
'''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
__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				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  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
																					):
																												_snake_case       =  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
																					):
																												_snake_case       =  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:
																																			_snake_case       =  True
																					if attribute_used:
																												break
														if attribute_used:
																					break

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

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

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

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

							return attribute_used or case_allowed






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  dict(inspect.signature(config_class.__init__				).parameters				)
							_snake_case       =  [x for x in list(signature.keys()				) if x not in ["""self""", """kwargs"""]]
							_snake_case       =  [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
							_snake_case       =  {}
							if len(config_class.attribute_map				) > 0:
														_snake_case       =  {v: k for k, v in config_class.attribute_map.items()}

							# Get the path to modeling source files
							_snake_case       =  inspect.getsourcefile(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  os.path.dirname(_SCREAMING_SNAKE_CASE				)
							# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
							_snake_case       =  [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
							_snake_case       =  []
							for path in modeling_paths:
														if os.path.isfile(_SCREAMING_SNAKE_CASE				):
																					with open(_SCREAMING_SNAKE_CASE				) as fp:
																												modeling_sources.append(fp.read()				)

							_snake_case       =  []
							for config_param, default_value in zip(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
														# `attributes` here is all the variant names for `config_param`
														_snake_case       =  [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  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  {}
							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.)
														_snake_case       =  [
														    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:
																					_snake_case       =  check_config_attributes_being_used(_SCREAMING_SNAKE_CASE				)
																					if len(_SCREAMING_SNAKE_CASE				) > 0:
																												_snake_case       =  unused_attributes

							if len(_SCREAMING_SNAKE_CASE				) > 0:
														_snake_case       =  """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()

'''simple docstring'''
from __future__ import annotations






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# We need to create solution object to save path.
							_snake_case       =  [[0 for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]
							_snake_case       =  run_maze(_SCREAMING_SNAKE_CASE							, 0							, 0							, _SCREAMING_SNAKE_CASE				)
							if solved:
														print("""\n""".join(str(_SCREAMING_SNAKE_CASE				) for row in solutions				)				)
							else:
														print("""No solution exists!"""				)
							return solved






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							# Final check point.
							if i == j == (size - 1):
														_snake_case       =  1
														return True

							_snake_case       =  (not i < 0) and (not j < 0)  # Check lower bounds
							_snake_case       =  (i < size) and (j < size)  # Check upper bounds

							if lower_flag and upper_flag:
														# check for already visited and block points.
														_snake_case       =  (not solutions[i][j]) and (not maze[i][j])
														if block_flag:
																					# check visited
																					_snake_case       =  1

																					# check for directions
																					if (
																					    run_maze(_SCREAMING_SNAKE_CASE							, i + 1							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, j + 1							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, i - 1							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
																					    or run_maze(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, j - 1							, _SCREAMING_SNAKE_CASE				)
																					):
																												return True

																					_snake_case       =  0
																					return False
							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()