"""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     :				Any    				=    "src/transformers"


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

__lowerCAmelCase     :				str    				=    transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCAmelCase     :				int    				=    {
    # 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		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = 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__   = 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
						):
								lowerCAmelCase__   = 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__   = 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__   = [
		    """bos_index""",
		    """eos_index""",
		    """pad_index""",
		    """unk_index""",
		    """mask_index""",
		    """image_size""",
		    """use_cache""",
		    """out_features""",
		    """out_indices""",
		]
		lowerCAmelCase__   = ["""encoder_no_repeat_ngram_size"""]

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

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

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

		return attribute_used or case_allowed







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




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

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

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

		lowerCAmelCase__   = []
		for config_param, default_value in zip(lowerCamelCase__      ,	lowerCamelCase__  ):
				# `attributes` here is all the variant names for `config_param`
				lowerCAmelCase__   = [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 				_UpperCAmelCase		(							):





		"""simple docstring"""




		lowerCAmelCase__   = {}
		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__   = [
				    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:
						lowerCAmelCase__   = check_config_attributes_being_used(lowerCamelCase__  )
						if len(lowerCamelCase__  ) > 0:
								lowerCAmelCase__   = unused_attributes

		if len(lowerCamelCase__  ) > 0:
				lowerCAmelCase__   = """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"""




import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


__lowerCAmelCase     :				str    				=    "__DUMMY_TRANSFORMERS_USER__"
__lowerCAmelCase     :				Dict    				=    "Dummy User"
__lowerCAmelCase     :				Optional[Any]    				=    "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"

__lowerCAmelCase     :				str    				=    "https://hub-ci.huggingface.co"
__lowerCAmelCase     :				int    				=    CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
__lowerCAmelCase     :				Dict    				=    CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
__lowerCAmelCase     :				Optional[Any]    				=    Path("~/.huggingface/hub_ci_token").expanduser()







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		monkeypatch.setattr(
		    """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""      ,	lowerCamelCase__  )







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""      ,	lowerCamelCase__  )
		monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""      ,	lowerCamelCase__  )







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""      ,	lowerCamelCase__  )







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		HfFolder.save_token(lowerCamelCase__  )
		yield
		HfFolder.delete_token()







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		return HfApi(endpoint=lowerCamelCase__  )







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = HfFolder.get_token()
		HfFolder.save_token(lowerCamelCase__  )
		yield CI_HUB_USER_TOKEN
		if previous_token is not None:
				HfFolder.save_token(lowerCamelCase__  )







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		def _cleanup_repo(lowerCamelCase__  ):
				hf_api.delete_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""  )

		return _cleanup_repo







@pytest.fixture
def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		@contextmanager
		def _temporary_repo(lowerCamelCase__  ):
				try:
						yield repo_id
				finally:
						cleanup_repo(lowerCamelCase__  )

		return _temporary_repo







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = f"""repo_txt_data-{int(time.time() * 10e3  )}"""
		lowerCAmelCase__   = f"""{CI_HUB_USER}/{repo_name}"""
		hf_api.create_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""      ,	private=lowerCamelCase__  )
		hf_api.upload_file(
		    token=lowerCamelCase__      ,	path_or_fileobj=str(lowerCamelCase__  )      ,	path_in_repo="""data/text_data.txt"""      ,	repo_id=lowerCamelCase__      ,	repo_type="""dataset"""      ,	)
		yield repo_id
		try:
				hf_api.delete_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""  )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return hf_private_dataset_repo_txt_data_







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = f"""repo_zipped_txt_data-{int(time.time() * 10e3  )}"""
		lowerCAmelCase__   = f"""{CI_HUB_USER}/{repo_name}"""
		hf_api.create_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""      ,	private=lowerCamelCase__  )
		hf_api.upload_file(
		    token=lowerCamelCase__      ,	path_or_fileobj=str(lowerCamelCase__  )      ,	path_in_repo="""data.zip"""      ,	repo_id=lowerCamelCase__      ,	repo_type="""dataset"""      ,	)
		yield repo_id
		try:
				hf_api.delete_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""  )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return hf_private_dataset_repo_zipped_txt_data_







@pytest.fixture(scope="""session"""  )
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = f"""repo_zipped_img_data-{int(time.time() * 10e3  )}"""
		lowerCAmelCase__   = f"""{CI_HUB_USER}/{repo_name}"""
		hf_api.create_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""      ,	private=lowerCamelCase__  )
		hf_api.upload_file(
		    token=lowerCamelCase__      ,	path_or_fileobj=str(lowerCamelCase__  )      ,	path_in_repo="""data.zip"""      ,	repo_id=lowerCamelCase__      ,	repo_type="""dataset"""      ,	)
		yield repo_id
		try:
				hf_api.delete_repo(lowerCamelCase__      ,	token=lowerCamelCase__      ,	repo_type="""dataset"""  )
		except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
				pass







@pytest.fixture()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return hf_private_dataset_repo_zipped_img_data_




