




"""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
UpperCamelCase__						=			'''src/transformers'''


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

UpperCamelCase__						=			transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCamelCase__						=			{
    # 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		(		snake_case			:					Union[str, Any]      ,       snake_case			:					Dict      ,       snake_case			:					int      ,       snake_case			:					Union[str, Any]    ):
					_lowerCAmelCase:Union[str, 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:List[str]									=  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}\"'      ,       _snake_case      ,       )
															    is not None
															):
																				_lowerCAmelCase:Optional[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:str									=  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[Any]									=  ['''encoder_no_repeat_ngram_size''']

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

															# Allow cases without checking the default value in the configuration class
															elif attribute in attributes_to_allow + attributes_used_in_generation:
																				_lowerCAmelCase:Union[str, Any]									=  True
															elif attribute.endswith('''_token_id'''    ):
																				_lowerCAmelCase:Optional[int]									=  True

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

					return attribute_used or case_allowed





def 							UpperCAmelCase		(		snake_case			:					List[str]    ):
					_lowerCAmelCase:Union[str, Any]									=  dict(inspect.signature(config_class.__init__    ).parameters    )
					_lowerCAmelCase:Dict									=  [x for x in list(signature.keys()    ) if x not in ['''self''', '''kwargs''']]
					_lowerCAmelCase:Tuple									=  [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:Dict									=  {}
					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[int]									=  inspect.getsourcefile(_snake_case    )
					_lowerCAmelCase:List[str]									=  os.path.dirname(_snake_case    )
					# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
					_lowerCAmelCase:Dict									=  [os.path.join(_snake_case      ,       _snake_case    ) for fn in os.listdir(_snake_case    ) if fn.startswith('''modeling_'''    )]

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

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

					return sorted(_snake_case    )





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:List[Any]									=  {}
					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:Dict									=  [
										    cls
										    for name, cls in inspect.getmembers(
										        inspect.getmodule(_config_class    )      ,       lambda snake_case    : inspect.isclass(_snake_case    )
										        and issubclass(_snake_case      ,       _snake_case    )
										        and inspect.getmodule(_snake_case    ) == inspect.getmodule(_config_class    )      ,       )
										]
										for config_class in config_classes_in_module:
															_lowerCAmelCase:Optional[int]									=  check_config_attributes_being_used(_snake_case    )
															if len(_snake_case    ) > 0:
																				_lowerCAmelCase:Tuple									=  unused_attributes

					if len(_snake_case    ) > 0:
										_lowerCAmelCase:int									=  '''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(_snake_case    )


if __name__ == "__main__":
				check_config_attributes()







"""simple docstring"""






import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.distilbert.modeling_flax_distilbert import (
				    FlaxDistilBertForMaskedLM,
				    FlaxDistilBertForMultipleChoice,
				    FlaxDistilBertForQuestionAnswering,
				    FlaxDistilBertForSequenceClassification,
				    FlaxDistilBertForTokenClassification,
				    FlaxDistilBertModel,
				)




class   a__			(       unittest.TestCase  ):





						def __init__(							self      :	Dict     ,a__      :	List[Any]     ,a__      :	Optional[int]=13     ,a__      :	Dict=7     ,a__      :	Any=True     ,a__      :	Tuple=True     ,a__      :	Union[str, Any]=True     ,a__      :	Dict=True     ,a__      :	Dict=99     ,a__      :	Optional[Any]=32     ,a__      :	Dict=5     ,a__      :	List[Any]=4     ,a__      :	Union[str, Any]=37     ,a__      :	str="gelu"     ,a__      :	Any=0.1     ,a__      :	Optional[int]=0.1     ,a__      :	Tuple=512     ,a__      :	Any=16     ,a__      :	Any=2     ,a__      :	Dict=0.02     ,a__      :	int=4     ,)						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  parent
											_lowerCAmelCase:List[str]									=  batch_size
											_lowerCAmelCase:Tuple									=  seq_length
											_lowerCAmelCase:List[Any]									=  is_training
											_lowerCAmelCase:Dict									=  use_attention_mask
											_lowerCAmelCase:Dict									=  use_token_type_ids
											_lowerCAmelCase:Any									=  use_labels
											_lowerCAmelCase:str									=  vocab_size
											_lowerCAmelCase:int									=  hidden_size
											_lowerCAmelCase:Union[str, Any]									=  num_hidden_layers
											_lowerCAmelCase:List[str]									=  num_attention_heads
											_lowerCAmelCase:Optional[int]									=  intermediate_size
											_lowerCAmelCase:str									=  hidden_act
											_lowerCAmelCase:Optional[int]									=  hidden_dropout_prob
											_lowerCAmelCase:Optional[int]									=  attention_probs_dropout_prob
											_lowerCAmelCase:Any									=  max_position_embeddings
											_lowerCAmelCase:Optional[int]									=  type_vocab_size
											_lowerCAmelCase:Optional[int]									=  type_sequence_label_size
											_lowerCAmelCase:Tuple									=  initializer_range
											_lowerCAmelCase:Dict									=  num_choices





						def       __UpperCamelCase     (							self      :	List[str])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:int									=  ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

											_lowerCAmelCase:List[Any]									=  None
											if self.use_attention_mask:
																_lowerCAmelCase:str									=  random_attention_mask([self.batch_size, self.seq_length])

											_lowerCAmelCase:Dict									=  DistilBertConfig(
											    vocab_size=self.vocab_size     ,dim=self.hidden_size     ,n_layers=self.num_hidden_layers     ,n_heads=self.num_attention_heads     ,hidden_dim=self.intermediate_size     ,hidden_act=self.hidden_act     ,dropout=self.hidden_dropout_prob     ,attention_dropout=self.attention_probs_dropout_prob     ,max_position_embeddings=self.max_position_embeddings     ,initializer_range=self.initializer_range     ,tie_weights_=a__     ,)

											return config, input_ids, attention_mask







						def       __UpperCamelCase     (							self      :	Any)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.prepare_config_and_inputs()
											_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  config_and_inputs
											_lowerCAmelCase:Any									=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict




@require_flax
class   a__			(       UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = (
						    (
						        FlaxDistilBertModel,
						        FlaxDistilBertForMaskedLM,
						        FlaxDistilBertForMultipleChoice,
						        FlaxDistilBertForQuestionAnswering,
						        FlaxDistilBertForSequenceClassification,
						        FlaxDistilBertForTokenClassification,
						        FlaxDistilBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def       __UpperCamelCase     (							self      :	str)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:str									=  FlaxDistilBertModelTester(self)







						@slow
						def       __UpperCamelCase     (							self      :	List[str])						->       int:




											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																_lowerCAmelCase:List[str]									=  model_class_name.from_pretrained('''distilbert-base-uncased''')
																_lowerCAmelCase:Tuple									=  model(np.ones((1, 1)))
																self.assertIsNotNone(a__)




@require_flax
class   a__			(       unittest.TestCase  ):





						@slow
						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased''')
											_lowerCAmelCase:Optional[int]									=  np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
											_lowerCAmelCase:Tuple									=  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
											_lowerCAmelCase:List[str]									=  model(a__     ,attention_mask=a__)[0]
											_lowerCAmelCase:List[str]									=  (1, 11, 768)
											self.assertEqual(output.shape     ,a__)
											_lowerCAmelCase:int									=  np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]     ,a__     ,atol=1E-4))






