





'''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
lowercase__						:      List[Any]	=    'src/transformers'


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

lowercase__						:      Any	=    transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase__						:      Optional[Any]	=    {
    # 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__    (    lowercase :					Optional[Any], lowercase :					List[str], lowercase :					Optional[Any], lowercase :					Tuple				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	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       =	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       =	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       =	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       =	[
         '''bos_index''',
         '''eos_index''',
         '''pad_index''',
         '''unk_index''',
         '''mask_index''',
         '''image_size''',
         '''use_cache''',
         '''out_features''',
         '''out_indices''',
     ]
     _UpperCamelCase       =	['''encoder_no_repeat_ngram_size''']

     # Special cases to be allowed
     _UpperCamelCase       =	True
     if not attribute_used:
          _UpperCamelCase       =	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       =	True
               elif attribute in ["tie_word_embeddings"] and default_value is False:
                    _UpperCamelCase       =	True

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

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

     return attribute_used or case_allowed







def  a__    (    lowercase :					Tuple				)		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	dict(inspect.signature(config_class.__init__				).parameters				)
     _UpperCamelCase       =	[x for x in list(signature.keys()				) if x not in ['''self''', '''kwargs''']]
     _UpperCamelCase       =	[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       =	{}
     if len(config_class.attribute_map				) > 0:
          _UpperCamelCase       =	{v: k for k, v in config_class.attribute_map.items()}

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

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

     _UpperCamelCase       =	[]
     for config_param, default_value in zip(lowercase, lowercase				):
          # `attributes` here is all the variant names for `config_param`
          _UpperCamelCase       =	[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  a__    (    )		->		str:





     """simple docstring"""

     _UpperCamelCase       =	{}
     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       =	[
              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       =	check_config_attributes_being_used(lowercase				)
               if len(lowercase				) > 0:
                    _UpperCamelCase       =	unused_attributes

     if len(lowercase				) > 0:
          _UpperCamelCase       =	'''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







def  a__    (    lowercase :					list[int], lowercase :					list[int], lowercase :					int				)		->		tuple[float, list[float]]:





     """simple docstring"""

     _UpperCamelCase       =	list(range(len(lowercase				)				)				)
     _UpperCamelCase       =	[v / w for v, w in zip(lowercase, lowercase				)]
     index.sort(key=lambda lowercase				: ratio[i], reverse=lowercase				)

     _UpperCamelCase       =	0
     _UpperCamelCase       =	[0] * len(lowercase				)
     for i in index:
          if weight[i] <= capacity:
               _UpperCamelCase       =	1
               max_value += value[i]
               capacity -= weight[i]
          else:
               _UpperCamelCase       =	capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
   import doctest

   doctest.testmod()



