
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
_A     =  "src/transformers"


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

_A     =  transformers.models.auto.configuration_auto.CONFIG_MAPPING

_A     =  {
    # 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						:						Any					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple       ):




			"""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}\""""					,							lowercase__					,							)
									    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      lowerCamelCase__       (       __lowerCAmelCase						:						int       ):




			"""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(lowercase__       )
			lowerCAmelCase_      			=			os.path.dirname(lowercase__       )
			# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
			lowerCAmelCase_      			=			[os.path.join(lowercase__					,							lowercase__       ) for fn in os.listdir(lowercase__       ) if fn.startswith("modeling_"       )]

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

			lowerCAmelCase_      			=			[]
			for config_param, default_value in zip(lowercase__					,							lowercase__       ):
						# `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(lowercase__					,							lowercase__					,							lowercase__					,							lowercase__       ):
									unused_attributes.append(attributes[0]       )

			return sorted(lowercase__       )






def      lowerCamelCase__       (       ):




			"""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(lowercase__       )
						        and issubclass(lowercase__					,							lowercase__       )
						        and inspect.getmodule(lowercase__       ) == inspect.getmodule(_config_class       )					,							)
						]
						for config_class in config_classes_in_module:
									lowerCAmelCase_      			=			check_config_attributes_being_used(lowercase__       )
									if len(lowercase__       ) > 0:
												lowerCAmelCase_      			=			unused_attributes

			if len(lowercase__       ) > 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(lowercase__       )


if __name__ == "__main__":
			check_config_attributes()




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     =  logging.get_logger(__name__)

_A     =  {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''bert'''




				def __init__(      self       ,       _UpperCamelCase=30_522       ,       _UpperCamelCase=768       ,       _UpperCamelCase=12       ,       _UpperCamelCase=12       ,       _UpperCamelCase=3_072       ,       _UpperCamelCase="gelu"       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=512       ,       _UpperCamelCase=2       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=1e-1_2       ,       _UpperCamelCase=0       ,       _UpperCamelCase="absolute"       ,       _UpperCamelCase=True       ,       _UpperCamelCase=None       ,       **_UpperCamelCase       ,       )       ->   str:
							super().__init__(pad_token_id=_UpperCamelCase       ,       **_UpperCamelCase  )

							lowerCAmelCase_      			=			vocab_size
							lowerCAmelCase_      			=			hidden_size
							lowerCAmelCase_      			=			num_hidden_layers
							lowerCAmelCase_      			=			num_attention_heads
							lowerCAmelCase_      			=			hidden_act
							lowerCAmelCase_      			=			intermediate_size
							lowerCAmelCase_      			=			hidden_dropout_prob
							lowerCAmelCase_      			=			attention_probs_dropout_prob
							lowerCAmelCase_      			=			max_position_embeddings
							lowerCAmelCase_      			=			type_vocab_size
							lowerCAmelCase_      			=			initializer_range
							lowerCAmelCase_      			=			layer_norm_eps
							lowerCAmelCase_      			=			position_embedding_type
							lowerCAmelCase_      			=			use_cache
							lowerCAmelCase_      			=			classifier_dropout





class 			_lowerCAmelCase      (			__a ):




				@property
				def   __a		(      self  )       ->   Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_      			=			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_      			=			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							    ]  )



