




'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_SCREAMING_SNAKE_CASE							:       Tuple						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       int						=			{
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Tuple							=   "marian"
			lowerCAmelCase_					:				int							=   ["past_key_values"]
			lowerCAmelCase_					:				Dict							=   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




			def __init__( self    ,							a__=58_101    ,							a__=None    ,							a__=1_024    ,							a__=12    ,							a__=4_096    ,							a__=16    ,							a__=12    ,							a__=4_096    ,							a__=16    ,							a__=0.0    ,							a__=0.0    ,							a__=True    ,							a__=True    ,							a__="gelu"    ,							a__=1_024    ,							a__=0.1    ,							a__=0.0    ,							a__=0.0    ,							a__=0.0_2    ,							a__=58_100    ,							a__=False    ,							a__=58_100    ,							a__=0    ,							a__=0    ,							a__=True    ,							**a__    ,							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				vocab_size
										snake_case_      =				decoder_vocab_size or vocab_size
										snake_case_      =				max_position_embeddings
										snake_case_      =				d_model
										snake_case_      =				encoder_ffn_dim
										snake_case_      =				encoder_layers
										snake_case_      =				encoder_attention_heads
										snake_case_      =				decoder_ffn_dim
										snake_case_      =				decoder_layers
										snake_case_      =				decoder_attention_heads
										snake_case_      =				dropout
										snake_case_      =				attention_dropout
										snake_case_      =				activation_dropout
										snake_case_      =				activation_function
										snake_case_      =				init_std
										snake_case_      =				encoder_layerdrop
										snake_case_      =				decoder_layerdrop
										snake_case_      =				use_cache
										snake_case_      =				encoder_layers
										snake_case_      =				scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case_      =				share_encoder_decoder_embeddings
										super().__init__(
										    pad_token_id=a__    ,							eos_token_id=a__    ,							is_encoder_decoder=a__    ,							decoder_start_token_id=a__    ,							forced_eos_token_id=a__    ,							**a__    ,							)




class 			_snake_case		(     lowercase_							):




			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
			def        lowerCAmelCase__    ( self							)    ->    Mapping[str, Mapping[int, str]]:

										'''simple docstring'''




										if self.task in ["default", "seq2seq-lm"]:
																	snake_case_      =				OrderedDict(
																	    [
																	        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																	        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																	    ]							)

																	if self.use_past:
																								snake_case_      =				{0: "batch"}
																								snake_case_      =				{0: "batch", 1: "past_decoder_sequence + sequence"}
																	else:
																								snake_case_      =				{0: "batch", 1: "decoder_sequence"}
																								snake_case_      =				{0: "batch", 1: "decoder_sequence"}

																	if self.use_past:
																								self.fill_with_past_key_values_(a__    ,							direction="inputs"							)
										elif self.task == "causal-lm":
																	# TODO: figure this case out.
																	snake_case_      =				OrderedDict(
																	    [
																	        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																	        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																	    ]							)
																	if self.use_past:
																								snake_case_ ,				snake_case_      =				self.num_layers
																								for i in range(a__							):
																															snake_case_      =				{0: "batch", 2: "past_sequence + sequence"}
																															snake_case_      =				{0: "batch", 2: "past_sequence + sequence"}
										else:
																	snake_case_      =				OrderedDict(
																	    [
																	        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																	        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																	        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																	        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																	    ]							)

										return common_inputs




			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def        lowerCAmelCase__    ( self							)    ->    Mapping[str, Mapping[int, str]]:

										'''simple docstring'''




										if self.task in ["default", "seq2seq-lm"]:
																	snake_case_      =				super().outputs
										else:
																	snake_case_      =				super(a__    ,							self							).outputs
																	if self.use_past:
																								snake_case_ ,				snake_case_      =				self.num_layers
																								for i in range(a__							):
																															snake_case_      =				{0: "batch", 2: "past_sequence + sequence"}
																															snake_case_      =				{0: "batch", 2: "past_sequence + sequence"}
										return common_outputs




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										snake_case_      =				self._generate_dummy_inputs_for_encoder_and_decoder(
										    a__    ,							a__    ,							a__    ,							a__    ,							a__							)

										# Generate decoder inputs
										snake_case_      =				seq_length if not self.use_past else 1
										snake_case_      =				self._generate_dummy_inputs_for_encoder_and_decoder(
										    a__    ,							a__    ,							a__    ,							a__    ,							a__							)
										snake_case_      =				{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
										snake_case_      =				dict(**a__    ,							**a__							)

										if self.use_past:
																	if not is_torch_available():
																								raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
																	else:
																								import torch
																	snake_case_ ,				snake_case_      =				common_inputs["input_ids"].shape
																	snake_case_      =				common_inputs["decoder_input_ids"].shape[1]
																	snake_case_ ,				snake_case_      =				self.num_attention_heads
																	snake_case_      =				(
																	    batch,
																	    num_encoder_attention_heads,
																	    encoder_seq_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)
																	snake_case_      =				decoder_seq_length + 3
																	snake_case_      =				(
																	    batch,
																	    num_decoder_attention_heads,
																	    decoder_past_length,
																	    self._config.hidden_size // num_decoder_attention_heads,
																	)

																	snake_case_      =				torch.cat(
																	    [common_inputs["decoder_attention_mask"], torch.ones(a__    ,							a__							)]    ,							dim=1							)

																	snake_case_      =				[]
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	snake_case_ ,				snake_case_      =				self.num_layers
																	snake_case_      =				min(a__    ,							a__							)
																	snake_case_      =				max(a__    ,							a__							) - min_num_layers
																	snake_case_      =				"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																	for _ in range(a__							):
																								common_inputs["past_key_values"].append(
																								    (
																								        torch.zeros(a__							),
																								        torch.zeros(a__							),
																								        torch.zeros(a__							),
																								        torch.zeros(a__							),
																								    )							)
																	# TODO: test this.
																	snake_case_      =				encoder_shape if remaining_side_name == "encoder" else decoder_shape
																	for _ in range(a__    ,							a__							):
																								common_inputs["past_key_values"].append((torch.zeros(a__							), torch.zeros(a__							))							)
										return common_inputs




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										snake_case_      =				self._generate_dummy_inputs_for_encoder_and_decoder(
										    a__    ,							a__    ,							a__    ,							a__    ,							a__							)

										if self.use_past:
																	if not is_torch_available():
																								raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."							)
																	else:
																								import torch
																	snake_case_ ,				snake_case_      =				common_inputs["input_ids"].shape
																	# Not using the same length for past_key_values
																	snake_case_      =				seqlen + 2
																	snake_case_ ,				snake_case_      =				self.num_layers
																	snake_case_ ,				snake_case_      =				self.num_attention_heads
																	snake_case_      =				(
																	    batch,
																	    num_encoder_attention_heads,
																	    past_key_values_length,
																	    self._config.hidden_size // num_encoder_attention_heads,
																	)

																	snake_case_      =				common_inputs["attention_mask"].dtype
																	snake_case_      =				torch.cat(
																	    [common_inputs["attention_mask"], torch.ones(a__    ,							a__    ,							dtype=a__							)]    ,							dim=1							)
																	snake_case_      =				[
																	    (torch.zeros(a__							), torch.zeros(a__							)) for _ in range(a__							)
																	]
										return common_inputs




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										snake_case_      =				compute_effective_axis_dimension(
										    a__    ,							fixed_dimension=OnnxConfig.default_fixed_batch    ,							num_token_to_add=0							)

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										snake_case_      =				tokenizer.num_special_tokens_to_add(a__							)
										snake_case_      =				compute_effective_axis_dimension(
										    a__    ,							fixed_dimension=OnnxConfig.default_fixed_sequence    ,							num_token_to_add=a__							)

										# Generate dummy inputs according to compute batch and sequence
										snake_case_      =				[" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
										snake_case_      =				dict(tokenizer(a__    ,							return_tensors=a__							)							)
										return common_inputs




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = -1    ,							a__ = -1    ,							a__ = False    ,							a__ = None    ,							)    ->    Mapping[str, Any]:

										'''simple docstring'''




										if self.task in ["default", "seq2seq-lm"]:
																	snake_case_      =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																	    a__    ,							batch_size=a__    ,							seq_length=a__    ,							is_pair=a__    ,							framework=a__							)

										else:
																	snake_case_      =				self._generate_dummy_inputs_for_causal_lm(
																	    a__    ,							batch_size=a__    ,							seq_length=a__    ,							is_pair=a__    ,							framework=a__							)

										return common_inputs




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__    ,							a__    ,							a__							)    ->    Any:

										'''simple docstring'''




										if self.task in ["default", "seq2seq-lm"]:
																	snake_case_      =				super()._flatten_past_key_values_(a__    ,							a__    ,							a__    ,							a__							)
										else:
																	snake_case_      =				super(a__    ,							self							)._flatten_past_key_values_(
																	    a__    ,							a__    ,							a__    ,							a__							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    float:

										'''simple docstring'''




										return 1e-4










'''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,
)

_SCREAMING_SNAKE_CASE							:       Dict						=			logging.getLogger(__name__)

_SCREAMING_SNAKE_CASE							:       List[str]						=			{"facebook/bart-base": BartForConditionalGeneration}
_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			{"facebook/bart-base": BartTokenizer}


def 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				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."					)

							snake_case_      =				parser.parse_args()

							return args


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]							,    snake_case							:	List[str]="cpu"					):

							'''simple docstring'''






							snake_case_      =				model_dict[model_name].from_pretrained(snake_case					).to(snake_case					)
							snake_case_      =				tokenizer_dict[model_name].from_pretrained(snake_case					)

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

							return huggingface_model, tokenizer


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	Tuple							,    snake_case							:	Optional[int]							,    snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






							model.eval()

							snake_case_      =				None
							snake_case_      =				torch.jit.script(BARTBeamSearchGenerator(snake_case					)					)

							with torch.no_grad():
														snake_case_      =				"My friends are cool but they eat too many carbs."
														snake_case_      =				tokenizer([ARTICLE_TO_SUMMARIZE]							,    max_length=1_0_2_4							,    return_tensors="pt"					).to(model.device					)

														snake_case_      =				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=1_4							,    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					)					)

														snake_case_      =				remove_dup_initializers(os.path.abspath(snake_case					)					)

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

														snake_case_      =				onnxruntime.InferenceSession(snake_case					)
														snake_case_      =				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 					UpperCamelCase_(     ):

							'''simple docstring'''






							snake_case_      =				parse_args()
							snake_case_      =				5
							snake_case_      =				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()

							snake_case_      =				torch.device(args.device					)

							snake_case_ ,				snake_case_      =				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:
														snake_case_      =				args.max_length

							if args.num_beams:
														snake_case_      =				args.num_beams

							if args.output_file_path:
														snake_case_      =				args.output_file_path
							else:
														snake_case_      =				"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()




