




"""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
_A													=      """src/transformers"""


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

_A													=      transformers.models.auto.configuration_auto.CONFIG_MAPPING

_A													=      {
    # 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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			Tuple   =	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
                  ):
                        lowerCAmelCase__      :			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}\""""    ,		__UpperCAmelCase    ,		)
                      is not None
                  ):
                        lowerCAmelCase__      :			Union[str, 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:
                              lowerCAmelCase__      :			Optional[int]   =	True
                  if attribute_used:
                        break
            if attribute_used:
                  break

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

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

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

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

      return attribute_used or case_allowed



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

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

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

      lowerCAmelCase__      :			Optional[Any]   =	[]
      for config_param, default_value in zip(__UpperCAmelCase    ,		__UpperCAmelCase				):
            # `attributes` here is all the variant names for `config_param`
            lowerCAmelCase__      :			Tuple   =	[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 	lowercase_				(			)						->    List[str]:
      lowerCAmelCase__      :			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.)
            lowerCAmelCase__      :			Union[str, Any]   =	[
                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:
                  lowerCAmelCase__      :			str   =	check_config_attributes_being_used(__UpperCAmelCase				)
                  if len(__UpperCAmelCase				) > 0:
                        lowerCAmelCase__      :			str   =	unused_attributes

      if len(__UpperCAmelCase				) > 0:
            lowerCAmelCase__      :			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()











"""simple docstring"""


import os
from collections import deque

import torch
from torch.utils.data import Dataset


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		Tuple   ,     UpperCamelCase       :		List[Any]=""   ,     UpperCamelCase       :		List[str]="train"   )			->						List[Any]:

         """simple docstring"""
         assert os.path.isdir(UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	[]
         lowerCAmelCase__      :			List[Any]   =	os.listdir(UpperCamelCase   )
         for story_filename in story_filenames_list:
               if "summary" in story_filename:
                     continue
               lowerCAmelCase__      :			Any   =	os.path.join(UpperCamelCase   ,     UpperCamelCase   )
               if not os.path.isfile(UpperCamelCase   ):
                     continue
               self.documents.append(UpperCamelCase   )






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

         """simple docstring"""
         return len(self.documents   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	self.documents[idx]
         lowerCAmelCase__      :			List[Any]   =	document_path.split("""/"""   )[-1]
         with open(UpperCamelCase   ,     encoding="""utf-8"""   ) as source:
               lowerCAmelCase__      :			List[str]   =	source.read()
               lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	process_story(UpperCamelCase   )
         return document_name, story_lines, summary_lines






def 	lowercase_				(			__UpperCAmelCase				)						->    Union[str, Any]:
      lowerCAmelCase__      :			Tuple   =	list(filter(lambda __UpperCAmelCase				: len(__UpperCAmelCase				) != 0    ,		[line.strip() for line in raw_story.split("""\n"""				)]				)				)

      # for some unknown reason some lines miss a period, add it
      lowerCAmelCase__      :			List[str]   =	[_add_missing_period(__UpperCAmelCase				) for line in nonempty_lines]

      # gather article lines
      lowerCAmelCase__      :			Optional[int]   =	[]
      lowerCAmelCase__      :			Optional[int]   =	deque(__UpperCAmelCase				)
      while True:
            try:
                  lowerCAmelCase__      :			List[Any]   =	lines.popleft()
                  if element.startswith("""@highlight"""				):
                        break
                  story_lines.append(__UpperCAmelCase				)
            except IndexError:
                  # if "@highlight" is absent from the file we pop
                  # all elements until there is None, raising an exception.
                  return story_lines, []

    # gather summary lines
      lowerCAmelCase__      :			Any   =	list(filter(lambda __UpperCAmelCase				: not t.startswith("""@highlight"""				)    ,		__UpperCAmelCase				)				)

      return story_lines, summary_lines



def 	lowercase_				(			__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			List[str]   =	[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
      if line.startswith("""@highlight"""				):
            return line
      if line[-1] in END_TOKENS:
            return line
      return line + "."



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      if len(__UpperCAmelCase				) > block_size:
            return sequence[:block_size]
      else:
            sequence.extend([pad_token_id] * (block_size - len(__UpperCAmelCase				))				)
            return sequence



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Tuple:
      lowerCAmelCase__      :			List[str]   =	torch.ones_like(__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	sequence == pad_token_id
      lowerCAmelCase__      :			str   =	0
      return mask



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      lowerCAmelCase__      :			Tuple   =	[tokenizer.encode(__UpperCAmelCase				) for line in story_lines]
      lowerCAmelCase__      :			List[str]   =	[token for sentence in story_lines_token_ids for token in sentence]
      lowerCAmelCase__      :			int   =	[tokenizer.encode(__UpperCAmelCase				) for line in summary_lines]
      lowerCAmelCase__      :			Union[str, Any]   =	[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      lowerCAmelCase__      :			Tuple   =	[]
      for sequence in batch:
            lowerCAmelCase__      :			Union[str, Any]   =	-1
            lowerCAmelCase__      :			List[str]   =	[]
            for s in sequence:
                  if s == separator_token_id:
                        sentence_num += 1
                  embeddings.append(sentence_num % 2				)
            batch_embeddings.append(__UpperCAmelCase				)
      return torch.tensor(__UpperCAmelCase				)





