
"""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,
)

_a								=						logging.getLogger(__name__)

_a								=						{"""facebook/bart-base""": BartForConditionalGeneration}
_a								=						{"""facebook/bart-base""": BartTokenizer}



def  lowerCamelCase__						(     )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    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.'''      )

					_UpperCamelCase									=    parser.parse_args()

					return args



def  lowerCamelCase__						(     __snake_case,	__snake_case="cpu"      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    model_dict[model_name].from_pretrained(__snake_case      ).to(__snake_case      )
					_UpperCamelCase									=    tokenizer_dict[model_name].from_pretrained(__snake_case      )

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

					return huggingface_model, tokenizer



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					model.eval()

					_UpperCamelCase									=    None
					_UpperCamelCase									=    torch.jit.script(BARTBeamSearchGenerator(__snake_case      )      )

					with torch.no_grad():
										_UpperCamelCase									=    '''My friends are cool but they eat too many carbs.'''
										_UpperCamelCase									=    tokenizer([ARTICLE_TO_SUMMARIZE],	max_length=10_24,	return_tensors='''pt'''      ).to(model.device      )

										_UpperCamelCase									=    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=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=__snake_case,	)

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

										_UpperCamelCase									=    remove_dup_initializers(os.path.abspath(__snake_case      )      )

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

										_UpperCamelCase									=    onnxruntime.InferenceSession(__snake_case      )
										_UpperCamelCase									=    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  lowerCamelCase__						(     )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    parse_args()
					_UpperCamelCase									=    5
					_UpperCamelCase									=    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()

					_UpperCamelCase									=    torch.device(args.device      )

					_UpperCamelCase					,    _UpperCamelCase									=    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:
										_UpperCamelCase									=    args.max_length

					if args.num_beams:
										_UpperCamelCase									=    args.num_beams

					if args.output_file_path:
										_UpperCamelCase									=    args.output_file_path
					else:
										_UpperCamelCase									=    '''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()








"""simple docstring"""





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['vqvae']



		def __init__(   self							,						__a							,						__a							,						__a							,						__a							,						)			->			List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=__a							,						scheduler=__a							,						mel=__a							,						vqvae=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return 50 if isinstance(self.scheduler							,						__a) else 10_00



		@torch.no_grad()
		def __call__(   self							,						__a = 1							,						__a = None							,						__a = None							,						__a = 0							,						__a = 0							,						__a = None							,						__a = None							,						__a = 0							,						__a = 0							,						__a = None							,						__a = 0							,						__a = None							,						__a = None							,						__a=True							,						)			->			Union[
		    Union[AudioPipelineOutput, ImagePipelineOutput],
		    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
		]:






							'''simple docstring'''
							_UpperCamelCase									=    steps or self.get_default_steps()
							self.scheduler.set_timesteps(__a)
							_UpperCamelCase									=    step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size) == int:
												_UpperCamelCase									=    (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
												_UpperCamelCase									=    randn_tensor(
												    (
												        batch_size,
												        self.unet.config.in_channels,
												        self.unet.config.sample_size[0],
												        self.unet.config.sample_size[1],
												    )							,						generator=__a							,						device=self.device							,						)
							_UpperCamelCase									=    noise
							_UpperCamelCase									=    None

							if audio_file is not None or raw_audio is not None:
												self.mel.load_audio(__a							,						__a)
												_UpperCamelCase									=    self.mel.audio_slice_to_image(__a)
												_UpperCamelCase									=    np.frombuffer(input_image.tobytes()							,						dtype='''uint8''').reshape(
												    (input_image.height, input_image.width))
												_UpperCamelCase									=    (input_image / 2_55) * 2 - 1
												_UpperCamelCase									=    torch.tensor(input_image[np.newaxis, :, :]							,						dtype=torch.float).to(self.device)

												if self.vqvae is not None:
																	_UpperCamelCase									=    self.vqvae.encode(torch.unsqueeze(__a							,						0)).latent_dist.sample(
																	    generator=__a)[0]
																	_UpperCamelCase									=    self.vqvae.config.scaling_factor * input_images

												if start_step > 0:
																	_UpperCamelCase									=    self.scheduler.add_noise(__a							,						__a							,						self.scheduler.timesteps[start_step - 1])

												_UpperCamelCase									=    (
												    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
												)
												_UpperCamelCase									=    int(mask_start_secs * pixels_per_second)
												_UpperCamelCase									=    int(mask_end_secs * pixels_per_second)
												_UpperCamelCase									=    self.scheduler.add_noise(__a							,						__a							,						torch.tensor(self.scheduler.timesteps[start_step:]))

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
												if isinstance(self.unet							,						__a):
																	_UpperCamelCase									=    self.unet(__a							,						__a							,						__a)['''sample''']
												else:
																	_UpperCamelCase									=    self.unet(__a							,						__a)['''sample''']

												if isinstance(self.scheduler							,						__a):
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						eta=__a							,						generator=__a							,						)['''prev_sample''']
												else:
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						generator=__a							,						)['''prev_sample''']

												if mask is not None:
																	if mask_start > 0:
																						_UpperCamelCase									=    mask[:, step, :, :mask_start]
																	if mask_end > 0:
																						_UpperCamelCase									=    mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
												# 0.18215 was scaling factor used in training to ensure unit variance
												_UpperCamelCase									=    1 / self.vqvae.config.scaling_factor * images
												_UpperCamelCase									=    self.vqvae.decode(__a)['''sample''']

							_UpperCamelCase									=    (images / 2 + 0.5).clamp(0							,						1)
							_UpperCamelCase									=    images.cpu().permute(0							,						2							,						3							,						1).numpy()
							_UpperCamelCase									=    (images * 2_55).round().astype('''uint8''')
							_UpperCamelCase									=    list(
							    (Image.fromarray(_[:, :, 0]) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__a							,						mode='''RGB''').convert('''L''') for _ in images))

							_UpperCamelCase									=    [self.mel.image_to_audio(__a) for _ in images]
							if not return_dict:
												return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__a)[:, np.newaxis, :])							,						**ImagePipelineOutput(__a))



		@torch.no_grad()
		def 		UpperCAmelCase				(   self							,						__a							,						__a = 50)			->			np.ndarray:






							'''simple docstring'''
							assert isinstance(self.scheduler							,						__a)
							self.scheduler.set_timesteps(__a)
							_UpperCamelCase									=    np.array(
							    [np.frombuffer(image.tobytes()							,						dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
							_UpperCamelCase									=    (sample / 2_55) * 2 - 1
							_UpperCamelCase									=    torch.Tensor(__a).to(self.device)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps							,						(0,))):
												_UpperCamelCase									=    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
												_UpperCamelCase									=    self.scheduler.alphas_cumprod[t]
												_UpperCamelCase									=    (
												    self.scheduler.alphas_cumprod[prev_timestep]
												    if prev_timestep >= 0
												    else self.scheduler.final_alpha_cumprod
												)
												_UpperCamelCase									=    1 - alpha_prod_t
												_UpperCamelCase									=    self.unet(__a							,						__a)['''sample''']
												_UpperCamelCase									=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
												_UpperCamelCase									=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
												_UpperCamelCase									=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




		@staticmethod
		def 		UpperCAmelCase				(   __a							,						__a							,						__a)			->			torch.Tensor:






							'''simple docstring'''
							_UpperCamelCase									=    acos(torch.dot(torch.flatten(__a)							,						torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a))
							return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)






