


'''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,
)

UpperCAmelCase_						:  Any				  =						logging.getLogger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{"""facebook/bart-base""": BartForConditionalGeneration}
UpperCAmelCase_						:  str				  =						{"""facebook/bart-base""": BartTokenizer}







def      snake_case_  (    ):







				"""simple docstring"""





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

				_SCREAMING_SNAKE_CASE					:			int							= parser.parse_args()

				return args







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__="cpu"	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			str							= model_dict[model_name].from_pretrained(lowerCAmelCase_	).to(lowerCAmelCase_	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= tokenizer_dict[model_name].from_pretrained(lowerCAmelCase_	)

				if model_name in ["facebook/bart-base"]:
								_SCREAMING_SNAKE_CASE					:			Tuple							= 0
								_SCREAMING_SNAKE_CASE					:			str							= None
								_SCREAMING_SNAKE_CASE					:			int							= 0

				return huggingface_model, tokenizer







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				model.eval()

				_SCREAMING_SNAKE_CASE					:			Tuple							= None
				_SCREAMING_SNAKE_CASE					:			Dict							= torch.jit.script(BARTBeamSearchGenerator(lowerCAmelCase_	)	)

				with torch.no_grad():
								_SCREAMING_SNAKE_CASE					:			List[Any]							= """My friends are cool but they eat too many carbs."""
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= tokenizer([ARTICLE_TO_SUMMARIZE]				, max_length=1024				, return_tensors="""pt"""	).to(model.device	)

								_SCREAMING_SNAKE_CASE					:			List[str]							= model.generate(
								    inputs["""input_ids"""]				, attention_mask=inputs["""attention_mask"""]				, num_beams=lowerCAmelCase_				, max_length=lowerCAmelCase_				, early_stopping=lowerCAmelCase_				, decoder_start_token_id=model.config.decoder_start_token_id				, )

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

								logger.info("""Model exported to {}""".format(lowerCAmelCase_	)	)

								_SCREAMING_SNAKE_CASE					:			Tuple							= remove_dup_initializers(os.path.abspath(lowerCAmelCase_	)	)

								logger.info("""Deduplicated and optimized model written to {}""".format(lowerCAmelCase_	)	)

								_SCREAMING_SNAKE_CASE					:			List[str]							= onnxruntime.InferenceSession(lowerCAmelCase_	)
								_SCREAMING_SNAKE_CASE					:			List[str]							= ort_sess.run(
								    lowerCAmelCase_				, {
								        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
								        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
								        """num_beams""": np.array(lowerCAmelCase_	),
								        """max_length""": np.array(lowerCAmelCase_	),
								        """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      snake_case_  (    ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[int]							= parse_args()
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 5
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= 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()

				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.device(args.device	)

				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			int							= load_model_tokenizer(args.model_name_or_path				, lowerCAmelCase_	)

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

				model.to(lowerCAmelCase_	)

				if args.max_length:
								_SCREAMING_SNAKE_CASE					:			Dict							= args.max_length

				if args.num_beams:
								_SCREAMING_SNAKE_CASE					:			Any							= args.num_beams

				if args.output_file_path:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= args.output_file_path
				else:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= """BART.onnx"""

				logger.info("""Exporting model to ONNX"""	)
				export_and_validate_model(lowerCAmelCase_				, lowerCAmelCase_				, lowerCAmelCase_				, lowerCAmelCase_				, lowerCAmelCase_	)


if __name__ == "__main__":
				main()











import torch
from torch import nn



class 					__snake_case	(  nn.Module    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_=1   ,				A_=False     ):





        '''simple docstring'''

        super().__init__()

        SCREAMING_SNAKE_CASE__											=			n_token
        SCREAMING_SNAKE_CASE__											=			d_embed
        SCREAMING_SNAKE_CASE__											=			d_proj

        SCREAMING_SNAKE_CASE__											=			cutoffs + [n_token]
        SCREAMING_SNAKE_CASE__											=			[0] + self.cutoffs
        SCREAMING_SNAKE_CASE__											=			div_val

        SCREAMING_SNAKE_CASE__											=			self.cutoffs[0]
        SCREAMING_SNAKE_CASE__											=			len(self.cutoffs     ) - 1
        SCREAMING_SNAKE_CASE__											=			self.shortlist_size + self.n_clusters

        if self.n_clusters > 0:
            SCREAMING_SNAKE_CASE__											=			nn.Parameter(torch.zeros(self.n_clusters   ,				self.d_embed     )     )
            SCREAMING_SNAKE_CASE__											=			nn.Parameter(torch.zeros(self.n_clusters     )     )

        SCREAMING_SNAKE_CASE__											=			nn.ModuleList()
        SCREAMING_SNAKE_CASE__											=			nn.ParameterList()

        if div_val == 1:
            for i in range(len(self.cutoffs     )     ):
                if d_proj != d_embed:
                    self.out_projs.append(nn.Parameter(torch.FloatTensor(A_   ,				A_     )     )     )
                else:
                    self.out_projs.append(A_     )

            self.out_layers.append(nn.Linear(A_   ,				A_     )     )
        else:
            for i in range(len(self.cutoffs     )     ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                SCREAMING_SNAKE_CASE__											=			d_embed // (div_val**i)

                self.out_projs.append(nn.Parameter(torch.FloatTensor(A_   ,				A_     )     )     )

                self.out_layers.append(nn.Linear(A_   ,				r_idx - l_idx     )     )

        SCREAMING_SNAKE_CASE__											=			keep_order



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        if proj is None:
            SCREAMING_SNAKE_CASE__											=			nn.functional.linear(A_   ,				A_   ,				bias=A_     )
        else:
            # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
            SCREAMING_SNAKE_CASE__											=			nn.functional.linear(A_   ,				proj.t().contiguous()     )
            SCREAMING_SNAKE_CASE__											=			nn.functional.linear(A_   ,				A_   ,				bias=A_     )
            # else:
            #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
            #     if bias is not None:
            #         logit = logit + bias

        return logit



    def  lowercase_					(       self   ,				A_   ,				A_=None   ,				A_=False     ):





        '''simple docstring'''

        if labels is not None:
            # Shift so that tokens < n predict n
            SCREAMING_SNAKE_CASE__											=			hidden[..., :-1, :].contiguous()
            SCREAMING_SNAKE_CASE__											=			labels[..., 1:].contiguous()
            SCREAMING_SNAKE_CASE__											=			hidden.view(-1   ,				hidden.size(-1     )     )
            SCREAMING_SNAKE_CASE__											=			labels.view(-1     )
            if hidden.size(0     ) != labels.size(0     ):
                raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''     )
        else:
            SCREAMING_SNAKE_CASE__											=			hidden.view(-1   ,				hidden.size(-1     )     )

        if self.n_clusters == 0:
            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				self.out_layers[0].weight   ,				self.out_layers[0].bias   ,				self.out_projs[0]     )
            if labels is not None:
                SCREAMING_SNAKE_CASE__											=			labels != -1_00
                SCREAMING_SNAKE_CASE__											=			torch.zeros_like(A_   ,				dtype=hidden.dtype   ,				device=hidden.device     )
                SCREAMING_SNAKE_CASE__											=			(
                    -nn.functional.log_softmax(A_   ,				dim=-1     )[mask].gather(1   ,				labels[mask].unsqueeze(1     )     ).squeeze(1     )
                )
            else:
                SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=-1     )
        else:
            # construct weights and biases
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			[], []
            for i in range(len(self.cutoffs     )     ):
                if self.div_val == 1:
                    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[0].weight[l_idx:r_idx]
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[0].bias[l_idx:r_idx]
                else:
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[i].weight
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[i].bias

                if i == 0:
                    SCREAMING_SNAKE_CASE__											=			torch.cat([weight_i, self.cluster_weight]   ,				dim=0     )
                    SCREAMING_SNAKE_CASE__											=			torch.cat([bias_i, self.cluster_bias]   ,				dim=0     )

                weights.append(A_     )
                biases.append(A_     )

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[0], biases[0], self.out_projs[0]

            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )
            SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=1     )

            if labels is None:
                SCREAMING_SNAKE_CASE__											=			hidden.new_empty((head_logit.size(0     ), self.n_token)     )
            else:
                SCREAMING_SNAKE_CASE__											=			torch.zeros_like(A_   ,				dtype=hidden.dtype   ,				device=hidden.device     )

            SCREAMING_SNAKE_CASE__											=			0
            SCREAMING_SNAKE_CASE__											=			[0] + self.cutoffs
            for i in range(len(A_     ) - 1     ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			cutoff_values[i], cutoff_values[i + 1]

                if labels is not None:
                    SCREAMING_SNAKE_CASE__											=			(labels >= l_idx) & (labels < r_idx)
                    SCREAMING_SNAKE_CASE__											=			mask_i.nonzero().squeeze()

                    if indices_i.numel() == 0:
                        continue

                    SCREAMING_SNAKE_CASE__											=			labels.index_select(0   ,				A_     ) - l_idx
                    SCREAMING_SNAKE_CASE__											=			head_logprob.index_select(0   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			hidden.index_select(0   ,				A_     )
                else:
                    SCREAMING_SNAKE_CASE__											=			hidden

                if i == 0:
                    if labels is not None:
                        SCREAMING_SNAKE_CASE__											=			head_logprob_i.gather(1   ,				target_i[:, None]     ).squeeze(1     )
                    else:
                        SCREAMING_SNAKE_CASE__											=			head_logprob[:, : self.cutoffs[0]]
                else:
                    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[i], biases[i], self.out_projs[i]

                    SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=1     )
                    SCREAMING_SNAKE_CASE__											=			self.cutoffs[0] + i - 1  # No probability for the head cluster
                    if labels is not None:
                        SCREAMING_SNAKE_CASE__											=			head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
                            1   ,				target_i[:, None]     ).squeeze(1     )
                    else:
                        SCREAMING_SNAKE_CASE__											=			head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
                        SCREAMING_SNAKE_CASE__											=			logprob_i

                if labels is not None:
                    if (hasattr(self   ,				'''keep_order'''     ) and self.keep_order) or keep_order:
                        out.index_copy_(0   ,				A_   ,				-logprob_i     )
                    else:
                        out[offset : offset + logprob_i.size(0     )].copy_(-logprob_i     )
                    offset += logprob_i.size(0     )

        return out




    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        if self.n_clusters == 0:
            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				self.out_layers[0].weight   ,				self.out_layers[0].bias   ,				self.out_projs[0]     )
            return nn.functional.log_softmax(A_   ,				dim=-1     )
        else:
            # construct weights and biases
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			[], []
            for i in range(len(self.cutoffs     )     ):
                if self.div_val == 1:
                    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[0].weight[l_idx:r_idx]
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[0].bias[l_idx:r_idx]
                else:
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[i].weight
                    SCREAMING_SNAKE_CASE__											=			self.out_layers[i].bias

                if i == 0:
                    SCREAMING_SNAKE_CASE__											=			torch.cat([weight_i, self.cluster_weight]   ,				dim=0     )
                    SCREAMING_SNAKE_CASE__											=			torch.cat([bias_i, self.cluster_bias]   ,				dim=0     )

                weights.append(A_     )
                biases.append(A_     )

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[0], biases[0], self.out_projs[0]
            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )

            SCREAMING_SNAKE_CASE__											=			hidden.new_empty((head_logit.size(0     ), self.n_token)     )
            SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=1     )

            SCREAMING_SNAKE_CASE__											=			[0] + self.cutoffs
            for i in range(len(A_     ) - 1     ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			cutoff_values[i], cutoff_values[i + 1]

                if i == 0:
                    SCREAMING_SNAKE_CASE__											=			head_logprob[:, : self.cutoffs[0]]
                else:
                    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[i], biases[i], self.out_projs[i]

                    SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=1     )

                    SCREAMING_SNAKE_CASE__											=			head_logprob[:, -i] + tail_logprob_i
                    SCREAMING_SNAKE_CASE__											=			logprob_i

            return out
