



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
UpperCAmelCase__     = "src/transformers"


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

UpperCAmelCase__     = transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCAmelCase__     = {
    # 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			(  _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     List[str]					)       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     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	 =     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
									):
												_UpperCAmelCase	 =     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	 =     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	 =     [
			    'bos_index',
			    'eos_index',
			    'pad_index',
			    'unk_index',
			    'mask_index',
			    'image_size',
			    'use_cache',
			    'out_features',
			    'out_indices',
			]
			_UpperCAmelCase	 =     ['encoder_no_repeat_ngram_size']

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

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

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

			return attribute_used or case_allowed





def 							A			(  _UpperCAmelCase       :     int					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     dict(inspect.signature(config_class.__init__					).parameters					)
			_UpperCAmelCase	 =     [x for x in list(signature.keys()					) if x not in ['self', 'kwargs']]
			_UpperCAmelCase	 =     [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	 =     {}
			if len(config_class.attribute_map					) > 0:
						_UpperCAmelCase	 =     {v: k for k, v in config_class.attribute_map.items()}

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

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

			_UpperCAmelCase	 =     []
			for config_param, default_value in zip(_UpperCAmelCase							,     _UpperCAmelCase					):
						# `attributes` here is all the variant names for `config_param`
						_UpperCAmelCase	 =     [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 							A			(  )       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}
			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	 =     [
						    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:
									_UpperCAmelCase	 =     check_config_attributes_being_used(_UpperCAmelCase					)
									if len(_UpperCAmelCase					) > 0:
												_UpperCAmelCase	 =     unused_attributes

			if len(_UpperCAmelCase					) > 0:
						_UpperCAmelCase	 =     '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()





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  ['''pixel_values''']



			def __init__(		self     :						Any				,	A     :						bool = True				,	A     :						Optional[Dict[str, int]] = None				,	A     :						PILImageResampling = PILImageResampling.BILINEAR				,	A     :						bool = True				,	A     :						Dict[str, int] = None				,	A     :						bool = True				,	A     :						Union[int, float] = 1 / 2_55				,	A     :						bool = True				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	**A     :						Union[str, Any]				,	)     ->					None:



						"""simple docstring"""






						super().__init__(**A)
						_UpperCAmelCase	 =     size if size is not None else {'shortest_edge': 2_56}
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size
						_UpperCAmelCase	 =     resample
						_UpperCAmelCase	 =     do_center_crop
						_UpperCAmelCase	 =     crop_size
						_UpperCAmelCase	 =     do_rescale
						_UpperCAmelCase	 =     rescale_factor
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						_UpperCAmelCase	 =     image_std if image_std is not None else IMAGENET_STANDARD_STD



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						PILImageResampling = PILImageResampling.BICUBIC				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						List[str]				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						if "shortest_edge" not in size:
									raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
						_UpperCAmelCase	 =     get_resize_output_image_size(A				,	size=size['shortest_edge']				,	default_to_square=A)
						return resize(A				,	size=A				,	resample=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Any				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Union[str, Any]				,	)     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(A)
						if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}")
						return center_crop(A				,	size=(size['height'], size['width'])				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Any				,	A     :						np.ndarray				,	A     :						float				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict)     ->					np.ndarray:



						"""simple docstring"""






						return rescale(A				,	scale=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						int				,	A     :						np.ndarray				,	A     :						Union[float, List[float]]				,	A     :						Union[float, List[float]]				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Dict				,	)     ->					np.ndarray:



						"""simple docstring"""






						return normalize(A				,	mean=A				,	std=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						ImageInput				,	A     :						Optional[bool] = None				,	A     :						Dict[str, int] = None				,	A     :						PILImageResampling = None				,	A     :						bool = None				,	A     :						Dict[str, int] = None				,	A     :						Optional[bool] = None				,	A     :						Optional[float] = None				,	A     :						Optional[bool] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[float, List[float]]] = None				,	A     :						Optional[Union[str, TensorType]] = None				,	A     :						Union[str, ChannelDimension] = ChannelDimension.FIRST				,	**A     :						int				,	)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase	 =     size if size is not None else self.size
						_UpperCAmelCase	 =     get_size_dict(A				,	default_to_square=A)
						_UpperCAmelCase	 =     resample if resample is not None else self.resample
						_UpperCAmelCase	 =     do_center_crop if do_center_crop is not None else self.do_center_crop
						_UpperCAmelCase	 =     crop_size if crop_size is not None else self.crop_size
						_UpperCAmelCase	 =     get_size_dict(A				,	param_name='crop_size')
						_UpperCAmelCase	 =     do_rescale if do_rescale is not None else self.do_rescale
						_UpperCAmelCase	 =     rescale_factor if rescale_factor is not None else self.rescale_factor
						_UpperCAmelCase	 =     do_normalize if do_normalize is not None else self.do_normalize
						_UpperCAmelCase	 =     image_mean if image_mean is not None else self.image_mean
						_UpperCAmelCase	 =     image_std if image_std is not None else self.image_std

						_UpperCAmelCase	 =     make_list_of_images(A)

						if not valid_images(A):
									raise ValueError(
									    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
									    'torch.Tensor, tf.Tensor or jax.ndarray.')

						if do_resize and size is None:
									raise ValueError('Size must be specified if do_resize is True.')

						if do_center_crop and crop_size is None:
									raise ValueError('Crop size must be specified if do_center_crop is True.')

						if do_rescale and rescale_factor is None:
									raise ValueError('Rescale factor must be specified if do_rescale is True.')

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('Image mean and std must be specified if do_normalize is True.')

						# All transformations expect numpy arrays.
						_UpperCAmelCase	 =     [to_numpy_array(A) for image in images]

						if do_resize:
									_UpperCAmelCase	 =     [self.resize(image=A				,	size=A				,	resample=A) for image in images]

						if do_center_crop:
									_UpperCAmelCase	 =     [self.center_crop(image=A				,	size=A) for image in images]

						if do_rescale:
									_UpperCAmelCase	 =     [self.rescale(image=A				,	scale=A) for image in images]

						if do_normalize:
									_UpperCAmelCase	 =     [self.normalize(image=A				,	mean=A				,	std=A) for image in images]

						_UpperCAmelCase	 =     [to_channel_dimension_format(A				,	A) for image in images]

						_UpperCAmelCase	 =     {'pixel_values': images}
						return BatchFeature(data=A				,	tensor_type=A)



			def 					_lowerCamelCase (		self     :						str				,	A     :						Any				,	A     :						List[Tuple] = None)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(A) != len(A):
												raise ValueError(
												    'Make sure that you pass in as many target sizes as the batch dimension of the logits')

									if is_torch_tensor(A):
												_UpperCAmelCase	 =     target_sizes.numpy()

									_UpperCAmelCase	 =     []

									for idx in range(len(A)):
												_UpperCAmelCase	 =     torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0)				,	size=target_sizes[idx]				,	mode='bilinear'				,	align_corners=A)
												_UpperCAmelCase	 =     resized_logits[0].argmax(dim=0)
												semantic_segmentation.append(A)
						else:
									_UpperCAmelCase	 =     logits.argmax(dim=1)
									_UpperCAmelCase	 =     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

						return semantic_segmentation
