


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			:			int												=					'''src/transformers'''


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

a			:			Optional[int]												=					transformers.models.auto.configuration_auto.CONFIG_MAPPING

a			:			Union[str, Any]												=					{
    # 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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''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}"'							,      _UpperCamelCase							,      )
									    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 		lowercase_	(  _UpperCamelCase	):


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

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

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

			return sorted(_UpperCamelCase	)


def 		lowercase_	(  ):


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

			if len(_UpperCamelCase	) > 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(_UpperCamelCase	)


if __name__ == "__main__":
	check_config_attributes()






from typing import TYPE_CHECKING

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


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	a			:			Dict												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


