
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
SCREAMING_SNAKE_CASE__   :			List[str]												=    "src/transformers"


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

SCREAMING_SNAKE_CASE__   :			int												=    transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE__   :			Dict												=    {
    # 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  __magic_name__ (   __lowerCAmelCase	:						Any		,  __lowerCAmelCase	:						Tuple		,  __lowerCAmelCase	:						List[str]		,  __lowerCAmelCase	:						Tuple							)			->   str:
				__lowerCamelCase    					=	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    					=	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
												):
																__lowerCamelCase    					=	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    					=	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    					=	[
				    '''bos_index''',
				    '''eos_index''',
				    '''pad_index''',
				    '''unk_index''',
				    '''mask_index''',
				    '''image_size''',
				    '''use_cache''',
				    '''out_features''',
				    '''out_indices''',
				]
				__lowerCamelCase    					=	['''encoder_no_repeat_ngram_size''']

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

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

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

				return attribute_used or case_allowed




def  __magic_name__ (   __lowerCAmelCase	:						str							)			->   Dict:
				__lowerCamelCase    					=	dict(inspect.signature(config_class.__init__							).parameters							)
				__lowerCamelCase    					=	[x for x in list(signature.keys()							) if x not in ['''self''', '''kwargs''']]
				__lowerCamelCase    					=	[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    					=	{}
				if len(config_class.attribute_map							) > 0:
								__lowerCamelCase    					=	{v: k for k, v in config_class.attribute_map.items()}

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

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

				__lowerCamelCase    					=	[]
				for config_param, default_value in zip(__lowerCAmelCase		,  __lowerCAmelCase							):
								# `attributes` here is all the variant names for `config_param`
								__lowerCamelCase    					=	[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  __magic_name__ (   )			->   List[Any]:
				__lowerCamelCase    					=	{}
				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    					=	[
								    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:
												__lowerCamelCase    					=	check_config_attributes_being_used(__lowerCAmelCase							)
												if len(__lowerCAmelCase							) > 0:
																__lowerCamelCase    					=	unused_attributes

				if len(__lowerCAmelCase							) > 0:
								__lowerCamelCase    					=	'''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()





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

						__lowerCamelCase    					=	img
						__lowerCamelCase    					=	img.shape[1]
						__lowerCamelCase    					=	img.shape[0]
						__lowerCamelCase    					=	dst_width
						__lowerCamelCase    					=	dst_height

						__lowerCamelCase    					=	self.src_w / self.dst_w
						__lowerCamelCase    					=	self.src_h / self.dst_h

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
					)
					waitKey(0)
					destroyAllWindows()



