
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	:    Union[str, Any]          =      direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowerCamelCase	:    Union[str, Any]          =      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_		(	__lowercase       :     List[Any]	, __lowercase       :     Optional[int]	, __lowercase       :     str	, __lowercase       :     Dict			)			->  Union[str, Any]:






			"""simple docstring"""




			_snake_case          =      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
									):
												_snake_case          =      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
									):
												_snake_case          =      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:
															_snake_case          =      True
									if attribute_used:
												break
						if attribute_used:
									break

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

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

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

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

			return attribute_used or case_allowed






def 	a_		(	__lowercase       :     Tuple			)			->  List[Any]:






			"""simple docstring"""




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

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

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

			_snake_case          =      []
			for config_param, default_value in zip(__A	, __A			):
						# `attributes` here is all the variant names for `config_param`
						_snake_case          =      [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 	a_		(	)			->  Any:






			"""simple docstring"""




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

			if len(__A			) > 0:
						_snake_case          =      '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()

from __future__ import annotations

import requests

_lowerCamelCase	:    List[str]          =      set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)






def 	a_		(	__lowercase       :     str	, __lowercase       :     int = 1	, __lowercase       :     str = "new"	, __lowercase       :     list | None = None			)			->  dict:
			_snake_case          =      wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(__lowercase			) - valid_terms			)			):
						_snake_case          =      f'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(__lowercase			)
			_snake_case          =      requests.get(
			    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''	, headers={'User-agent': 'A random string'}	, )
			if response.status_code == 429:
						raise requests.HTTPError

			_snake_case          =      response.json()
			if not wanted_data:
						return {id_: data["data"]["children"][id_] for id_ in range(__lowercase			)}

			_snake_case          =      {}
			for id_ in range(__lowercase			):
						_snake_case          =      {
						    item: data['data']['children'][id_]['data'][item] for item in wanted_data
						}
			return data_dict


if __name__ == "__main__":
			# If you get Error 429, that means you are rate limited.Try after some time
			print(get_subreddit_data('''learnpython''', wanted_data=['''title''', '''url''', '''selftext''']))