






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     							=						logging.getLogger(__name__)

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




def 					lowercase__				(					):


     '''simple docstring'''


     UpperCAmelCase_   :				Optional[Any]						=						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_   :				List[str]						=						parser.parse_args()

     return args




def 					lowercase__				(					__snake_case   :							Optional[int]					,	__snake_case   :							List[Any]="cpu"  ):


     '''simple docstring'''


     UpperCAmelCase_   :				int						=						model_dict[model_name].from_pretrained(__snake_case  ).to(__snake_case  )
     UpperCAmelCase_   :				Any						=						tokenizer_dict[model_name].from_pretrained(__snake_case  )

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

     return huggingface_model, tokenizer




def 					lowercase__				(					__snake_case   :							List[str]					,	__snake_case   :							List[Any]					,	__snake_case   :							str					,	__snake_case   :							Tuple					,	__snake_case   :							List[Any]  ):


     '''simple docstring'''


     model.eval()

     UpperCAmelCase_   :				Any						=						None
     UpperCAmelCase_   :				str						=						torch.jit.script(BARTBeamSearchGenerator(__snake_case  )  )

     with torch.no_grad():
          UpperCAmelCase_   :				Any						=						'My friends are cool but they eat too many carbs.'
          UpperCAmelCase_   :				Dict						=						tokenizer([ARTICLE_TO_SUMMARIZE]					,	max_length=1_024					,	return_tensors='pt'  ).to(model.device  )

          UpperCAmelCase_   :				Optional[Any]						=						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_   :				List[Any]						=						remove_dup_initializers(os.path.abspath(__snake_case  )  )

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

          UpperCAmelCase_   :				List[Any]						=						onnxruntime.InferenceSession(__snake_case  )
          UpperCAmelCase_   :				List[str]						=						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 					lowercase__				(					):


     '''simple docstring'''


     UpperCAmelCase_   :				Optional[Any]						=						parse_args()
     UpperCAmelCase_   :				Union[str, Any]						=						5
     UpperCAmelCase_   :				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()

     UpperCAmelCase_   :				Tuple						=						torch.device(args.device  )

     UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						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_   :				Optional[int]						=						args.max_length

     if args.num_beams:
          UpperCAmelCase_   :				List[str]						=						args.num_beams

     if args.output_file_path:
          UpperCAmelCase_   :				Dict						=						args.output_file_path
     else:
          UpperCAmelCase_   :				Union[str, Any]						=						'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()














from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__UpperCAmelCase     							=						logging.get_logger(__name__)  # pylint: disable=invalid-name



class   lowerCamelCase				(_snake_case			,  _snake_case      ):







		'''simple docstring'''



		@register_to_config
		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None       )     ->							int:
							super().__init__()

							UpperCAmelCase_   :				str						=						learnable

							if self.learnable:
												assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
												assert length is not None, "learnable=True requires `length` to be set"

												UpperCAmelCase_   :				Optional[Any]						=						torch.zeros(_UpperCamelCase    ,		_UpperCamelCase       )
							else:
												UpperCAmelCase_   :				Any						=						None

							UpperCAmelCase_   :				Any						=						torch.nn.Parameter(_UpperCamelCase       )





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   VQModel
		_snake_case   :   CLIPTextModel
		_snake_case   :   CLIPTokenizer
		_snake_case   :   TransformeraDModel
		_snake_case   :   LearnedClassifierFreeSamplingEmbeddings
		_snake_case   :   VQDiffusionScheduler



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		)     ->							List[Any]:
							super().__init__()

							self.register_modules(
							    vqvae=_UpperCamelCase    ,		transformer=_UpperCamelCase    ,		text_encoder=_UpperCamelCase    ,		tokenizer=_UpperCamelCase    ,		scheduler=_UpperCamelCase    ,		learned_classifier_free_sampling_embeddings=_UpperCamelCase    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				Union[str, Any]						=						len(_UpperCamelCase       ) if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) else 1

							# get prompt text embeddings
							UpperCAmelCase_   :				str						=						self.tokenizer(
							    _UpperCamelCase    ,		padding='max_length'    ,		max_length=self.tokenizer.model_max_length    ,		return_tensors='pt'    ,		)
							UpperCAmelCase_   :				Optional[Any]						=						text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												UpperCAmelCase_   :				List[Any]						=						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
												logger.warning(
												    'The following part of your input was truncated because CLIP can only handle sequences up to'
												    f" {self.tokenizer.model_max_length} tokens: {removed_text}"       )
												UpperCAmelCase_   :				str						=						text_input_ids[:, : self.tokenizer.model_max_length]
							UpperCAmelCase_   :				str						=						self.text_encoder(text_input_ids.to(self.device       )       )[0]

							# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
							# While CLIP does normalize the pooled output of the text transformer when combining
							# the image and text embeddings, CLIP does not directly normalize the last hidden state.
							#
							# CLIP normalizing the pooled output.
							# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
							UpperCAmelCase_   :				Dict						=						prompt_embeds / prompt_embeds.norm(dim=-1    ,		keepdim=_UpperCamelCase       )

							# duplicate text embeddings for each generation per prompt
							UpperCAmelCase_   :				Dict						=						prompt_embeds.repeat_interleave(_UpperCamelCase    ,		dim=0       )

							if do_classifier_free_guidance:
												if self.learned_classifier_free_sampling_embeddings.learnable:
																	UpperCAmelCase_   :				List[str]						=						self.learned_classifier_free_sampling_embeddings.embeddings
																	UpperCAmelCase_   :				List[str]						=						negative_prompt_embeds.unsqueeze(0       ).repeat(_UpperCamelCase    ,		1    ,		1       )
												else:
																	UpperCAmelCase_   :				List[Any]						=						[''] * batch_size

																	UpperCAmelCase_   :				List[Any]						=						text_input_ids.shape[-1]
																	UpperCAmelCase_   :				Dict						=						self.tokenizer(
																	    _UpperCamelCase    ,		padding='max_length'    ,		max_length=_UpperCamelCase    ,		truncation=_UpperCamelCase    ,		return_tensors='pt'    ,		)
																	UpperCAmelCase_   :				Tuple						=						self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
																	# See comment for normalizing text embeddings
																	UpperCAmelCase_   :				Dict						=						negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1    ,		keepdim=_UpperCamelCase       )

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												UpperCAmelCase_   :				List[Any]						=						negative_prompt_embeds.shape[1]
												UpperCAmelCase_   :				Dict						=						negative_prompt_embeds.repeat(1    ,		_UpperCamelCase    ,		1       )
												UpperCAmelCase_   :				Any						=						negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,		_UpperCamelCase    ,		-1       )

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												UpperCAmelCase_   :				Union[str, Any]						=						torch.cat([negative_prompt_embeds, prompt_embeds]       )

							return prompt_embeds



		@torch.no_grad()
		def __call__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = 1_0_0    ,		_UpperCamelCase = 5.0    ,		_UpperCamelCase = 1.0    ,		_UpperCamelCase = 1    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		_UpperCamelCase = "pil"    ,		_UpperCamelCase = True    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 1    ,		)     ->							Union[ImagePipelineOutput, Tuple]:
							if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Any						=						1
							elif isinstance(_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Tuple						=						len(_UpperCamelCase       )
							else:
												raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase       )}"       )

							UpperCAmelCase_   :				Union[str, Any]						=						batch_size * num_images_per_prompt

							UpperCAmelCase_   :				Optional[int]						=						guidance_scale > 1.0

							UpperCAmelCase_   :				Any						=						self._encode_prompt(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) or callback_steps <= 0)
							):
												raise ValueError(
												    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
												    f" {type(_UpperCamelCase       )}."       )

							# get the initial completely masked latents unless the user supplied it

							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.num_latent_pixels)
							if latents is None:
												UpperCAmelCase_   :				Tuple						=						self.transformer.num_vector_embeds - 1
												UpperCAmelCase_   :				List[Any]						=						torch.full(_UpperCamelCase    ,		_UpperCamelCase       ).to(self.device       )
							else:
												if latents.shape != latents_shape:
																	raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"       )
												if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																	raise ValueError(
																	    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
																	    f" {self.transformer.num_vector_embeds - 1} (inclusive)."       )
												UpperCAmelCase_   :				Any						=						latents.to(self.device       )

							# set timesteps
							self.scheduler.set_timesteps(_UpperCamelCase    ,		device=self.device       )

							UpperCAmelCase_   :				List[str]						=						self.scheduler.timesteps.to(self.device       )

							UpperCAmelCase_   :				Union[str, Any]						=						latents

							for i, t in enumerate(self.progress_bar(_UpperCamelCase       )       ):
												# expand the sample if we are doing classifier free guidance
												UpperCAmelCase_   :				Union[str, Any]						=						torch.cat([sample] * 2       ) if do_classifier_free_guidance else sample

												# predict the un-noised image
												# model_output == `log_p_x_0`
												UpperCAmelCase_   :				Dict						=						self.transformer(_UpperCamelCase    ,		encoder_hidden_states=_UpperCamelCase    ,		timestep=_UpperCamelCase       ).sample

												if do_classifier_free_guidance:
																	UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						model_output.chunk(2       )
																	UpperCAmelCase_   :				Optional[int]						=						model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(_UpperCamelCase    ,		dim=1    ,		keepdim=_UpperCamelCase       )

												UpperCAmelCase_   :				str						=						self.truncate(_UpperCamelCase    ,		_UpperCamelCase       )

												# remove `log(0)`'s (`-inf`s)
												UpperCAmelCase_   :				Optional[int]						=						model_output.clamp(-7_0       )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_   :				Union[str, Any]						=						self.scheduler.step(_UpperCamelCase    ,		timestep=_UpperCamelCase    ,		sample=_UpperCamelCase    ,		generator=_UpperCamelCase       ).prev_sample

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																	callback(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				str						=						self.vqvae.config.vq_embed_dim
							UpperCAmelCase_   :				Optional[int]						=						(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
							UpperCAmelCase_   :				int						=						self.vqvae.quantize.get_codebook_entry(_UpperCamelCase    ,		shape=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						self.vqvae.decode(_UpperCamelCase    ,		force_not_quantize=_UpperCamelCase       ).sample

							UpperCAmelCase_   :				List[str]						=						(image / 2 + 0.5).clamp(0    ,		1       )
							UpperCAmelCase_   :				Any						=						image.cpu().permute(0    ,		2    ,		3    ,		1       ).numpy()

							if output_type == "pil":
												UpperCAmelCase_   :				int						=						self.numpy_to_pil(_UpperCamelCase       )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							torch.FloatTensor:
							UpperCAmelCase_	,  UpperCAmelCase_   :				int						=						torch.sort(_UpperCamelCase    ,		1    ,		descending=_UpperCamelCase       )
							UpperCAmelCase_   :				Dict						=						torch.exp(_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						sorted_p_x_0.cumsum(dim=1       ) < truncation_rate

							# Ensure that at least the largest probability is not zeroed out
							UpperCAmelCase_   :				Tuple						=						torch.full_like(keep_mask[:, 0:1, :]    ,		_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						torch.cat((all_true, keep_mask)    ,		dim=1       )
							UpperCAmelCase_   :				int						=						keep_mask[:, :-1, :]

							UpperCAmelCase_   :				Any						=						keep_mask.gather(1    ,		indices.argsort(1       )       )

							UpperCAmelCase_   :				str						=						log_p_x_0.clone()

							UpperCAmelCase_   :				Any						=						-torch.inf  # -inf = log(0)

							return rv






