"""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
__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    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[Any]      ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     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					:		Tuple		   =     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					:		Tuple		   =     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					:		Optional[int]		   =     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					:		Optional[Any]		   =     [
							    "bos_index",
							    "eos_index",
							    "pad_index",
							    "unk_index",
							    "mask_index",
							    "image_size",
							    "use_cache",
							    "out_features",
							    "out_indices",
							]
							__lowerCamelCase					:		List[str]		   =     ["encoder_no_repeat_ngram_size"]

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

																					# Allow cases without checking the default value in the configuration class
																					elif attribute in attributes_to_allow + attributes_used_in_generation:
																												__lowerCamelCase					:		Tuple		   =     True
																					elif attribute.endswith("_token_id"      ):
																												__lowerCamelCase					:		Union[str, Any]		   =     True

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

							return attribute_used or case_allowed


def    lowercase_   (   _lowerCamelCase:	Any      ) ->      Optional[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		int		   =     dict(inspect.signature(config_class.__init__      ).parameters      )
							__lowerCamelCase					:		Union[str, Any]		   =     [x for x in list(signature.keys()      ) if x not in ["self", "kwargs"]]
							__lowerCamelCase					:		int		   =     [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					:		int		   =     {}
							if len(config_class.attribute_map      ) > 0:
														__lowerCamelCase					:		Tuple		   =     {v: k for k, v in config_class.attribute_map.items()}

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

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

							__lowerCamelCase					:		Union[str, Any]		   =     []
							for config_param, default_value in zip(_lowerCamelCase						,   _lowerCamelCase      ):
														# `attributes` here is all the variant names for `config_param`
														__lowerCamelCase					:		Optional[int]		   =     [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    lowercase_   (   ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     {}
							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					:		Optional[Any]		   =     [
														    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					:		str		   =     check_config_attributes_being_used(_lowerCamelCase      )
																					if len(_lowerCamelCase      ) > 0:
																												__lowerCamelCase					:		Any		   =     unused_attributes

							if len(_lowerCamelCase      ) > 0:
														__lowerCamelCase					:		Tuple		   =     "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()
"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	str						,   _lowerCamelCase:	Dict      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     TaConfig.from_json_file(_lowerCamelCase      )
							print(F"""Building PyTorch model from configuration: {config}"""      )
							__lowerCamelCase					:		Optional[int]		   =     TaForConditionalGeneration(_lowerCamelCase      )

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
							model.save_pretrained(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
				)
				parser.add_argument(
				    '''--config_file''',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
				    ),
				)
				parser.add_argument(
				    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
				)
				__A  =				parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)