
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_    :			int    =	"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase_    :			Optional[Any]    =	direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCamelCase_    :			int    =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCamelCase_    :			Union[str, 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__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Tuple:
				UpperCamelCase_:				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
												):
																UpperCamelCase_:				Union[str, Any]						=    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}"'''  ,     SCREAMING_SNAKE_CASE_  ,     )
												    is not None
												):
																UpperCamelCase_:				List[str]						=    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_:				int						=    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_:				Tuple						=    [
				    """bos_index""",
				    """eos_index""",
				    """pad_index""",
				    """unk_index""",
				    """mask_index""",
				    """image_size""",
				    """use_cache""",
				    """out_features""",
				    """out_indices""",
				]
				UpperCamelCase_:				Any						=    ["""encoder_no_repeat_ngram_size"""]

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

												# Allow cases without checking the default value in the configuration class
												elif attribute in attributes_to_allow + attributes_used_in_generation:
																UpperCamelCase_:				Tuple						=    True
												elif attribute.endswith("""_token_id"""    ):
																UpperCamelCase_:				Optional[Any]						=    True

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

				return attribute_used or case_allowed







def 							A__       (    lowerCamelCase    )	->						Dict:
				UpperCamelCase_:				List[str]						=    dict(inspect.signature(config_class.__init__    ).parameters    )
				UpperCamelCase_:				Dict						=    [x for x in list(signature.keys()    ) if x not in ["""self""", """kwargs"""]]
				UpperCamelCase_:				Any						=    [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_:				List[str]						=    {}
				if len(config_class.attribute_map    ) > 0:
								UpperCamelCase_:				Optional[Any]						=    {v: k for k, v in config_class.attribute_map.items()}

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

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

				UpperCamelCase_:				int						=    []
				for config_param, default_value in zip(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_    ):
								# `attributes` here is all the variant names for `config_param`
								UpperCamelCase_:				List[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(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_    ):
												unused_attributes.append(attributes[0]    )

				return sorted(SCREAMING_SNAKE_CASE_    )







def 							A__       (    )	->						Dict:
				UpperCamelCase_:				Dict						=    {}
				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_:				Tuple						=    [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class    )  ,     lambda lowerCamelCase    : inspect.isclass(SCREAMING_SNAKE_CASE_    )
								        and issubclass(SCREAMING_SNAKE_CASE_  ,     SCREAMING_SNAKE_CASE_    )
								        and inspect.getmodule(SCREAMING_SNAKE_CASE_    ) == inspect.getmodule(_config_class    )  ,     )
								]
								for config_class in config_classes_in_module:
												UpperCamelCase_:				Union[str, Any]						=    check_config_attributes_being_used(SCREAMING_SNAKE_CASE_    )
												if len(SCREAMING_SNAKE_CASE_    ) > 0:
																UpperCamelCase_:				Any						=    unused_attributes

				if len(SCREAMING_SNAKE_CASE_    ) > 0:
								UpperCamelCase_:				Optional[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(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
							check_config_attributes()












'''simple docstring'''
import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		CTRLTokenizer
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						str)    ->							List[Any]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								A_									= ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))
								A_									= ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
								A_									= {'unk_token': '<unk>'}

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['merges_file'])
								with open(self.vocab_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write(json.dumps(_lowercase) + '\n')
								with open(self.merges_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write('\n'.join(_lowercase))



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								kwargs.update(self.special_tokens_map)
								return CTRLTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any])    ->							Dict:
								A_									= 'adapt react readapt apt'
								A_									= 'adapt react readapt apt'
								return input_text, output_text



					def     __snake_case						(	self     :						Any)    ->							Any:
								A_									= CTRLTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map)
								A_									= 'adapt react readapt apt'
								A_									= 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
								A_									= tokenizer.tokenize(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= tokens + [tokenizer.unk_token]

								A_									= [0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)      ,       _lowercase)




