




from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor




class 			_snake_case		(     __UpperCamelCase						,						__UpperCamelCase							):
			lowerCAmelCase_					:				Union[str, Any]							=   "pixel_values"
			lowerCAmelCase_					:				Union[str, Any]							=   False
			lowerCAmelCase_					:				str							=   TimmBackboneConfig




			def __init__( self    ,							a__    ,							**a__							)    ->    List[Any]:

										'''simple docstring'''




										requires_backends(self    ,							"timm"							)
										super().__init__(_lowerCAmelCase							)
										snake_case_      =				config

										if config.backbone is None:
																	raise ValueError("backbone is not set in the config. Please set it to a timm model name."							)

										if config.backbone not in timm.list_models():
																	raise ValueError(F'backbone {config.backbone} is not supported by timm.'							)

										if hasattr(_lowerCAmelCase    ,							"out_features"							) and config.out_features is not None:
																	raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead."							)

										snake_case_      =				getattr(_lowerCAmelCase    ,							"use_pretrained_backbone"    ,							_lowerCAmelCase							)
										if pretrained is None:
																	raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False."							)

										# We just take the final layer by default. This matches the default for the transformers models.
										snake_case_      =				config.out_indices if getattr(_lowerCAmelCase    ,							"out_indices"    ,							_lowerCAmelCase							) is not None else (-1,)

										snake_case_      =				timm.create_model(
										    config.backbone    ,							pretrained=_lowerCAmelCase    ,							features_only=config.features_only    ,							in_chans=config.num_channels    ,							out_indices=_lowerCAmelCase    ,							**_lowerCAmelCase    ,							)
										# These are used to control the output of the model when called. If output_hidden_states is True, then
										# return_layers is modified to include all layers.
										snake_case_      =				self._backbone.return_layers
										snake_case_      =				{layer["""module"""]: str(_lowerCAmelCase							) for i, layer in enumerate(self._backbone.feature_info.info							)}
										super()._init_backbone(_lowerCAmelCase							)




			@classmethod
			def        lowerCAmelCase__    ( cls    ,							a__    ,							*a__    ,							**a__							)    ->    Optional[int]:

										'''simple docstring'''




										requires_backends(cls    ,							["vision", "timm"]							)
										from ...models.timm_backbone import TimmBackboneConfig

										snake_case_      =				kwargs.pop("config"    ,							TimmBackboneConfig()							)

										snake_case_      =				kwargs.pop("use_timm_backbone"    ,							_lowerCAmelCase							)
										if not use_timm:
																	raise ValueError("use_timm_backbone must be True for timm backbones"							)

										snake_case_      =				kwargs.pop("num_channels"    ,							config.num_channels							)
										snake_case_      =				kwargs.pop("features_only"    ,							config.features_only							)
										snake_case_      =				kwargs.pop("use_pretrained_backbone"    ,							config.use_pretrained_backbone							)
										snake_case_      =				kwargs.pop("out_indices"    ,							config.out_indices							)
										snake_case_      =				TimmBackboneConfig(
										    backbone=_lowerCAmelCase    ,							num_channels=_lowerCAmelCase    ,							features_only=_lowerCAmelCase    ,							use_pretrained_backbone=_lowerCAmelCase    ,							out_indices=_lowerCAmelCase    ,							)
										return super()._from_config(_lowerCAmelCase    ,							**_lowerCAmelCase							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    str:

										'''simple docstring'''




										pass




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__=None    ,							a__=None    ,							a__=None    ,							**a__							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				return_dict if return_dict is not None else self.config.use_return_dict
										snake_case_      =				(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										snake_case_      =				output_attentions if output_attentions is not None else self.config.output_attentions

										if output_attentions:
																	raise ValueError("Cannot output attentions for timm backbones at the moment"							)

										if output_hidden_states:
																	# We modify the return layers to include all the stages of the backbone
																	snake_case_      =				self._all_layers
																	snake_case_      =				self._backbone(_lowerCAmelCase    ,							**_lowerCAmelCase							)
																	snake_case_      =				self._return_layers
																	snake_case_      =				tuple(hidden_states[i] for i in self.out_indices							)
										else:
																	snake_case_      =				self._backbone(_lowerCAmelCase    ,							**_lowerCAmelCase							)
																	snake_case_      =				None

										snake_case_      =				tuple(_lowerCAmelCase							)
										snake_case_      =				tuple(_lowerCAmelCase							) if hidden_states is not None else None

										if not return_dict:
																	snake_case_      =				(feature_maps,)
																	if output_hidden_states:
																								snake_case_      =				output + (hidden_states,)
																	return output

										return BackboneOutput(feature_maps=_lowerCAmelCase    ,							hidden_states=_lowerCAmelCase    ,							attentions=_lowerCAmelCase							)










'''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()




