode="""floor"""							) + 1
 lowerCamelCase		:	int							=      torch.arange(lowerCamelCase							) + low_indices[:min_length][:, None]

 lowerCamelCase		:	str							=      [slice(lowerCamelCase							)] * rank
 lowerCamelCase		:	List[str]							=      indices
 lowerCamelCase		:	Dict							=      input[s]

 lowerCamelCase		:	Any							=      list(range(0,	rank + 1							)							)
 perm.append(perm.pop(dimension + 1							)							)

 return sliced.permute(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	List[Any]							=      torch.arange(1,	lowerCamelCase							)
 lowerCamelCase		:	Optional[int]							=      torch.remainder(lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      remainders == 0
 lowerCamelCase		:	List[Any]							=      candidates[divisor_indices]
 lowerCamelCase		:	Optional[Any]							=      torch.max(lowerCamelCase							)
 return largest_divisor, torch.div(lowerCamelCase,	lowerCamelCase,	rounding_mode="""floor"""							)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
       if self.use_past:
        self.fill_with_past_key_values_(__magic_name__    ,  direction="""inputs"""      )
        lowerCamelCase		:	int							=      {0: """batch""", 1: """past_sequence + sequence"""}
       else:
        lowerCamelCase		:	Tuple							=      {0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def 						UpperCamelCase__							(      self      ):
       return self._config.num_heads
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = -1    ,  __magic_name__ = -1    ,  __magic_name__ = False    ,  __magic_name__ = None    ,  ):
       lowerCamelCase		:	Optional[int]							=      super(__magic_name__    ,  self      ).generate_dummy_inputs(
           __magic_name__    ,  batch_size=__magic_name__    ,  seq_length=__magic_name__    ,  is_pair=__magic_name__    ,  framework=__magic_name__      )

       # We need to order the input in the way they appears in the forward()
       lowerCamelCase		:	int							=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
        else:
         import torch

         lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         lowerCamelCase		:	Optional[int]							=      seqlen + 2
         lowerCamelCase		:	List[Any]							=      (
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         lowerCamelCase		:	str							=      [
             (torch.zeros(__magic_name__      ), torch.zeros(__magic_name__      )) for _ in range(self.num_layers      )
         ]

       lowerCamelCase		:	Tuple							=      common_inputs["""attention_mask"""]
       if self.use_past:
        lowerCamelCase		:	str							=      ordered_inputs["""attention_mask"""].dtype
        lowerCamelCase		:	Any							=      torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(__magic_name__    ,  __magic_name__    ,  dtype=__magic_name__      )]    ,  dim=1      )

       return ordered_inputs






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_3




