





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__: int							   =						'src/transformers'


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

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

a__: Dict							   =						{
    # 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__(			UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			List[str]    )->str:
       A__		   =		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
                     ):
                            A__		   =		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
                     ):
                            A__		   =		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:
                                   A__		   =		True
                     if attribute_used:
                            break
              if attribute_used:
                     break

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

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

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

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

       return attribute_used or case_allowed


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->Optional[int]:
       A__		   =		dict(inspect.signature(config_class.__init__    ).parameters    )
       A__		   =		[x for x in list(signature.keys()    ) if x not in ['''self''', '''kwargs''']]
       A__		   =		[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
       A__		   =		{}
       if len(config_class.attribute_map    ) > 0:
              A__		   =		{v: k for k, v in config_class.attribute_map.items()}

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

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

       A__		   =		[]
       for config_param, default_value in zip(UpperCamelCase__					,	UpperCamelCase__    ):
              # `attributes` here is all the variant names for `config_param`
              A__		   =		[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       UpperCamelCase__(			)->Union[str, Any]:
       A__		   =		{}
       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.)
              A__		   =		[
                  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:
                     A__		   =		check_config_attributes_being_used(UpperCamelCase__    )
                     if len(UpperCamelCase__    ) > 0:
                            A__		   =		unused_attributes

       if len(UpperCamelCase__    ) > 0:
              A__		   =		'''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()












import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict




@dataclass
class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      ComputeEnvironment.AMAZON_SAGEMAKER
 __SCREAMING_SNAKE_CASE							=      True
 __SCREAMING_SNAKE_CASE							=      '''ml.p3.2xlarge'''
 __SCREAMING_SNAKE_CASE							=      '''accelerate_sagemaker_execution_role'''
 __SCREAMING_SNAKE_CASE							=      '''hf-sm'''
 __SCREAMING_SNAKE_CASE							=      '''us-east-1'''
 __SCREAMING_SNAKE_CASE							=      1
 __SCREAMING_SNAKE_CASE							=      '''accelerate-sagemaker-1'''
 __SCREAMING_SNAKE_CASE							=      '''1.6'''
 __SCREAMING_SNAKE_CASE							=      '''4.4'''
 __SCREAMING_SNAKE_CASE							=      '''train.py'''
 __SCREAMING_SNAKE_CASE							=      [
     '''--model_name_or_path''',
     '''bert''',
     '''--do_train''',
     '''False''',
     '''--epochs''',
     '''3''',
     '''--learning_rate''',
     '''5e-5''',
     '''--max_steps''',
     '''50.5''',
 ]
 __SCREAMING_SNAKE_CASE							=      [
     '''--model_name_or_path''',
     '''bert''',
     '''--do_train''',
     '''--do_test''',
     '''False''',
     '''--do_predict''',
     '''--epochs''',
     '''3''',
     '''--learning_rate''',
     '''5e-5''',
     '''--max_steps''',
     '''50.5''',
 ]




class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):



 def 							UpperCamelCase     (				self					):
        # If no defaults are changed, `to_kwargs` returns an empty dict.
        A__		   =		_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
        assert isinstance(converted_args['''model_name_or_path'''],__lowerCamelCase					)
        assert isinstance(converted_args['''do_train'''],__lowerCamelCase					)
        assert isinstance(converted_args['''epochs'''],__lowerCamelCase					)
        assert isinstance(converted_args['''learning_rate'''],__lowerCamelCase					)
        assert isinstance(converted_args['''max_steps'''],__lowerCamelCase					)

        with pytest.raises(__lowerCamelCase					):
               _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args					)





