






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 					snake_case__				(				UpperCAmelCase				:	List[Any]     ,      UpperCAmelCase				:	List[Any]     ,      UpperCAmelCase				:	int     ,      UpperCAmelCase				:	Dict							):
    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__ :Union[str, Any]   				=					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
            ):
                lowerCAmelCase__ :List[Any]   				=					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__ :int   				=					[
        "bos_index",
        "eos_index",
        "pad_index",
        "unk_index",
        "mask_index",
        "image_size",
        "use_cache",
        "out_features",
        "out_indices",
    ]
    lowerCAmelCase__ :Dict   				=					["encoder_no_repeat_ngram_size"]

    # Special cases to be allowed
    lowerCAmelCase__ :Union[str, Any]   				=					True
    if not attribute_used:
        lowerCAmelCase__ :Dict   				=					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__ :Dict   				=					True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                lowerCAmelCase__ :str   				=					True

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

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

    return attribute_used or case_allowed


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

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

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

    lowerCAmelCase__ :str   				=					[]
    for config_param, default_value in zip(_UpperCamelCase     ,      _UpperCamelCase							):
        # `attributes` here is all the variant names for `config_param`
        lowerCAmelCase__ :Any   				=					[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 					snake_case__				(				):
    lowerCAmelCase__ :int   				=					{}
    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__ :Any   				=					[
            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:
            lowerCAmelCase__ :str   				=					check_config_attributes_being_used(_UpperCamelCase							)
            if len(_UpperCamelCase							) > 0:
                lowerCAmelCase__ :Any   				=					unused_attributes

    if len(_UpperCamelCase							) > 0:
        lowerCAmelCase__ :Optional[int]   				=					"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()







import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

a			:			Union[str, Any]												=					logging.getLogger(__name__)

a			:			Any												=					{'''facebook/bart-base''': BartForConditionalGeneration}
a			:			int												=					{'''facebook/bart-base''': BartTokenizer}


def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''	)
			parser.add_argument(
			    '''--validation_file'''							,      type=_UpperCamelCase							,      default=_UpperCamelCase							,      help='''A csv or a json file containing the validation data.'''	)
			parser.add_argument(
			    '''--max_length'''							,      type=_UpperCamelCase							,      default=5							,      help='''The maximum total input sequence length after tokenization.'''							,      )
			parser.add_argument(
			    '''--num_beams'''							,      type=_UpperCamelCase							,      default=_UpperCamelCase							,      help=(
			        '''Number of beams to use for evaluation. This argument will be '''
			        '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
			    )							,      )
			parser.add_argument(
			    '''--model_name_or_path'''							,      type=_UpperCamelCase							,      help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,      required=_UpperCamelCase							,      )
			parser.add_argument(
			    '''--config_name'''							,      type=_UpperCamelCase							,      default=_UpperCamelCase							,      help='''Pretrained config name or path if not the same as model_name'''							,      )
			parser.add_argument(
			    '''--device'''							,      type=_UpperCamelCase							,      default='''cpu'''							,      help='''Device where the model will be run'''							,      )
			parser.add_argument('''--output_file_path'''							,      type=_UpperCamelCase							,      default=_UpperCamelCase							,      help='''Where to store the final ONNX file.'''	)

			__lowercase			       = parser.parse_args()

			return args


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase="cpu"	):


			'''simple docstring'''







			__lowercase			       = model_dict[model_name].from_pretrained(_UpperCamelCase	).to(_UpperCamelCase	)
			__lowercase			       = tokenizer_dict[model_name].from_pretrained(_UpperCamelCase	)

			if model_name in ["facebook/bart-base"]:
						__lowercase			       = 0
						__lowercase			       = None
						__lowercase			       = 0

			return huggingface_model, tokenizer


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			model.eval()

			__lowercase			       = None
			__lowercase			       = torch.jit.script(BARTBeamSearchGenerator(_UpperCamelCase	)	)

			with torch.no_grad():
						__lowercase			       = '''My friends are cool but they eat too many carbs.'''
						__lowercase			       = tokenizer([ARTICLE_TO_SUMMARIZE]							,      max_length=10_24							,      return_tensors='''pt'''	).to(model.device	)

						__lowercase			       = model.generate(
						    inputs['''input_ids''']							,      attention_mask=inputs['''attention_mask''']							,      num_beams=_UpperCamelCase							,      max_length=_UpperCamelCase							,      early_stopping=_UpperCamelCase							,      decoder_start_token_id=model.config.decoder_start_token_id							,      )

						torch.onnx.export(
						    _UpperCamelCase							,      (
						        inputs['''input_ids'''],
						        inputs['''attention_mask'''],
						        num_beams,
						        max_length,
						        model.config.decoder_start_token_id,
						    )							,      _UpperCamelCase							,      opset_version=14							,      input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']							,      output_names=['''output_ids''']							,      dynamic_axes={
						        '''input_ids''': {0: '''batch''', 1: '''seq'''},
						        '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
						    }							,      example_outputs=_UpperCamelCase							,      )

						logger.info('''Model exported to {}'''.format(_UpperCamelCase	)	)

						__lowercase			       = remove_dup_initializers(os.path.abspath(_UpperCamelCase	)	)

						logger.info('''Deduplicated and optimized model written to {}'''.format(_UpperCamelCase	)	)

						__lowercase			       = onnxruntime.InferenceSession(_UpperCamelCase	)
						__lowercase			       = ort_sess.run(
						    _UpperCamelCase							,      {
						        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
						        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
						        '''num_beams''': np.array(_UpperCamelCase	),
						        '''max_length''': np.array(_UpperCamelCase	),
						        '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id	),
						    }							,      )

						np.testing.assert_allclose(summary_ids.cpu().numpy()							,      ort_out[0]							,      rtol=1E-3							,      atol=1E-3	)

						logger.info('''Model outputs from torch and ONNX Runtime are similar.'''	)
						logger.info('''Success.'''	)


def 		lowercase_	(  ):


			'''simple docstring'''







			__lowercase			       = parse_args()
			__lowercase			       = 5
			__lowercase			       = 4

			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''							,      datefmt='''%m/%d/%Y %H:%M:%S'''							,      level=logging.INFO							,      )

			logger.setLevel(logging.INFO	)
			transformers.utils.logging.set_verbosity_error()

			__lowercase			       = torch.device(args.device	)

			__lowercase       ,			__lowercase			       = load_model_tokenizer(args.model_name_or_path							,      _UpperCamelCase	)

			if model.config.decoder_start_token_id is None:
						raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''	)

			model.to(_UpperCamelCase	)

			if args.max_length:
						__lowercase			       = args.max_length

			if args.num_beams:
						__lowercase			       = args.num_beams

			if args.output_file_path:
						__lowercase			       = args.output_file_path
			else:
						__lowercase			       = '''BART.onnx'''

			logger.info('''Exporting model to ONNX'''	)
			export_and_validate_model(_UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	)


if __name__ == "__main__":
	main()


