



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 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       List[str]   )    ->			Dict:







			'''simple docstring'''
			_UpperCAmelCase			=       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
									):
												_UpperCAmelCase			=       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}\""							,       _UpperCAmelCase							,       )
									    is not None
									):
												_UpperCAmelCase			=       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:
															_UpperCAmelCase			=       True
									if attribute_used:
												break
						if attribute_used:
									break

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

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

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

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

			return attribute_used or case_allowed





def 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			Any:







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

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

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

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

			return sorted(_UpperCAmelCase   )





def 							A     (					)    ->			str:







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

			if len(_UpperCAmelCase   ) > 0:
						_UpperCAmelCase			=       '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(_UpperCAmelCase   )


if __name__ == "__main__":
							check_config_attributes()





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import (
							    TFDebertaVaForMaskedLM,
							    TFDebertaVaForQuestionAnswering,
							    TFDebertaVaForSequenceClassification,
							    TFDebertaVaForTokenClassification,
							    TFDebertaVaModel,
							)







class       __lowerCAmelCase   :



			def __init__(						self       :  str			,		A       :  str			,		A       :  Dict=13			,		A       :  int=7			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  Any=True			,		A       :  Dict=True			,		A       :  Dict=99			,		A       :  Tuple=32			,		A       :  Any=2			,		A       :  Any=4			,		A       :  Any=37			,		A       :  Optional[Any]="gelu"			,		A       :  List[Any]=0.1			,		A       :  Tuple=0.1			,		A       :  Optional[Any]=5_12			,		A       :  Tuple=16			,		A       :  int=2			,		A       :  List[str]=0.0_2			,		A       :  int=False			,		A       :  List[Any]=True			,		A       :  Optional[Any]="None"			,		A       :  Union[str, Any]=3			,		A       :  List[str]=4			,		A       :  List[Any]=None			,		)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       seq_length
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_input_mask
						_UpperCAmelCase			=       use_token_type_ids
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       type_sequence_label_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       num_labels
						_UpperCAmelCase			=       num_choices
						_UpperCAmelCase			=       relative_attention
						_UpperCAmelCase			=       position_biased_input
						_UpperCAmelCase			=       pos_att_type
						_UpperCAmelCase			=       scope



			def    _lowerCamelCase	(						self       :  Any)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

						_UpperCAmelCase			=       None
						if self.use_input_mask:
									_UpperCAmelCase			=       random_attention_mask([self.batch_size, self.seq_length])

						_UpperCAmelCase			=       None
						if self.use_token_type_ids:
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.type_vocab_size)

						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						if self.use_labels:
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.type_sequence_label_size)
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.num_labels)

						_UpperCAmelCase			=       DebertaVaConfig(
						    vocab_size=self.vocab_size			,		hidden_size=self.hidden_size			,		num_hidden_layers=self.num_hidden_layers			,		num_attention_heads=self.num_attention_heads			,		intermediate_size=self.intermediate_size			,		hidden_act=self.hidden_act			,		hidden_dropout_prob=self.hidden_dropout_prob			,		attention_probs_dropout_prob=self.attention_probs_dropout_prob			,		max_position_embeddings=self.max_position_embeddings			,		type_vocab_size=self.type_vocab_size			,		relative_attention=self.relative_attention			,		position_biased_input=self.position_biased_input			,		initializer_range=self.initializer_range			,		return_dict=A			,		)

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  Optional[int]			,		A       :  Tuple			,		A       :  int			,		A       :  Any			,		A       :  List[str]			,		A       :  List[str]			,		A       :  int)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel(config=A)
						_UpperCAmelCase			=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

						_UpperCAmelCase			=       [input_ids, input_mask]
						_UpperCAmelCase			=       model(A)

						_UpperCAmelCase			=       model(A)

						self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size))



			def    _lowerCamelCase	(						self       :  str			,		A       :  Tuple			,		A       :  Tuple			,		A       :  Optional[int]			,		A       :  List[str]			,		A       :  Any			,		A       :  List[str]			,		A       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaForMaskedLM(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.vocab_size))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Tuple			,		A       :  Tuple			,		A       :  Optional[int]			,		A       :  Optional[int]			,		A       :  List[Any]			,		A       :  Any			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       TFDebertaVaForSequenceClassification(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  List[Any]			,		A       :  List[Any]			,		A       :  List[str]			,		A       :  Optional[Any]			,		A       :  int			,		A       :  Any			,		A       :  int)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       TFDebertaVaForTokenClassification(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.num_labels))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  List[Any]			,		A       :  List[str]			,		A       :  Dict			,		A       :  Dict			,		A       :  Any			,		A       :  Tuple			,		A       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaForQuestionAnswering(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.start_logits.shape			,		(self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape			,		(self.batch_size, self.seq_length))



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						(
						    (
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					
						)			=       config_and_inputs
						_UpperCAmelCase			=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
						return config, inputs_dict







@require_tf
class       __lowerCAmelCase   (      A    ,						A    ,						unittest.TestCase						):
			UpperCamelCase        =					(
			    (
			        TFDebertaVaModel,
			        TFDebertaVaForMaskedLM,
			        TFDebertaVaForQuestionAnswering,
			        TFDebertaVaForSequenceClassification,
			        TFDebertaVaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase        =					(
			    {
			        '''feature-extraction''': TFDebertaVaModel,
			        '''fill-mask''': TFDebertaVaForMaskedLM,
			        '''question-answering''': TFDebertaVaForQuestionAnswering,
			        '''text-classification''': TFDebertaVaForSequenceClassification,
			        '''token-classification''': TFDebertaVaForTokenClassification,
			        '''zero-shot''': TFDebertaVaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			UpperCamelCase        =					False
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  int)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModelTester(self)
						_UpperCAmelCase			=       ConfigTester(self			,		config_class=A			,		hidden_size=37)



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



						"""simple docstring"""






						self.config_tester.run_common_tests()



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*A)



			def    _lowerCamelCase	(						self       :  int)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*A)



			def    _lowerCamelCase	(						self       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*A)



			@slow
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
						self.assertIsNotNone(A)







@require_tf
class       __lowerCAmelCase   (      unittest.TestCase						):



			@unittest.skip(reason='Model not available yet')
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Dict:



						"""simple docstring"""






						pass



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



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
						_UpperCAmelCase			=       tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
						_UpperCAmelCase			=       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
						_UpperCAmelCase			=       model(A			,		attention_mask=A)[0]

						_UpperCAmelCase			=       tf.constant(
						    [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
						tf.debugging.assert_near(output[:, 1:4, 1:4]			,		A			,		atol=1E-4)
