



'''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()


import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""simple docstring"""



				assert np.shape(a_						)[0] == np.shape(a_						)[1]
				# Ensure proper dimensionality.
				assert np.shape(a_						)[0] == np.shape(a_						)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(a_						) == np.iscomplexobj(a_						)
				__A   			= np.iscomplexobj(a_						)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(a_		,   input_matrix.conj().T						)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				__A   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

				while not convergence:
								# Multiple matrix by the vector.
								__A   			= np.dot(a_		,   a_						)
								# Normalize the resulting output vector.
								__A   			= w / np.linalg.norm(a_						)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								__A   			= vector.conj().T if is_complex else vector.T
								__A   			= np.dot(a_		,   np.dot(a_		,   a_						)						)

								# Check convergence.
								__A   			= np.abs(lambda_ - lambda_previous						) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												__A   			= True

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__A   			= real_input_matrix
												__A   			= real_vector
								elif problem_type == "complex":
												__A   			= complex_input_matrix
												__A   			= complex_vector

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__A			,							__A   			= np.linalg.eigh(a_						)
								# Last eigenvalue is the maximum one.
								__A   			= eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__A   			= eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max						) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(a_						) - np.abs(a_						)						) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()
