





from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
							from fairscale.optim import OSS


UpperCAmelCase__						:			Optional[int]									=logging.get_logger(__name__)

UpperCAmelCase__						:			Any									={
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}

class       __A     (						__lowerCamelCase    ):


				def __init__(		self   , UpperCAmelCase_=None   , UpperCAmelCase_=None   , *UpperCAmelCase_   , **UpperCAmelCase_							):
								super().__init__(*SCREAMING_SNAKE_CASE_   , **SCREAMING_SNAKE_CASE_							)

								if config is None:
												assert isinstance(self.model   , SCREAMING_SNAKE_CASE_							), (
												    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
												    f""" {self.model.__class__}"""
												)
												lowerCamelCase	 =self.model.config
								else:
												lowerCamelCase	 =config

								lowerCamelCase	 =data_args
								lowerCamelCase	 =self.config.tgt_vocab_size if isinstance(self.config   , SCREAMING_SNAKE_CASE_							) else self.config.vocab_size

								if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
												assert self.config.pad_token_id is not None, (
												    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
												    " calculation or doing label smoothing."
												)

								if self.config.pad_token_id is None and self.config.eos_token_id is not None:
												logger.warning(
												    f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
												    """ padding.."""							)

								if self.args.label_smoothing == 0:
												lowerCamelCase	 =torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id							)
								else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												lowerCamelCase	 =label_smoothed_nll_loss


				def _snake_case       (		self   , UpperCAmelCase_							):
								if self.optimizer is None:
												lowerCamelCase	 =["""bias""", """LayerNorm.weight"""]
												lowerCamelCase	 =[
												    {
												        """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay							)],
												        """weight_decay""": self.args.weight_decay,
												    },
												    {
												        """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay							)],
												        """weight_decay""": 0.0,
												    },
												]
												lowerCamelCase	 =Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
																lowerCamelCase	 =Adafactor
																lowerCamelCase	 ={"""scale_parameter""": False, """relative_step""": False}
												else:
																lowerCamelCase	 =AdamW
																lowerCamelCase	 ={
																    """betas""": (self.args.adam_betaa, self.args.adam_betaa),
																    """eps""": self.args.adam_epsilon,
																}
												lowerCamelCase	 =self.args.learning_rate
												if self.sharded_ddp:
																lowerCamelCase	 =OSS(
																    params=SCREAMING_SNAKE_CASE_   , optim=SCREAMING_SNAKE_CASE_   , **SCREAMING_SNAKE_CASE_   , )
												else:
																lowerCamelCase	 =optimizer_cls(SCREAMING_SNAKE_CASE_   , **SCREAMING_SNAKE_CASE_							)

								if self.lr_scheduler is None:
												lowerCamelCase	 =self._get_lr_scheduler(SCREAMING_SNAKE_CASE_							)
								else:  # ignoring --lr_scheduler
												logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =arg_to_scheduler[self.args.lr_scheduler]
								if self.args.lr_scheduler == "constant":
												lowerCamelCase	 =schedule_func(self.optimizer							)
								elif self.args.lr_scheduler == "constant_w_warmup":
												lowerCamelCase	 =schedule_func(self.optimizer   , num_warmup_steps=self.args.warmup_steps							)
								else:
												lowerCamelCase	 =schedule_func(
												    self.optimizer   , num_warmup_steps=self.args.warmup_steps   , num_training_steps=SCREAMING_SNAKE_CASE_							)
								return scheduler


				def _snake_case       (		self							):
								if isinstance(self.train_dataset   , torch.utils.data.IterableDataset							):
												return None
								elif is_torch_tpu_available():
												return get_tpu_sampler(self.train_dataset							)
								else:
												if self.args.sortish_sampler:
																self.train_dataset.make_sortish_sampler(
																    self.args.per_device_train_batch_size   , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)   , )

												return (
												    RandomSampler(self.train_dataset							)
												    if self.args.local_rank == -1
												    else DistributedSampler(self.train_dataset							)
												)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								if self.args.label_smoothing == 0:
												if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																# force training to ignore pad token
																lowerCamelCase	 =model(**SCREAMING_SNAKE_CASE_   , use_cache=SCREAMING_SNAKE_CASE_							)[0]
																lowerCamelCase	 =self.loss_fn(logits.view(-1   , logits.shape[-1]							)   , labels.view(-1							)							)
												else:
																# compute usual loss via models
																lowerCamelCase ,  lowerCamelCase	 =model(**SCREAMING_SNAKE_CASE_   , labels=SCREAMING_SNAKE_CASE_   , use_cache=SCREAMING_SNAKE_CASE_							)[:2]
								else:
												# compute label smoothed loss
												lowerCamelCase	 =model(**SCREAMING_SNAKE_CASE_   , use_cache=SCREAMING_SNAKE_CASE_							)[0]
												lowerCamelCase	 =torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_   , dim=-1							)
												lowerCamelCase ,  lowerCamelCase	 =self.loss_fn(SCREAMING_SNAKE_CASE_   , SCREAMING_SNAKE_CASE_   , self.args.label_smoothing   , ignore_index=self.config.pad_token_id							)
								return loss, logits


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =inputs.pop("""labels"""							)
								lowerCamelCase ,  lowerCamelCase	 =self._compute_loss(SCREAMING_SNAKE_CASE_   , SCREAMING_SNAKE_CASE_   , SCREAMING_SNAKE_CASE_							)
								return loss


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = None   , ):
								lowerCamelCase	 =self._prepare_inputs(SCREAMING_SNAKE_CASE_							)

								lowerCamelCase	 ={
								    """max_length""": self.data_args.val_max_target_length
								    if self.data_args is not None
								    else self.config.max_length,
								    """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
								}

								if self.args.predict_with_generate and not self.args.prediction_loss_only:
												lowerCamelCase	 =self.model.generate(
												    inputs["""input_ids"""]   , attention_mask=inputs["""attention_mask"""]   , **SCREAMING_SNAKE_CASE_   , )
												# in case the batch is shorter than max length, the output should be padded
												if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																lowerCamelCase	 =self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_   , gen_kwargs["""max_length"""]							)

								lowerCamelCase	 =inputs.pop("""labels"""							)
								with torch.no_grad():
												# compute loss on predict data
												lowerCamelCase ,  lowerCamelCase	 =self._compute_loss(SCREAMING_SNAKE_CASE_   , SCREAMING_SNAKE_CASE_   , SCREAMING_SNAKE_CASE_							)

								lowerCamelCase	 =loss.mean().detach()
								if self.args.prediction_loss_only:
												return (loss, None, None)

								lowerCamelCase	 =generated_tokens if self.args.predict_with_generate else logits

								if labels.shape[-1] < gen_kwargs["max_length"]:
												lowerCamelCase	 =self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_   , gen_kwargs["""max_length"""]							)

								return (loss, logits, labels)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_							):
								# If PAD token is not defined at least EOS token has to be defined
								lowerCamelCase	 =self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

								if pad_token_id is None:
												raise ValueError(
												    """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
												    f""" padded to `max_length`={max_length}"""							)

								lowerCamelCase	 =pad_token_id * torch.ones(
								    (tensor.shape[0], max_length)   , dtype=tensor.dtype   , device=tensor.device							)
								lowerCamelCase	 =tensor
								return padded_tensor












import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCAmelCase__						:			Tuple									=version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False			,      )						->  int:
				output_path.parent.mkdir(parents=_UpperCAmelCase			,      exist_ok=_UpperCAmelCase				)
				# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
				# so we check the torch version for backwards compatibility
				if is_torch_less_than_1_11:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      use_external_data_format=_UpperCAmelCase			,      enable_onnx_checker=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )
				else:
								export(
								    _UpperCAmelCase			,      _UpperCAmelCase			,      f=output_path.as_posix()			,      input_names=_UpperCAmelCase			,      output_names=_UpperCAmelCase			,      dynamic_axes=_UpperCAmelCase			,      do_constant_folding=_UpperCAmelCase			,      opset_version=_UpperCAmelCase			,      )





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase = False				)						->  Tuple:
				lowerCamelCase	 =torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								lowerCamelCase	 ="""cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
				else:
								lowerCamelCase	 ="""cpu"""
				lowerCamelCase	 =Path(_UpperCAmelCase				)

				# VAE DECODER
				lowerCamelCase	 =AutoencoderKL.from_pretrained(model_path + """/vae"""				)
				lowerCamelCase	 =vae_decoder.config.latent_channels
				# forward only through the decoder part
				lowerCamelCase	 =vae_decoder.decode
				onnx_export(
				    _UpperCAmelCase			,      model_args=(
				        torch.randn(1			,      _UpperCAmelCase			,      25			,      25				).to(device=_UpperCAmelCase			,      dtype=_UpperCAmelCase				),
				        False,
				    )			,      output_path=output_path / """vae_decoder""" / """model.onnx"""			,      ordered_input_names=["""latent_sample""", """return_dict"""]			,      output_names=["""sample"""]			,      dynamic_axes={
				        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
				    }			,      opset=_UpperCAmelCase			,      )
				del vae_decoder


if __name__ == "__main__":
							UpperCAmelCase__						:			Union[str, Any]									=argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_path''',
							    type=str,
							    required=True,
							    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
							)

							parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
							parser.add_argument(
							    '''--opset''',
							    default=14,
							    type=int,
							    help='''The version of the ONNX operator set to use.''',
							)
							parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

							UpperCAmelCase__						:			Union[str, Any]									=parser.parse_args()
							print(args.output_path)
							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
							print('''SD: Done: ONNX''')





