



'''simple docstring'''





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
__a          =		"src/transformers"


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

__a          =		transformers.models.auto.configuration_auto.CONFIG_MAPPING

__a          =		{
    # 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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[str]:
      snake_case__  :    List[str]    						=      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
                  ):
                        snake_case__  :    str    						=      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
                  ):
                        snake_case__  :    Optional[Any]    						=      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:
                              snake_case__  :    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
      snake_case__  :    List[Any]    						=      [
          """bos_index""",
          """eos_index""",
          """pad_index""",
          """unk_index""",
          """mask_index""",
          """image_size""",
          """use_cache""",
          """out_features""",
          """out_indices""",
      ]
      snake_case__  :    Union[str, Any]    						=      ["""encoder_no_repeat_ngram_size"""]

      # Special cases to be allowed
      snake_case__  :    Union[str, Any]    						=      True
      if not attribute_used:
            snake_case__  :    Any    						=      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:
                        snake_case__  :    Dict    						=      True
                  elif attribute in ["tie_word_embeddings"] and default_value is False:
                        snake_case__  :    Any    						=      True

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

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

      return attribute_used or case_allowed





def 						__snake_case(				_lowerCAmelCase		)		-> str:
      snake_case__  :    List[str]    						=      dict(inspect.signature(config_class.__init__		).parameters		)
      snake_case__  :    Tuple    						=      [x for x in list(signature.keys()		) if x not in ["""self""", """kwargs"""]]
      snake_case__  :    List[str]    						=      [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
      snake_case__  :    str    						=      {}
      if len(config_class.attribute_map		) > 0:
            snake_case__  :    Dict    						=      {v: k for k, v in config_class.attribute_map.items()}

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

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

      snake_case__  :    List[str]    						=      []
      for config_param, default_value in zip(_lowerCAmelCase     ,   _lowerCAmelCase		):
            # `attributes` here is all the variant names for `config_param`
            snake_case__  :    Tuple    						=      [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 						__snake_case(				)		-> List[str]:
      snake_case__  :    str    						=      {}
      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.)
            snake_case__  :    List[Any]    						=      [
                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:
                  snake_case__  :    Union[str, Any]    						=      check_config_attributes_being_used(_lowerCAmelCase		)
                  if len(_lowerCAmelCase		) > 0:
                        snake_case__  :    Union[str, Any]    						=      unused_attributes

      if len(_lowerCAmelCase		) > 0:
            snake_case__  :    str    						=      """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()


"""simple docstring"""





from typing import List, Optional, Union

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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Dict						   =		'''informer'''
		__lowercase							:  Union[str, Any]						   =		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''num_hidden_layers''': '''encoder_layers''',
		}




		def __init__(     self							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "student_t"							,  lowerCAmelCase__ = "nll"							,  lowerCAmelCase__ = 1							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = "mean"							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 6_4							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 3_2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = 2							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = "gelu"							,  lowerCAmelCase__ = 0.05							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 0.1							,  lowerCAmelCase__ = 1_0_0							,  lowerCAmelCase__ = 0.02							,  lowerCAmelCase__=True							,  lowerCAmelCase__ = "prob"							,  lowerCAmelCase__ = 5							,  lowerCAmelCase__ = True							,  **lowerCAmelCase__							,  ):
				# time series specific configuration
				__SCREAMING_SNAKE_CASE            = prediction_length
				__SCREAMING_SNAKE_CASE            = context_length or prediction_length
				__SCREAMING_SNAKE_CASE            = distribution_output
				__SCREAMING_SNAKE_CASE            = loss
				__SCREAMING_SNAKE_CASE            = input_size
				__SCREAMING_SNAKE_CASE            = num_time_features
				__SCREAMING_SNAKE_CASE            = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				__SCREAMING_SNAKE_CASE            = scaling
				__SCREAMING_SNAKE_CASE            = num_dynamic_real_features
				__SCREAMING_SNAKE_CASE            = num_static_real_features
				__SCREAMING_SNAKE_CASE            = num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
						if len(lowerCAmelCase__) != num_static_categorical_features:
								raise ValueError(
								    """The cardinality should be a list of the same length as `num_static_categorical_features`""")
						__SCREAMING_SNAKE_CASE            = cardinality
				else:
						__SCREAMING_SNAKE_CASE            = [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
						if len(lowerCAmelCase__) != num_static_categorical_features:
								raise ValueError(
								    """The embedding dimension should be a list of the same length as `num_static_categorical_features`""")
						__SCREAMING_SNAKE_CASE            = embedding_dimension
				else:
						__SCREAMING_SNAKE_CASE            = [min(5_0							,  (cat + 1) // 2) for cat in self.cardinality]

				__SCREAMING_SNAKE_CASE            = num_parallel_samples

				# Transformer architecture configuration
				__SCREAMING_SNAKE_CASE            = input_size * len(self.lags_sequence) + self._number_of_features
				__SCREAMING_SNAKE_CASE            = d_model
				__SCREAMING_SNAKE_CASE            = encoder_attention_heads
				__SCREAMING_SNAKE_CASE            = decoder_attention_heads
				__SCREAMING_SNAKE_CASE            = encoder_ffn_dim
				__SCREAMING_SNAKE_CASE            = decoder_ffn_dim
				__SCREAMING_SNAKE_CASE            = encoder_layers
				__SCREAMING_SNAKE_CASE            = decoder_layers

				__SCREAMING_SNAKE_CASE            = dropout
				__SCREAMING_SNAKE_CASE            = attention_dropout
				__SCREAMING_SNAKE_CASE            = activation_dropout
				__SCREAMING_SNAKE_CASE            = encoder_layerdrop
				__SCREAMING_SNAKE_CASE            = decoder_layerdrop

				__SCREAMING_SNAKE_CASE            = activation_function
				__SCREAMING_SNAKE_CASE            = init_std

				__SCREAMING_SNAKE_CASE            = use_cache

				# Informer
				__SCREAMING_SNAKE_CASE            = attention_type
				__SCREAMING_SNAKE_CASE            = sampling_factor
				__SCREAMING_SNAKE_CASE            = distil

				super().__init__(is_encoder_decoder=lowerCAmelCase__							,  **lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				return (
				    sum(self.embedding_dimension)
				    + self.num_dynamic_real_features
				    + self.num_time_features
				    + self.num_static_real_features
				    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
				)




