



'''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
_lowerCAmelCase										=     '''src/transformers'''


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

_lowerCAmelCase										=     transformers.models.auto.configuration_auto.CONFIG_MAPPING

_lowerCAmelCase										=     {
    # 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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, 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
            ):
                lowerCAmelCase__			:  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}\""""							,   UpperCamelCase							,   )
                is not None
            ):
                lowerCAmelCase__			:  List[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:
                    lowerCAmelCase__			:  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
    lowerCAmelCase__			:  Tuple											= [
        """bos_index""",
        """eos_index""",
        """pad_index""",
        """unk_index""",
        """mask_index""",
        """image_size""",
        """use_cache""",
        """out_features""",
        """out_indices""",
    ]
    lowerCAmelCase__			:  str											= ["""encoder_no_repeat_ngram_size"""]

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

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

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

    return attribute_used or case_allowed
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





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

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

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

    lowerCAmelCase__			:  Optional[Any]											= []
    for config_param, default_value in zip(UpperCamelCase							,   UpperCamelCase      ):
        # `attributes` here is all the variant names for `config_param`
        lowerCAmelCase__			:  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(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):
            unused_attributes.append(attributes[0]      )

    return sorted(UpperCamelCase      )
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





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

    if len(UpperCamelCase      ) > 0:
        lowerCAmelCase__			:  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(UpperCamelCase      )


if __name__ == "__main__":
 check_config_attributes()







'''simple docstring'''





import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase = True							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = math.inf							,   UpperCamelCase = -math.inf							,   UpperCamelCase = False							,   UpperCamelCase = 100							,   UpperCamelCase = 0.01							,   UpperCamelCase = 1							,   ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= False
    lowerCAmelCase__			:  Optional[Any]											= search_prob
    lowerCAmelCase__			:  Tuple											= start_temperate
    lowerCAmelCase__			:  Optional[Any]											= []
    lowerCAmelCase__			:  str											= 0
    lowerCAmelCase__			:  Tuple											= None

    while not search_end:
        lowerCAmelCase__			:  List[str]											= current_state.score()
        if best_state is None or current_score > best_state.score():
            lowerCAmelCase__			:  List[str]											= current_state
        scores.append(UpperCamelCase      )
        iterations += 1
        lowerCAmelCase__			:  Dict											= None
        lowerCAmelCase__			:  List[Any]											= current_state.get_neighbors()
        while (
            next_state is None and neighbors
        ):  # till we do not find a neighbor that we can move to
            lowerCAmelCase__			:  Tuple											= random.randint(0							,   len(UpperCamelCase      ) - 1      )  # picking a random neighbor
            lowerCAmelCase__			:  str											= neighbors.pop(UpperCamelCase      )
            lowerCAmelCase__			:  Optional[Any]											= picked_neighbor.score() - current_score

            if (
                picked_neighbor.x > max_x
                or picked_neighbor.x < min_x
                or picked_neighbor.y > max_y
                or picked_neighbor.y < min_y
            ):
                continue  # neighbor outside our bounds

            if not find_max:
                lowerCAmelCase__			:  Union[str, Any]											= change * -1  # in case we are finding minimum
            if change > 0:  # improves the solution
                lowerCAmelCase__			:  Tuple											= picked_neighbor
            else:
                lowerCAmelCase__			:  List[Any]											= (math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    lowerCAmelCase__			:  List[Any]											= picked_neighbor
        lowerCAmelCase__			:  Optional[int]											= current_temp - (current_temp * rate_of_decrease)

        if current_temp < threshold_temp or next_state is None:
            # temperature below threshold, or could not find a suitable neighbor
            lowerCAmelCase__			:  str											= True
        else:
            lowerCAmelCase__			:  Optional[int]											= next_state

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(UpperCamelCase      )							,   UpperCamelCase      )
        plt.xlabel("""Iterations"""      )
        plt.ylabel("""Function values"""      )
        plt.show()
    return best_state


if __name__ == "__main__":
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





     return (x**2) + (y**2)

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(
     prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
     F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase										=     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(
     prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
 )
 print(
     '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
     F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
 )
 def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



     """simple docstring"""





     return (3 * x**2) - (6 * y)

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(prob, find_max=False, visualization=True)
 print(
     '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
     F"""{local_min.score()}"""
 )

 _lowerCAmelCase										=     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase										=     simulated_annealing(prob, find_max=True, visualization=True)




 print(
     '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
     F"""{local_min.score()}"""
 )


