



"""simple docstring"""


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   :							List[str]										=   logging.getLogger(__name__)

__A   :							Tuple										=   {"facebook/bart-base": BartForConditionalGeneration}
__A   :							Optional[Any]										=   {"facebook/bart-base": BartTokenizer}







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''				)
				parser.add_argument(
				    '''--validation_file'''					,     type=snake_case__					,     default=snake_case__					,     help='''A csv or a json file containing the validation data.'''				)
				parser.add_argument(
				    '''--max_length'''					,     type=snake_case__					,     default=5					,     help='''The maximum total input sequence length after tokenization.'''					,     )
				parser.add_argument(
				    '''--num_beams'''					,     type=snake_case__					,     default=snake_case__					,     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=snake_case__					,     help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,     required=snake_case__					,     )
				parser.add_argument(
				    '''--config_name'''					,     type=snake_case__					,     default=snake_case__					,     help='''Pretrained config name or path if not the same as model_name'''					,     )
				parser.add_argument(
				    '''--device'''					,     type=snake_case__					,     default='''cpu'''					,     help='''Device where the model will be run'''					,     )
				parser.add_argument('''--output_file_path'''					,     type=snake_case__					,     default=snake_case__					,     help='''Where to store the final ONNX file.'''				)

				_UpperCAmelCase					=    parser.parse_args()

				return args







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						Any="cpu"				):






				'''simple docstring'''



				_UpperCAmelCase					=    model_dict[model_name].from_pretrained(snake_case__				).to(snake_case__				)
				_UpperCAmelCase					=    tokenizer_dict[model_name].from_pretrained(snake_case__				)

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

				return huggingface_model, tokenizer







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				model.eval()

				_UpperCAmelCase					=    None
				_UpperCAmelCase					=    torch.jit.script(BARTBeamSearchGenerator(snake_case__				)				)

				with torch.no_grad():
								_UpperCAmelCase					=    '''My friends are cool but they eat too many carbs.'''
								_UpperCAmelCase					=    tokenizer([ARTICLE_TO_SUMMARIZE]					,     max_length=1024					,     return_tensors='''pt'''				).to(model.device				)

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

								torch.onnx.export(
								    snake_case__					,     (
								        inputs['''input_ids'''],
								        inputs['''attention_mask'''],
								        num_beams,
								        max_length,
								        model.config.decoder_start_token_id,
								    )					,     snake_case__					,     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=snake_case__					,     )

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

								_UpperCAmelCase					=    remove_dup_initializers(os.path.abspath(snake_case__				)				)

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

								_UpperCAmelCase					=    onnxruntime.InferenceSession(snake_case__				)
								_UpperCAmelCase					=    ort_sess.run(
								    snake_case__					,     {
								        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
								        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
								        '''num_beams''': np.array(snake_case__				),
								        '''max_length''': np.array(snake_case__				),
								        '''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'''



				_UpperCAmelCase					=    parse_args()
				_UpperCAmelCase					=    5
				_UpperCAmelCase					=    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()

				_UpperCAmelCase					=    torch.device(args.device				)

				_UpperCAmelCase		,						_UpperCAmelCase					=    load_model_tokenizer(args.model_name_or_path					,     snake_case__				)

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

				model.to(snake_case__				)

				if args.max_length:
								_UpperCAmelCase					=    args.max_length

				if args.num_beams:
								_UpperCAmelCase					=    args.num_beams

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

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


if __name__ == "__main__":
						main()








"""simple docstring"""


from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :							Any										=   logging.get_logger(__name__)

__A   :							Dict										=   {
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""informer"""
			UpperCamelCase__			      =	{
			    """hidden_size""": """d_model""",
			    """num_attention_heads""": """encoder_attention_heads""",
			    """num_hidden_layers""": """encoder_layers""",
			}



			def __init__(		self     :    List[str]				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    str = "student_t"				,   __UpperCamelCase     :    str = "nll"				,   __UpperCamelCase     :    int = 1				,   __UpperCamelCase     :    List[int] = None				,   __UpperCamelCase     :    Optional[Union[str, bool]] = "mean"				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    Optional[List[int]] = None				,   __UpperCamelCase     :    Optional[List[int]] = None				,   __UpperCamelCase     :    int = 6_4				,   __UpperCamelCase     :    int = 3_2				,   __UpperCamelCase     :    int = 3_2				,   __UpperCamelCase     :    int = 2				,   __UpperCamelCase     :    int = 2				,   __UpperCamelCase     :    int = 2				,   __UpperCamelCase     :    int = 2				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    str = "gelu"				,   __UpperCamelCase     :    float = 0.0_5				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    int = 1_0_0				,   __UpperCamelCase     :    float = 0.0_2				,   __UpperCamelCase     :    Optional[Any]=True				,   __UpperCamelCase     :    str = "prob"				,   __UpperCamelCase     :    int = 5				,   __UpperCamelCase     :    bool = True				,   **__UpperCamelCase     :    Union[str, Any]				,   )->Dict:
							# time series specific configuration
							_UpperCAmelCase					=    prediction_length
							_UpperCAmelCase					=    context_length or prediction_length
							_UpperCAmelCase					=    distribution_output
							_UpperCAmelCase					=    loss
							_UpperCAmelCase					=    input_size
							_UpperCAmelCase					=    num_time_features
							_UpperCAmelCase					=    lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
							_UpperCAmelCase					=    scaling
							_UpperCAmelCase					=    num_dynamic_real_features
							_UpperCAmelCase					=    num_static_real_features
							_UpperCAmelCase					=    num_static_categorical_features

							# set cardinality
							if cardinality and num_static_categorical_features > 0:
											if len(__UpperCamelCase   ) != num_static_categorical_features:
															raise ValueError(
															    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''   )
											_UpperCAmelCase					=    cardinality
							else:
											_UpperCAmelCase					=    [0]

							# set embedding_dimension
							if embedding_dimension and num_static_categorical_features > 0:
											if len(__UpperCamelCase   ) != num_static_categorical_features:
															raise ValueError(
															    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''   )
											_UpperCAmelCase					=    embedding_dimension
							else:
											_UpperCAmelCase					=    [min(5_0				,   (cat + 1) // 2   ) for cat in self.cardinality]

							_UpperCAmelCase					=    num_parallel_samples

							# Transformer architecture configuration
							_UpperCAmelCase					=    input_size * len(self.lags_sequence   ) + self._number_of_features
							_UpperCAmelCase					=    d_model
							_UpperCAmelCase					=    encoder_attention_heads
							_UpperCAmelCase					=    decoder_attention_heads
							_UpperCAmelCase					=    encoder_ffn_dim
							_UpperCAmelCase					=    decoder_ffn_dim
							_UpperCAmelCase					=    encoder_layers
							_UpperCAmelCase					=    decoder_layers

							_UpperCAmelCase					=    dropout
							_UpperCAmelCase					=    attention_dropout
							_UpperCAmelCase					=    activation_dropout
							_UpperCAmelCase					=    encoder_layerdrop
							_UpperCAmelCase					=    decoder_layerdrop

							_UpperCAmelCase					=    activation_function
							_UpperCAmelCase					=    init_std

							_UpperCAmelCase					=    use_cache

							# Informer
							_UpperCAmelCase					=    attention_type
							_UpperCAmelCase					=    sampling_factor
							_UpperCAmelCase					=    distil

							super().__init__(is_encoder_decoder=__UpperCamelCase				,   **__UpperCamelCase   )



			@property
			def  lowercase__						(		self     :    Optional[int]   )->int:
							return (
							    sum(self.embedding_dimension   )
							    + self.num_dynamic_real_features
							    + self.num_time_features
							    + self.num_static_real_features
							    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
							)



