
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
SCREAMING_SNAKE_CASE				:Dict									=							'src/transformers'


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

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

SCREAMING_SNAKE_CASE				: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 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_		)						->      str:







				"""simple docstring"""



				__A       =					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       =					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}"'''						,  a_						,  )
												    is not None
												):
																__A       =					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       =					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       =					[
				    "bos_index",
				    "eos_index",
				    "pad_index",
				    "unk_index",
				    "mask_index",
				    "image_size",
				    "use_cache",
				    "out_features",
				    "out_indices",
				]
				__A       =					["encoder_no_repeat_ngram_size"]

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

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

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

				return attribute_used or case_allowed


def 		UpperCAmelCase     (      a_		)						->      Dict:







				"""simple docstring"""



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

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

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

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

				return sorted(a_		)


def 		UpperCAmelCase     (      )						->      Optional[Any]:







				"""simple docstring"""



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

				if len(a_		) > 0:
								__A       =					"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(a_		)


if __name__ == "__main__":
					check_config_attributes()




'''simple docstring'''







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     RoFormerTokenizer
     __magic_name__           =     RoFormerTokenizerFast
     __magic_name__           =     True
     __magic_name__           =     True







     def a						(			self ):

            '''simple docstring'''

            super().setUp()







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							'永和服装饰品有限公司,今天天气非常好'
            _lowerCAmelCase     :				str       =							'永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
            return input_text, output_text







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				List[Any]       =							tokenizer.tokenize(snake_case__ )

            self.assertListEqual(snake_case__   ,						output_text.split() )

            _lowerCAmelCase     :				Optional[Any]       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[str]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.get_rust_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				Optional[int]       =							tokenizer.tokenize(snake_case__ )
            self.assertListEqual(snake_case__   ,						output_text.split() )
            _lowerCAmelCase     :				Tuple       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[Any]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass

