


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       :		Union[str, Any]         =				logging.getLogger(__name__)

SCREAMING_SNAKE_CASE       :		Any         =				{"facebook/bart-base": BartForConditionalGeneration}
SCREAMING_SNAKE_CASE       :		Tuple         =				{"facebook/bart-base": BartTokenizer}




def     UpperCamelCase    (      )					->							List[str]:
					'''simple docstring'''





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

					lowercase_       :int     =       parser.parse_args()

					return args




def     UpperCamelCase    (      _a       ,		_a="cpu"		)					->							int:
					'''simple docstring'''





					lowercase_       :Optional[int]     =       model_dict[model_name].from_pretrained(_a		).to(_a		)
					lowercase_       :Optional[Any]     =       tokenizer_dict[model_name].from_pretrained(_a		)

					if model_name in ["facebook/bart-base"]:
										lowercase_       :List[str]     =       0
										lowercase_       :List[Any]     =       None
										lowercase_       :str     =       0

					return huggingface_model, tokenizer




def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a       ,		_a		)					->							Any:
					'''simple docstring'''





					model.eval()

					lowercase_       :Any     =       None
					lowercase_       :List[str]     =       torch.jit.script(BARTBeamSearchGenerator(_a		)		)

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

										lowercase_       :int     =       model.generate(
										    inputs['''input_ids''']       ,		attention_mask=inputs['''attention_mask''']       ,		num_beams=_a       ,		max_length=_a       ,		early_stopping=_a       ,		decoder_start_token_id=model.config.decoder_start_token_id       ,		)

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

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

										lowercase_       :List[str]     =       remove_dup_initializers(os.path.abspath(_a		)		)

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

										lowercase_       :Tuple     =       onnxruntime.InferenceSession(_a		)
										lowercase_       :Optional[int]     =       ort_sess.run(
										    _a       ,		{
										        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
										        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
										        '''num_beams''': np.array(_a		),
										        '''max_length''': np.array(_a		),
										        '''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    (      )					->							Optional[int]:
					'''simple docstring'''





					lowercase_       :int     =       parse_args()
					lowercase_       :Any     =       5
					lowercase_       :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()

					lowercase_       :int     =       torch.device(args.device		)

					lowercase_       :str     =       load_model_tokenizer(args.model_name_or_path       ,		_a		)

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

					model.to(_a		)

					if args.max_length:
										lowercase_       :str     =       args.max_length

					if args.num_beams:
										lowercase_       :List[Any]     =       args.num_beams

					if args.output_file_path:
										lowercase_       :Dict     =       args.output_file_path
					else:
										lowercase_       :Union[str, Any]     =       '''BART.onnx'''

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


if __name__ == "__main__":
			main()





from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
   import numpy as np
   from decord import VideoReader


if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE       :		List[str]         =				logging.get_logger(__name__)






@add_end_docstrings(lowercase__     )
class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   def __init__(     self   ,							*UpperCamelCase_   ,							**UpperCamelCase_		):
        super().__init__(*UpperCamelCase_   ,							**UpperCamelCase_		)
        requires_backends(self   ,							'''decord'''		)
        self.check_model_type(UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None		):
        lowercase_       :int     =       {}
        if frame_sampling_rate is not None:
             lowercase_       :int     =       frame_sampling_rate
        if num_frames is not None:
             lowercase_       :int     =       num_frames

        lowercase_       :str     =       {}
        if top_k is not None:
             lowercase_       :Optional[int]     =       top_k
        return preprocess_params, {}, postprocess_params
   def __call__(     self   ,							UpperCamelCase_   ,							**UpperCamelCase_		):
        return super().__call__(UpperCamelCase_   ,							**UpperCamelCase_		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=1		):
        if num_frames is None:
             lowercase_       :str     =       self.model.config.num_frames

        if video.startswith('''http://'''		) or video.startswith('''https://'''		):
             lowercase_       :str     =       BytesIO(requests.get(UpperCamelCase_		).content		)

        lowercase_       :Optional[int]     =       VideoReader(UpperCamelCase_		)
        videoreader.seek(0		)

        lowercase_       :Tuple     =       0
        lowercase_       :Optional[Any]     =       num_frames * frame_sampling_rate - 1
        lowercase_       :Any     =       np.linspace(UpperCamelCase_   ,							UpperCamelCase_   ,							num=UpperCamelCase_   ,							dtype=np.intaa		)

        lowercase_       :Dict     =       videoreader.get_batch(UpperCamelCase_		).asnumpy()
        lowercase_       :List[Any]     =       list(UpperCamelCase_		)

        lowercase_       :Any     =       self.image_processor(UpperCamelCase_   ,							return_tensors=self.framework		)
        return model_inputs
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_		):
        lowercase_       :List[str]     =       self.model(**UpperCamelCase_		)
        return model_outputs




   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=5		):
        if top_k > self.model.config.num_labels:
             lowercase_       :List[str]     =       self.model.config.num_labels

        if self.framework == "pt":
             lowercase_       :Optional[int]     =       model_outputs.logits.softmax(-1		)[0]
             lowercase_    ,      lowercase_       :Dict     =       probs.topk(UpperCamelCase_		)
        else:
             raise ValueError(f"Unsupported framework: {self.framework}"		)

        lowercase_       :Dict     =       scores.tolist()
        lowercase_       :Any     =       ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase_   ,							UpperCamelCase_		)]

