




'''simple docstring'''




def      __snake_case       (   lowercase   :							str  ):







					if len(lowercase  ) < 2:
										return collection

					def circle_sort_util(lowercase   :							Any			, lowercase   :							int			, lowercase   :							List[str]  ) -> bool:
										snake_case_	=      False

										if low == high:
															return swapped

										snake_case_	=      low
										snake_case_	=      high

										while left < right:
															if collection[left] > collection[right]:
																				snake_case_	,					snake_case_	=      (
																				    collection[right],
																				    collection[left],
																				)
																				snake_case_	=      True

															left += 1
															right -= 1

										if left == right and collection[left] > collection[right + 1]:
															snake_case_	,					snake_case_	=      (
															    collection[right + 1],
															    collection[left],
															)

															snake_case_	=      True

										snake_case_	=      low + int((high - low) / 2  )
										snake_case_	=      circle_sort_util(lowercase			, lowercase			, lowercase  )
										snake_case_	=      circle_sort_util(lowercase			, mid + 1			, lowercase  )

										return swapped or left_swap or right_swap

					snake_case_	=      True

					while is_not_sorted is True:
										snake_case_	=      circle_sort_util(lowercase			, 0			, len(lowercase  ) - 1  )

					return collection


if __name__ == "__main__":
					lowercase__  							=					input('''Enter numbers separated by a comma:\n''').strip()
					lowercase__  							=					[int(item) for item in user_input.split(''',''')]
					print(circle_sort(unsorted))








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
__lowercase				      =						"""src/transformers"""


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

__lowercase				      =						transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowercase				      =						{
    # 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 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      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}\""		,				SCREAMING_SNAKE_CASE		,				)
                      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 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      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(SCREAMING_SNAKE_CASE   )
      A_							       =		os.path.dirname(SCREAMING_SNAKE_CASE   )
      # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
      A_							       =		[os.path.join(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ) for fn in os.listdir(SCREAMING_SNAKE_CASE   ) if fn.startswith('''modeling_'''   )]

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

      A_							       =		[]
      for config_param, default_value in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
            # `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(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
                  unused_attributes.append(attributes[0]   )

      return sorted(SCREAMING_SNAKE_CASE   )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      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 SCREAMING_SNAKE_CASE   : inspect.isclass(SCREAMING_SNAKE_CASE   )
                    and issubclass(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
                    and inspect.getmodule(SCREAMING_SNAKE_CASE   ) == inspect.getmodule(_config_class   )		,				)
            ]
            for config_class in config_classes_in_module:
                  A_							       =		check_config_attributes_being_used(SCREAMING_SNAKE_CASE   )
                  if len(SCREAMING_SNAKE_CASE   ) > 0:
                        A_							       =		unused_attributes

      if len(SCREAMING_SNAKE_CASE   ) > 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(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      check_config_attributes()





