
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 				a						(							snake_case__:					Dict						,   snake_case__:					Optional[Any]						,   snake_case__:					Tuple						,   snake_case__:					Tuple					):


				'''simple docstring'''
				lowercase_       =					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
												):
																lowercase_       =					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}"'''						,   snake_case__						,   )
												    is not None
												):
																lowercase_       =					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:
																				lowercase_       =					True
												if attribute_used:
																break
								if attribute_used:
												break

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

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

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

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

				return attribute_used or case_allowed




def 				a						(							snake_case__:					Dict					):


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

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

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

				lowercase_       =					[]
				for config_param, default_value in zip(snake_case__						,   snake_case__					):
								# `attributes` here is all the variant names for `config_param`
								lowercase_       =					[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(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__					):
												unused_attributes.append(attributes[0]					)

				return sorted(snake_case__					)




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					{}
				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.)
								lowercase_       =					[
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class					)						,   lambda snake_case__					: inspect.isclass(snake_case__					)
								        and issubclass(snake_case__						,   snake_case__					)
								        and inspect.getmodule(snake_case__					) == inspect.getmodule(_config_class					)						,   )
								]
								for config_class in config_classes_in_module:
												lowercase_       =					check_config_attributes_being_used(snake_case__					)
												if len(snake_case__					) > 0:
																lowercase_       =					unused_attributes

				if len(snake_case__					) > 0:
								lowercase_       =					'''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(snake_case__					)


if __name__ == "__main__":
						check_config_attributes()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a  			=					{
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




