"""simple docstring"""





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


__A  =				logging.get_logger(__name__)

__A  =				{
    '''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  _snake_case     (					a__		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int=None							,					UpperCAmelCase      :			List[Any]=None							,					*UpperCAmelCase      :			int							,					**UpperCAmelCase      :			Optional[int]    ):
													super().__init__(*UpperCAmelCase							,					**UpperCAmelCase    )

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

													__lowerCamelCase					:		Dict		   =     data_args
													__lowerCamelCase					:		Any		   =     self.config.tgt_vocab_size if isinstance(self.config							,					UpperCAmelCase    ) 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					:		List[str]		   =     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					:		Dict		   =     label_smoothed_nll_loss






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			int    ):
													if self.optimizer is None:
																				__lowerCamelCase					:		Union[str, Any]		   =     ["bias", "LayerNorm.weight"]
																				__lowerCamelCase					:		Dict		   =     [
																				    {
																				        "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					:		List[str]		   =     Adafactor if self.args.adafactor else AdamW
																				if self.args.adafactor:
																											__lowerCamelCase					:		Tuple		   =     Adafactor
																											__lowerCamelCase					:		Optional[int]		   =     {"scale_parameter": False, "relative_step": False}
																				else:
																											__lowerCamelCase					:		int		   =     AdamW
																											__lowerCamelCase					:		List[str]		   =     {
																											    "betas": (self.args.adam_betaa, self.args.adam_betaa),
																											    "eps": self.args.adam_epsilon,
																											}
																				__lowerCamelCase					:		Optional[Any]		   =     self.args.learning_rate
																				if self.sharded_ddp:
																											__lowerCamelCase					:		Any		   =     OSS(
																											    params=UpperCAmelCase							,					optim=UpperCAmelCase							,					**UpperCAmelCase							,					)
																				else:
																											__lowerCamelCase					:		int		   =     optimizer_cls(UpperCAmelCase							,					**UpperCAmelCase    )

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






						def       lowerCamelCase__     (       self      :			Any							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase					:		Optional[Any]		   =     arg_to_scheduler[self.args.lr_scheduler]
													if self.args.lr_scheduler == "constant":
																				__lowerCamelCase					:		Any		   =     schedule_func(self.optimizer    )
													elif self.args.lr_scheduler == "constant_w_warmup":
																				__lowerCamelCase					:		Union[str, Any]		   =     schedule_func(self.optimizer							,					num_warmup_steps=self.args.warmup_steps    )
													else:
																				__lowerCamelCase					:		List[Any]		   =     schedule_func(
																				    self.optimizer							,					num_warmup_steps=self.args.warmup_steps							,					num_training_steps=UpperCAmelCase    )
													return scheduler






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													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       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													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					:		Dict		   =     model(**UpperCAmelCase							,					use_cache=UpperCAmelCase    )[0]
																											__lowerCamelCase					:		Optional[int]		   =     self.loss_fn(logits.view(-1							,					logits.shape[-1]    )							,					labels.view(-1    )    )
																				else:
																											# compute usual loss via models
																											__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     model(**UpperCAmelCase							,					labels=UpperCAmelCase							,					use_cache=UpperCAmelCase    )[:2]
													else:
																				# compute label smoothed loss
																				__lowerCamelCase					:		str		   =     model(**UpperCAmelCase							,					use_cache=UpperCAmelCase    )[0]
																				__lowerCamelCase					:		int		   =     torch.nn.functional.log_softmax(UpperCAmelCase							,					dim=-1    )
																				__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     self.loss_fn(UpperCAmelCase							,					UpperCAmelCase							,					self.args.label_smoothing							,					ignore_index=self.config.pad_token_id    )
													return loss, logits






						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		Dict		   =     inputs.pop("labels"    )
													__lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     self._compute_loss(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )
													return loss






						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			nn.Module							,					UpperCAmelCase      :			Dict[str, Union[torch.Tensor, Any]]							,					UpperCAmelCase      :			bool							,					UpperCAmelCase      :			Optional[List[str]] = None							,					):
													__lowerCamelCase					:		Any		   =     self._prepare_inputs(UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     {
													    "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					:		Any		   =     self.model.generate(
																				    inputs["input_ids"]							,					attention_mask=inputs["attention_mask"]							,					**UpperCAmelCase							,					)
																				# in case the batch is shorter than max length, the output should be padded
																				if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																											__lowerCamelCase					:		Tuple		   =     self._pad_tensors_to_max_len(UpperCAmelCase							,					gen_kwargs["max_length"]    )

													__lowerCamelCase					:		Union[str, Any]		   =     inputs.pop("labels"    )
													with torch.no_grad():
																				# compute loss on predict data
																				__lowerCamelCase   ,  __lowerCamelCase					:		Optional[int]		   =     self._compute_loss(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )

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

													__lowerCamelCase					:		Dict		   =     generated_tokens if self.args.predict_with_generate else logits

													if labels.shape[-1] < gen_kwargs["max_length"]:
																				__lowerCamelCase					:		Tuple		   =     self._pad_tensors_to_max_len(UpperCAmelCase							,					gen_kwargs["max_length"]    )

													return (loss, logits, labels)







						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[str]    ):
													# If PAD token is not defined at least EOS token has to be defined
													__lowerCamelCase					:		Optional[int]		   =     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					:		Union[str, Any]		   =     pad_token_id * torch.ones(
													    (tensor.shape[0], max_length)							,					dtype=tensor.dtype							,					device=tensor.device    )
													__lowerCamelCase					:		Tuple		   =     tensor
													return padded_tensor
"""simple docstring"""





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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  _snake_case     (					a__		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			TransformeraDModel							,					UpperCAmelCase      :			AutoencoderKL							,					UpperCAmelCase      :			KarrasDiffusionSchedulers							,					UpperCAmelCase      :			Optional[Dict[int, str]] = None							,					):
													super().__init__()
													self.register_modules(transformer=UpperCAmelCase							,					vae=UpperCAmelCase							,					scheduler=UpperCAmelCase    )

													# create a imagenet -> id dictionary for easier use
													__lowerCamelCase					:		Optional[Any]		   =     {}
													if idalabel is not None:
																				for key, value in idalabel.items():
																											for label in value.split(","    ):
																																		__lowerCamelCase					:		List[str]		   =     int(UpperCAmelCase    )
																				__lowerCamelCase					:		Union[str, Any]		   =     dict(sorted(self.labels.items()    )    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, List[str]]    ):

													if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		List[str]		   =     list(UpperCAmelCase    )

													for l in label:
																				if l not in self.labels:
																											raise ValueError(
																											    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""    )

													return [self.labels[l] for l in label]







						@torch.no_grad()
						def __call__(       self      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     self.transformer.config.sample_size
													__lowerCamelCase					:		Dict		   =     self.transformer.config.in_channels

													__lowerCamelCase					:		Any		   =     randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)							,					generator=UpperCAmelCase							,					device=self.device							,					dtype=self.transformer.dtype							,					)
													__lowerCamelCase					:		Optional[Any]		   =     torch.cat([latents] * 2    ) if guidance_scale > 1 else latents

													__lowerCamelCase					:		Optional[int]		   =     torch.tensor(UpperCAmelCase							,					device=self.device    ).reshape(-1    )
													__lowerCamelCase					:		Optional[int]		   =     torch.tensor([1000] * batch_size							,					device=self.device    )
													__lowerCamelCase					:		Any		   =     torch.cat([class_labels, class_null]							,					0    ) if guidance_scale > 1 else class_labels

													# set step values
													self.scheduler.set_timesteps(UpperCAmelCase    )

													for t in self.progress_bar(self.scheduler.timesteps    ):
																				if guidance_scale > 1:
																											__lowerCamelCase					:		str		   =     latent_model_input[: len(UpperCAmelCase    ) // 2]
																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([half, half]							,					dim=0    )
																				__lowerCamelCase					:		Dict		   =     self.scheduler.scale_model_input(UpperCAmelCase							,					UpperCAmelCase    )

																				__lowerCamelCase					:		Dict		   =     t
																				if not torch.is_tensor(UpperCAmelCase    ):
																											# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																											# This would be a good case for the `match` statement (Python 3.10+)
																											__lowerCamelCase					:		List[str]		   =     latent_model_input.device.type == "mps"
																											if isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																																		__lowerCamelCase					:		List[Any]		   =     torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowerCamelCase					:		Optional[Any]		   =     torch.intaa if is_mps else torch.intaa
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([timesteps]							,					dtype=UpperCAmelCase							,					device=latent_model_input.device    )
																				elif len(timesteps.shape    ) == 0:
																											__lowerCamelCase					:		Union[str, Any]		   =     timesteps[None].to(latent_model_input.device    )
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowerCamelCase					:		List[Any]		   =     timesteps.expand(latent_model_input.shape[0]    )
																				# predict noise model_output
																				__lowerCamelCase					:		Union[str, Any]		   =     self.transformer(
																				    UpperCAmelCase							,					timestep=UpperCAmelCase							,					class_labels=UpperCAmelCase    ).sample

																				# perform guidance
																				if guidance_scale > 1:
																											__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																											__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     torch.split(UpperCAmelCase							,					len(UpperCAmelCase    ) // 2							,					dim=0    )

																											__lowerCamelCase					:		List[str]		   =     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([half_eps, half_eps]							,					dim=0    )

																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([eps, rest]							,					dim=1    )

																				# learned sigma
																				if self.transformer.config.out_channels // 2 == latent_channels:
																											__lowerCamelCase   ,  __lowerCamelCase					:		int		   =     torch.split(UpperCAmelCase							,					UpperCAmelCase							,					dim=1    )
																				else:
																											__lowerCamelCase					:		int		   =     noise_pred

																				# compute previous image: x_t -> x_t-1
																				__lowerCamelCase					:		Optional[Any]		   =     self.scheduler.step(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    ).prev_sample

													if guidance_scale > 1:
																				__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     latent_model_input.chunk(2							,					dim=0    )
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     latent_model_input

													__lowerCamelCase					:		Tuple		   =     1 / self.vae.config.scaling_factor * latents
													__lowerCamelCase					:		Any		   =     self.vae.decode(UpperCAmelCase    ).sample

													__lowerCamelCase					:		Tuple		   =     (samples / 2 + 0.5).clamp(0							,					1    )

													# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
													__lowerCamelCase					:		Optional[int]		   =     samples.cpu().permute(0							,					2							,					3							,					1    ).float().numpy()

													if output_type == "pil":
																				__lowerCamelCase					:		Dict		   =     self.numpy_to_pil(UpperCAmelCase    )

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=UpperCAmelCase    )