'''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
SCREAMING_SNAKE_CASE_	   =			'src/transformers'


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

SCREAMING_SNAKE_CASE_	   =			transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE_	   =			{
    # 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  UpperCamelCase__					(					_lowercase    :				Tuple   ,    _lowercase    :				str   ,    _lowercase    :				str   ,    _lowercase    :				List[Any]     )							->	Any:
  __UpperCAmelCase:					int		= 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
      ):
        __UpperCAmelCase:					Tuple		= 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}"'''   ,    _lowercase   ,    )
          is not None
      ):
        __UpperCAmelCase:					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:
          __UpperCAmelCase:					str		= True
      if attribute_used:
        break
    if attribute_used:
      break

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

  # Special cases to be allowed
  __UpperCAmelCase:					Optional[int]		= True
  if not attribute_used:
    __UpperCAmelCase:					Tuple		= 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:
        __UpperCAmelCase:					int		= True
      elif attribute in ["tie_word_embeddings"] and default_value is False:
        __UpperCAmelCase:					str		= True

      # Allow cases without checking the default value in the configuration class
      elif attribute in attributes_to_allow + attributes_used_in_generation:
        __UpperCAmelCase:					Any		= True
      elif attribute.endswith("""_token_id"""     ):
        __UpperCAmelCase:					int		= True

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

  return attribute_used or case_allowed






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	List[Any]:
  __UpperCAmelCase:					Optional[int]		= dict(inspect.signature(config_class.__init__     ).parameters     )
  __UpperCAmelCase:					Optional[int]		= [x for x in list(signature.keys()     ) if x not in ["""self""", """kwargs"""]]
  __UpperCAmelCase:					Optional[int]		= [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
  __UpperCAmelCase:					Dict		= {}
  if len(config_class.attribute_map     ) > 0:
    __UpperCAmelCase:					Any		= {v: k for k, v in config_class.attribute_map.items()}

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

  # Get the source code strings
  __UpperCAmelCase:					Union[str, Any]		= []
  for path in modeling_paths:
    if os.path.isfile(_lowercase     ):
      with open(_lowercase     ) as fp:
        modeling_sources.append(fp.read()     )

  __UpperCAmelCase:					Tuple		= []
  for config_param, default_value in zip(_lowercase   ,    _lowercase     ):
    # `attributes` here is all the variant names for `config_param`
    __UpperCAmelCase:					List[Any]		= [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(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase     ):
      unused_attributes.append(attributes[0]     )

  return sorted(_lowercase     )






def  UpperCamelCase__					(					)							->	Any:
  __UpperCAmelCase:					List[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.)
    __UpperCAmelCase:					List[Any]		= [
        cls
        for name, cls in inspect.getmembers(
            inspect.getmodule(_config_class     )   ,    lambda _lowercase     : inspect.isclass(_lowercase     )
            and issubclass(_lowercase   ,    _lowercase     )
            and inspect.getmodule(_lowercase     ) == inspect.getmodule(_config_class     )   ,    )
    ]
    for config_class in config_classes_in_module:
      __UpperCAmelCase:					str		= check_config_attributes_being_used(_lowercase     )
      if len(_lowercase     ) > 0:
        __UpperCAmelCase:					Dict		= unused_attributes

  if len(_lowercase     ) > 0:
    __UpperCAmelCase:					List[Any]		= """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(_lowercase     )


if __name__ == "__main__":
    check_config_attributes()
'''simple docstring'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
SCREAMING_SNAKE_CASE_	   =			numpy.array([0, 0])
SCREAMING_SNAKE_CASE_	   =			numpy.array([0.5, 0.866_0254])
SCREAMING_SNAKE_CASE_	   =			numpy.array([1, 0])
SCREAMING_SNAKE_CASE_	   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]   ,    _lowercase    :				int     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					int		= initial_vectors
  for _ in range(_lowercase     ):
    __UpperCAmelCase:					Any		= iteration_step(_lowercase     )
  return vectors






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	list[numpy.ndarray]:
  __UpperCAmelCase:					Optional[int]		= []
  for i, start_vector in enumerate(vectors[:-1]     ):
    __UpperCAmelCase:					Union[str, Any]		= vectors[i + 1]
    new_vectors.append(_lowercase     )
    __UpperCAmelCase:					Optional[int]		= end_vector - start_vector
    new_vectors.append(start_vector + difference_vector / 3     )
    new_vectors.append(
        start_vector + difference_vector / 3 + rotate(difference_vector / 3   ,    6_0     )     )
    new_vectors.append(start_vector + difference_vector * 2 / 3     )
  new_vectors.append(vectors[-1]     )
  return new_vectors






def  UpperCamelCase__					(					_lowercase    :				numpy.ndarray   ,    _lowercase    :				float     )							->	numpy.ndarray:
  __UpperCAmelCase:					Tuple		= numpy.radians(_lowercase     )
  __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= numpy.cos(_lowercase     ), numpy.sin(_lowercase     )
  __UpperCAmelCase:					Tuple		= numpy.array(((c, -s), (s, c))     )
  return numpy.dot(_lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				list[numpy.ndarray]     )							->	None:
  __UpperCAmelCase:					Union[str, Any]		= plt.gca()
  axes.set_aspect("""equal"""     )

  # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
  # y-coordinates as inputs, which are constructed from the vector-list using
  # zip()
  __UpperCAmelCase, __UpperCAmelCase:					Dict		= zip(*_lowercase     )
  plt.plot(_lowercase   ,    _lowercase     )
  plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			iterate(INITIAL_VECTORS, 5)
    plot(processed_vectors)