"""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     :		int											=					"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
_UpperCamelCase     :		Union[str, Any]											=					direct_transformers_import(PATH_TO_TRANSFORMERS)

_UpperCamelCase     :		int											=					transformers.models.auto.configuration_auto.CONFIG_MAPPING

_UpperCamelCase     :		List[Any]											=					{
    # 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						:				int     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	List[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
                     ):
                            lowercase__    :	Optional[int]     					=			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
                     ):
                            lowercase__    :	List[str]     					=			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:
                                   lowercase__    :	Union[str, Any]     					=			True
                     if attribute_used:
                            break
              if attribute_used:
                     break

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

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

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

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

       return attribute_used or case_allowed





def        a_  (   _lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

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

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

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

       lowercase__    :	Dict     					=			[]
       for config_param, default_value in zip(lowercase__     ,				lowercase__  ):
              # `attributes` here is all the variant names for `config_param`
              lowercase__    :	str     					=			[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        a_  (   ):





       '''simple docstring'''

       lowercase__    :	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.)
              lowercase__    :	Union[str, Any]     					=			[
                  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:
                     lowercase__    :	List[Any]     					=			check_config_attributes_being_used(lowercase__  )
                     if len(lowercase__  ) > 0:
                            lowercase__    :	int     					=			unused_attributes

       if len(lowercase__  ) > 0:
              lowercase__    :	Union[str, Any]     					=			'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()



"""simple docstring"""





import unittest

import numpy as np

from transformers import AlbertConfig, 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.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=True	,    a=True	,    a=9_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=4	,    ) ->      Dict:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	Dict     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	int     					=			is_training
             lowercase__    :	str     					=			use_attention_mask
             lowercase__    :	Dict     					=			use_token_type_ids
             lowercase__    :	Optional[int]     					=			use_labels
             lowercase__    :	Tuple     					=			vocab_size
             lowercase__    :	List[str]     					=			hidden_size
             lowercase__    :	Union[str, Any]     					=			num_hidden_layers
             lowercase__    :	int     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Dict     					=			hidden_dropout_prob
             lowercase__    :	Tuple     					=			attention_probs_dropout_prob
             lowercase__    :	List[str]     					=			max_position_embeddings
             lowercase__    :	int     					=			type_vocab_size
             lowercase__    :	List[str]     					=			type_sequence_label_size
             lowercase__    :	Union[str, Any]     					=			initializer_range
             lowercase__    :	Optional[int]     					=			num_choices


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

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

             lowercase__    :	List[str]     					=			None
             if self.use_token_type_ids:
                    lowercase__    :	Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size							)

             lowercase__    :	Any     					=			AlbertConfig(
                 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	,    is_decoder=a	,    initializer_range=self.initializer_range	,    )

             return config, input_ids, token_type_ids, attention_mask


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[int]     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Tuple     					=			config_and_inputs
             lowercase__    :	Union[str, Any]     					=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
             return config, inputs_dict





@require_flax
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Tuple										=						(
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Union[str, Any]     					=			FlaxAlbertModelTester(self							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_class_name in self.all_model_classes:
                    lowercase__    :	str     					=			model_class_name.from_pretrained('albert-base-v2'							)
                    lowercase__    :	Tuple     					=			model(np.ones((1, 1)							)							)
                    self.assertIsNotNone(a							)





@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			FlaxAlbertModel.from_pretrained('albert-base-v2'							)
             lowercase__    :	Optional[int]     					=			np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
             lowercase__    :	Optional[Any]     					=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             lowercase__    :	Any     					=			model(a	,    attention_mask=a							)[0]
             lowercase__    :	Tuple     					=			(1, 1_1, 7_6_8)
             self.assertEqual(output.shape	,    a							)
             lowercase__    :	Optional[Any]     					=			np.array(
                 [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]							)

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

