






"""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    			=       '''src/transformers'''


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

lowercase    			=       transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase    			=       {
    # 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 _lowerCAmelCase	(  __lowerCamelCase:List[str] ,						__lowerCamelCase:int ,						__lowerCamelCase:Union[str, Any] ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						__magic_name__            =     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
																		):
																								__magic_name__            =     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
																		):
																								__magic_name__            =     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:
																														__magic_name__            =     True
																		if attribute_used:
																								break
												if attribute_used:
																		break

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

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

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

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

						return attribute_used or case_allowed






def _lowerCAmelCase	(  __lowerCamelCase:str   ):

						'''simple docstring'''






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

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

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

						__magic_name__            =     []
						for config_param, default_value in zip(__lowerCamelCase ,						__lowerCamelCase   ):
												# `attributes` here is all the variant names for `config_param`
												__magic_name__            =     [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 _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     {}
						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.)
												__magic_name__            =     [
												    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:
																		__magic_name__            =     check_config_attributes_being_used(__lowerCamelCase   )
																		if len(__lowerCamelCase   ) > 0:
																								__magic_name__            =     unused_attributes

						if len(__lowerCamelCase   ) > 0:
												__magic_name__            =     "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()











"""simple docstring"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



