
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
lowerCamelCase      ="src/transformers"


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

lowerCamelCase      =transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCamelCase      ={
    # 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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   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
                  ):
                        UpperCamelCase__    :   List[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}"''' ,		UpperCamelCase__ ,		)
                      is not None
                  ):
                        UpperCamelCase__    :   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:
                              UpperCamelCase__    :   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
      UpperCamelCase__    :   List[Any]					=				[
          '''bos_index''',
          '''eos_index''',
          '''pad_index''',
          '''unk_index''',
          '''mask_index''',
          '''image_size''',
          '''use_cache''',
          '''out_features''',
          '''out_indices''',
      ]
      UpperCamelCase__    :   Optional[int]					=				['''encoder_no_repeat_ngram_size''']

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

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

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

      return attribute_used or case_allowed


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

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

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

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

      return sorted(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Union[str, 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.)
            UpperCamelCase__    :   Dict					=				[
                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__    :   str					=				check_config_attributes_being_used(UpperCamelCase__		)
                  if len(UpperCamelCase__		) > 0:
                        UpperCamelCase__    :   int					=				unused_attributes

      if len(UpperCamelCase__		) > 0:
            UpperCamelCase__    :   Optional[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(UpperCamelCase__		)


if __name__ == "__main__":
    check_config_attributes()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      ={
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CLIPSegModel",
        "CLIPSegPreTrainedModel",
        "CLIPSegTextModel",
        "CLIPSegVisionModel",
        "CLIPSegForImageSegmentation",
    ]

if TYPE_CHECKING:
    from .configuration_clipseg import (
        CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
        CLIPSegConfig,
        CLIPSegTextConfig,
        CLIPSegVisionConfig,
    )
    from .processing_clipseg import CLIPSegProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_clipseg import (
            CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
            CLIPSegForImageSegmentation,
            CLIPSegModel,
            CLIPSegPreTrainedModel,
            CLIPSegTextModel,
            CLIPSegVisionModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


