


import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


a			:			Optional[Any]												=					'''\

'''

a			:			List[str]												=					'''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
'''

a			:			str												=					'''
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to \'cuda\' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        78.22
        >>> print(round(results["perplexities"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric("perplexity")
        >>> input_texts = datasets.load_dataset("wikitext",
        ...                                     "wikitext-2-raw-v1",
        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!=\'\']
        >>> results = perplexity.compute(model_id=\'gpt2\',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        [\'perplexities\', \'mean_perplexity\']
        >>> print(round(results["mean_perplexity"], 2))
        60.35
        >>> print(round(results["perplexities"][0], 2))
        81.12
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,  _KWARGS_DESCRIPTION					)
class 						lowerCamelCase_							(					datasets.Metric					):



					'''simple docstring'''







					def A   (					self    ) ->      str:







								'''simple docstring'''

								return datasets.MetricInfo(
								    description=_DESCRIPTION			,							citation=_CITATION			,							inputs_description=_KWARGS_DESCRIPTION			,							features=datasets.Features(
								        {
								            '''input_texts''': datasets.Value('''string'''    ),
								        }    )			,							reference_urls=['''https://huggingface.co/docs/transformers/perplexity''']			,							)






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = 1_6			,							snake_case_ = True			,							snake_case_=None    ) ->      Optional[int]:







								'''simple docstring'''

								if device is not None:
											assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
											if device == "gpu":
														__lowercase			       = '''cuda'''
								else:
											__lowercase			       = '''cuda''' if torch.cuda.is_available() else '''cpu'''

								__lowercase			       = AutoModelForCausalLM.from_pretrained(snake_case_    )
								__lowercase			       = model.to(snake_case_    )

								__lowercase			       = AutoTokenizer.from_pretrained(snake_case_    )

								# if batch_size > 1 (which generally leads to padding being required), and
								# if there is not an already assigned pad_token, assign an existing
								# special token to also be the padding token
								if tokenizer.pad_token is None and batch_size > 1:
											__lowercase			       = list(tokenizer.special_tokens_map_extended.values()    )
											# check that the model already has at least one special token defined
											assert (
											    len(snake_case_    ) > 0
											), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
											# assign one of the special tokens to also be the pad token
											tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}    )

								if add_start_token:
											# leave room for <BOS> token to be added:
											assert (
											    tokenizer.bos_token is not None
											), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
											__lowercase			       = model.config.max_length - 1
								else:
											__lowercase			       = model.config.max_length

								__lowercase			       = tokenizer(
								    snake_case_			,							add_special_tokens=snake_case_			,							padding=snake_case_			,							truncation=snake_case_			,							max_length=snake_case_			,							return_tensors='''pt'''			,							return_attention_mask=snake_case_			,							).to(snake_case_    )

								__lowercase			       = encodings['''input_ids''']
								__lowercase			       = encodings['''attention_mask''']

								# check that each input is long enough:
								if add_start_token:
											assert torch.all(torch.ge(attn_masks.sum(1    )			,							1    )    ), "Each input text must be at least one token long."
								else:
											assert torch.all(
											    torch.ge(attn_masks.sum(1    )			,							2    )    ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

								__lowercase			       = []
								__lowercase			       = CrossEntropyLoss(reduction='''none'''    )

								for start_index in logging.tqdm(range(0			,							len(snake_case_    )			,							snake_case_    )    ):
											__lowercase			       = min(start_index + batch_size			,							len(snake_case_    )    )
											__lowercase			       = encoded_texts[start_index:end_index]
											__lowercase			       = attn_masks[start_index:end_index]

											if add_start_token:
														__lowercase			       = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0    )    ).to(snake_case_    )
														__lowercase			       = torch.cat([bos_tokens_tensor, encoded_batch]			,							dim=1    )
														__lowercase			       = torch.cat(
														    [torch.ones(bos_tokens_tensor.size()			,							dtype=torch.intaa    ).to(snake_case_    ), attn_mask]			,							dim=1    )

											__lowercase			       = encoded_batch

											with torch.no_grad():
														__lowercase			       = model(snake_case_			,							attention_mask=snake_case_    ).logits

											__lowercase			       = out_logits[..., :-1, :].contiguous()
											__lowercase			       = labels[..., 1:].contiguous()
											__lowercase			       = attn_mask[..., 1:].contiguous()

											__lowercase			       = torch.expa(
											    (loss_fct(shift_logits.transpose(1			,							2    )			,							snake_case_    ) * shift_attention_mask_batch).sum(1    )
											    / shift_attention_mask_batch.sum(1    )    )

											ppls += perplexity_batch.tolist()

								return {"perplexities": ppls, "mean_perplexity": np.mean(snake_case_    )}






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()


