





"""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 		__lowercase     (   snake_case_  :  Dict				,snake_case_  :  Tuple				,snake_case_  :  str				,snake_case_  :  List[str]						)   ->Optional[int]:



    '''simple docstring'''







    __A    :						List[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
            ):
                __A    :						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}\""""				,snake_case_				,)
                is not None
            ):
                __A    :						Tuple						 =     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:
                    __A    :						Optional[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
    __A    :						Tuple						 =     [
        '''bos_index''',
        '''eos_index''',
        '''pad_index''',
        '''unk_index''',
        '''mask_index''',
        '''image_size''',
        '''use_cache''',
        '''out_features''',
        '''out_indices''',
    ]
    __A    :						Union[str, Any]						 =     ['''encoder_no_repeat_ngram_size''']

    # Special cases to be allowed
    __A    :						int						 =     True
    if not attribute_used:
        __A    :						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:
                __A    :						Optional[Any]						 =     True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                __A    :						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:
                __A    :						int						 =     True
            elif attribute.endswith('''_token_id'''						):
                __A    :						Optional[int]						 =     True

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

    return attribute_used or case_allowed







def 		__lowercase     (   snake_case_  :  List[str]						)   ->Dict:



    '''simple docstring'''







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

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

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

    __A    :						Dict						 =     []
    for config_param, default_value in zip(snake_case_				,snake_case_						):
        # `attributes` here is all the variant names for `config_param`
        __A    :						List[str]						 =     [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(snake_case_				,snake_case_				,snake_case_				,snake_case_						):
            unused_attributes.append(attributes[0]						)

    return sorted(snake_case_						)







def 		__lowercase     (   )   ->Union[str, Any]:



    '''simple docstring'''







    __A    :						int						 =     {}
    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.)
        __A    :						Any						 =     [
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class						)				,lambda snake_case_						: inspect.isclass(snake_case_						)
                and issubclass(snake_case_				,snake_case_						)
                and inspect.getmodule(snake_case_						) == inspect.getmodule(_config_class						)				,)
        ]
        for config_class in config_classes_in_module:
            __A    :						List[str]						 =     check_config_attributes_being_used(snake_case_						)
            if len(snake_case_						) > 0:
                __A    :						List[str]						 =     unused_attributes

    if len(snake_case_						) > 0:
        __A    :						Optional[int]						 =     '''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(snake_case_						)


if __name__ == "__main__":
       check_config_attributes()











"""simple docstring"""



def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    if n == 0:
        return 0
    __A    :						int						 =     float('''-inf'''						)
    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max(
            snake_case_				,prices[i - 1] + naive_cut_rod_recursive(n - i				,snake_case_						)						)

    return max_revue







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->int:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    __A    :						Dict						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    return _top_down_cut_rod_recursive(snake_case_				,snake_case_				,snake_case_						)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        __A    :						Any						 =     float('''-inf'''						)
        for i in range(1				,n + 1						):
            __A    :						Union[str, Any]						 =     max(
                snake_case_				,prices[i - 1] + _top_down_cut_rod_recursive(n - i				,snake_case_				,snake_case_						)				,)

        __A    :						Any						 =     max_revenue

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    __A    :						Union[str, Any]						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    __A    :						List[Any]						 =     0

    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max_rev[i]
        for j in range(1				,i + 1						):
            __A    :						str						 =     max(snake_case_				,prices[j - 1] + max_rev[i - j]						)

        __A    :						List[str]						 =     max_revenue_i

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Union[str, Any]:



    '''simple docstring'''







    if n < 0:
        __A    :						Union[str, Any]						 =     F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(snake_case_						)

    if n > len(snake_case_						):
        __A    :						List[Any]						 =     (
            '''Each integral piece of rod must have a corresponding price. '''
            F"""Got n = {n} but length of prices = {len(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)







def 		__lowercase     (   )   ->str:



    '''simple docstring'''







    __A    :						Any						 =     [6, 10, 12, 15, 20, 23]
    __A    :						Union[str, Any]						 =     len(snake_case_						)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    __A    :						str						 =     36

    __A    :						Any						 =     top_down_cut_rod(snake_case_				,snake_case_						)
    __A    :						Any						 =     bottom_up_cut_rod(snake_case_				,snake_case_						)
    __A    :						Optional[Any]						 =     naive_cut_rod_recursive(snake_case_				,snake_case_						)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()




