



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
_snake_case		=  "src/transformers"


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

_snake_case		=  transformers.models.auto.configuration_auto.CONFIG_MAPPING

_snake_case		=  {
    # 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  ):





							'''simple docstring'''




							_lowerCAmelCase     :							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
																					):
																												_lowerCAmelCase     :							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}\""				, _lowerCamelCase				, )
																					    is not None
																					):
																												_lowerCAmelCase     :							Union[str, Any]    =    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     :							Optional[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
							_lowerCAmelCase     :							Tuple    =    [
							    "bos_index",
							    "eos_index",
							    "pad_index",
							    "unk_index",
							    "mask_index",
							    "image_size",
							    "use_cache",
							    "out_features",
							    "out_indices",
							]
							_lowerCAmelCase     :							List[str]    =    ["encoder_no_repeat_ngram_size"]

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

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

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

							return attribute_used or case_allowed






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




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

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

							# Get the source code strings
							_lowerCAmelCase     :							Union[str, Any]    =    []
							for path in modeling_paths:
														if os.path.isfile(_lowerCamelCase  ):
																					with open(_lowerCamelCase  ) as fp:
																												modeling_sources.append(fp.read()  )

							_lowerCAmelCase     :							List[str]    =    []
							for config_param, default_value in zip(_lowerCamelCase				, _lowerCamelCase  ):
														# `attributes` here is all the variant names for `config_param`
														_lowerCAmelCase     :							int    =    [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 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    {}
							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     :							Tuple    =    [
														    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     :							Optional[int]    =    check_config_attributes_being_used(_lowerCamelCase  )
																					if len(_lowerCamelCase  ) > 0:
																												_lowerCAmelCase     :							Dict    =    unused_attributes

							if len(_lowerCamelCase  ) > 0:
														_lowerCAmelCase     :							Dict    =    "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()





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'wav2vec2'







							def __init__(   self,			__a=32,			__a=768,			__a=12,			__a=12,			__a=3072,			__a="gelu",			__a=0.1,			__a=0.1,			__a=0.1,			__a=0.0,			__a=0.0,			__a=0.1,			__a=0.1,			__a=0.02,			__a=1E-5,			__a="group",			__a="gelu",			__a=(512, 512, 512, 512, 512, 512, 512),			__a=(5, 2, 2, 2, 2, 2, 2),			__a=(10, 3, 3, 3, 3, 2, 2),			__a=False,			__a=128,			__a=16,			__a=False,			__a=True,			__a=0.05,			__a=10,			__a=2,			__a=0.0,			__a=10,			__a=0,			__a=320,			__a=2,			__a=0.1,			__a=100,			__a=256,			__a=256,			__a=0.1,			__a="sum",			__a=False,			__a=False,			__a=256,			__a=(512, 512, 512, 512, 1500),			__a=(5, 3, 3, 1, 1),			__a=(1, 2, 3, 1, 1),			__a=512,			__a=0,			__a=1,			__a=2,			__a=False,			__a=3,			__a=2,			__a=3,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a,			pad_token_id=__a,			bos_token_id=__a,			eos_token_id=__a)
														_lowerCAmelCase     :							str    =    hidden_size
														_lowerCAmelCase     :							Optional[int]    =    feat_extract_norm
														_lowerCAmelCase     :							Union[str, Any]    =    feat_extract_activation
														_lowerCAmelCase     :							Optional[Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    conv_bias
														_lowerCAmelCase     :							str    =    num_conv_pos_embeddings
														_lowerCAmelCase     :							List[Any]    =    num_conv_pos_embedding_groups
														_lowerCAmelCase     :							str    =    len(self.conv_dim)
														_lowerCAmelCase     :							List[str]    =    num_hidden_layers
														_lowerCAmelCase     :							str    =    intermediate_size
														_lowerCAmelCase     :							Any    =    hidden_act
														_lowerCAmelCase     :							int    =    num_attention_heads
														_lowerCAmelCase     :							Optional[Any]    =    hidden_dropout
														_lowerCAmelCase     :							List[str]    =    attention_dropout
														_lowerCAmelCase     :							Tuple    =    activation_dropout
														_lowerCAmelCase     :							int    =    feat_proj_dropout
														_lowerCAmelCase     :							List[str]    =    final_dropout
														_lowerCAmelCase     :							int    =    layerdrop
														_lowerCAmelCase     :							int    =    layer_norm_eps
														_lowerCAmelCase     :							Union[str, Any]    =    initializer_range
														_lowerCAmelCase     :							str    =    vocab_size
														_lowerCAmelCase     :							Optional[Any]    =    do_stable_layer_norm
														_lowerCAmelCase     :							Any    =    use_weighted_layer_sum

														if (
														    (len(self.conv_stride) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel) != self.num_feat_extract_layers)
														    or (len(self.conv_dim) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																					    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																					    f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
																					    f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														_lowerCAmelCase     :							str    =    apply_spec_augment
														_lowerCAmelCase     :							Optional[Any]    =    mask_time_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_time_length
														_lowerCAmelCase     :							List[str]    =    mask_time_min_masks
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_length
														_lowerCAmelCase     :							List[str]    =    mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														_lowerCAmelCase     :							Union[str, Any]    =    num_codevectors_per_group
														_lowerCAmelCase     :							str    =    num_codevector_groups
														_lowerCAmelCase     :							Optional[int]    =    contrastive_logits_temperature
														_lowerCAmelCase     :							Optional[int]    =    feat_quantizer_dropout
														_lowerCAmelCase     :							Optional[int]    =    num_negatives
														_lowerCAmelCase     :							Union[str, Any]    =    codevector_dim
														_lowerCAmelCase     :							Any    =    proj_codevector_dim
														_lowerCAmelCase     :							Optional[int]    =    diversity_loss_weight

														# ctc loss
														_lowerCAmelCase     :							Tuple    =    ctc_loss_reduction
														_lowerCAmelCase     :							Tuple    =    ctc_zero_infinity

														# adapter
														_lowerCAmelCase     :							List[Any]    =    add_adapter
														_lowerCAmelCase     :							List[str]    =    adapter_kernel_size
														_lowerCAmelCase     :							str    =    adapter_stride
														_lowerCAmelCase     :							List[str]    =    num_adapter_layers
														_lowerCAmelCase     :							str    =    output_hidden_size or hidden_size
														_lowerCAmelCase     :							Tuple    =    adapter_attn_dim

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							Tuple    =    xvector_output_dim







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return functools.reduce(operator.mul,			self.conv_stride,			1)
