





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       :     Optional[Any]   =     """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase       :     List[str]   =     direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowerCamelCase       :     Union[str, Any]   =     transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCamelCase       :     Union[str, 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							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 UpperCamelCase	:					List[str]       	=   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	:					Union[str, 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}\""""							,   _lowerCAmelCase							,   )
       is not None
   ):
    UpperCamelCase	:					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:
     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	:					Tuple       	=   [
     "bos_index",
     "eos_index",
     "pad_index",
     "unk_index",
     "mask_index",
     "image_size",
     "use_cache",
     "out_features",
     "out_indices",
 ]
 UpperCamelCase	:					str       	=   ["encoder_no_repeat_ngram_size"]

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

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

 return attribute_used or case_allowed



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

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

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

 return sorted(_lowerCAmelCase     )



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

 if len(_lowerCAmelCase     ) > 0:
  UpperCamelCase	:					Tuple       	=   "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(_lowerCAmelCase     )


if __name__ == "__main__":
    check_config_attributes()


"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_A			:       Optional[int]   		=       logging.getLogger(__name__)

class 		a__						(      a_						):
   def __init__(			self	,    _a=-1				):
          # in NER datasets, the last column is usually reserved for NER label
          lowercase    :				List[str]       		=							label_idx
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[Any]       		=							mode.value
          lowercase    :				List[str]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				str       		=							1
          lowercase    :				Optional[int]       		=							[]
          with open(_a	,    encoding="utf-8"				) as f:
                 lowercase    :				List[Any]       		=							[]
                 lowercase    :				Optional[int]       		=							[]
                 for line in f:
                        if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                               if words:
                                      examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                                      guid_index += 1
                                      lowercase    :				int       		=							[]
                                      lowercase    :				int       		=							[]
                        else:
                               lowercase    :				Optional[Any]       		=							line.split(" "				)
                               words.append(splits[0]				)
                               if len(_a				) > 1:
                                      labels.append(splits[self.label_idx].replace("\n"	,    ""				)				)
                               else:
                                      # Examples could have no label for mode = "test"
                                      labels.append("O"				)
                 if words:
                        examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							0
          for line in test_input_reader:
                 if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                        writer.write(_a				)
                        if not preds_list[example_id]:
                               example_id += 1
                 elif preds_list[example_id]:
                        lowercase    :				Any       		=							line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"
                        writer.write(_a				)
                 else:
                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,    line.split()[0]				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Optional[Any]       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				List[Any]       		=							["O"] + labels
                 return labels
          else:
                 return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]


class 		a__						(      a_						):
   def __init__(			self				):
          # in CONLL2003 dataset chunk column is second-to-last
          super().__init__(label_idx=-2				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Tuple       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				Optional[int]       		=							["O"] + labels
                 return labels
          else:
                 return [
                     "O",
                     "B-ADVP",
                     "B-INTJ",
                     "B-LST",
                     "B-PRT",
                     "B-NP",
                     "B-SBAR",
                     "B-VP",
                     "B-ADJP",
                     "B-CONJP",
                     "B-PP",
                     "I-ADVP",
                     "I-INTJ",
                     "I-LST",
                     "I-PRT",
                     "I-NP",
                     "I-SBAR",
                     "I-VP",
                     "I-ADJP",
                     "I-CONJP",
                     "I-PP",
                 ]


class 		a__						(      a_						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				List[Any]       		=							mode.value
          lowercase    :				Optional[int]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				Tuple       		=							1
          lowercase    :				List[str]       		=							[]

          with open(_a	,    encoding="utf-8"				) as f:
                 for sentence in parse_incr(_a				):
                        lowercase    :				Optional[Any]       		=							[]
                        lowercase    :				str       		=							[]
                        for token in sentence:
                               words.append(token["form"]				)
                               labels.append(token["upos"]				)
                        assert len(_a				) == len(_a				)
                        if words:
                               examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                               guid_index += 1
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				str       		=							0
          for sentence in parse_incr(_a				):
                 lowercase    :				List[Any]       		=							preds_list[example_id]
                 lowercase    :				List[str]       		=							""
                 for token in sentence:
                        out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) """
                 out += "\n"
                 writer.write(_a				)
                 example_id += 1

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        return f.read().splitlines()
          else:
                 return [
                     "ADJ",
                     "ADP",
                     "ADV",
                     "AUX",
                     "CCONJ",
                     "DET",
                     "INTJ",
                     "NOUN",
                     "NUM",
                     "PART",
                     "PRON",
                     "PROPN",
                     "PUNCT",
                     "SCONJ",
                     "SYM",
                     "VERB",
                     "X",
                 ]


