

'''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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "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_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					Optional[Any]					,      lowercase_			:					List[Any]=None					,      lowercase_			:					Optional[Any]=None					,      *lowercase_			:					List[Any]					,      **lowercase_			:					str	)						->     int:
      super().__init__(*lowercase_					,      **lowercase_	)

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

      UpperCAmelCase	:     int						 =			data_args
      UpperCAmelCase	:     Union[str, Any]						 =			self.config.tgt_vocab_size if isinstance(self.config					,      lowercase_	) 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:
       UpperCAmelCase	:     int						 =			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id	)
      else:
       # dynamically import label_smoothed_nll_loss
       from utils import label_smoothed_nll_loss

       UpperCAmelCase	:     Union[str, Any]						 =			label_smoothed_nll_loss


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

      if self.lr_scheduler is None:
       UpperCAmelCase	:     List[str]						 =			self._get_lr_scheduler(lowercase_	)
      else:  # ignoring --lr_scheduler
       logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'	)


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Any	)						->     List[Any]:
      UpperCAmelCase	:     Dict						 =			arg_to_scheduler[self.args.lr_scheduler]
      if self.args.lr_scheduler == "constant":
       UpperCAmelCase	:     str						 =			schedule_func(self.optimizer	)
      elif self.args.lr_scheduler == "constant_w_warmup":
       UpperCAmelCase	:     Optional[Any]						 =			schedule_func(self.optimizer					,      num_warmup_steps=self.args.warmup_steps	)
      else:
       UpperCAmelCase	:     Optional[int]						 =			schedule_func(
           self.optimizer					,      num_warmup_steps=self.args.warmup_steps					,      num_training_steps=lowercase_	)
      return scheduler


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


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					str	)						->     int:
      UpperCAmelCase	:     Optional[Any]						 =			inputs.pop('labels'	)
      UpperCAmelCase ,			UpperCAmelCase	:     List[str]						 =			self._compute_loss(lowercase_					,      lowercase_					,      lowercase_	)
      return loss


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					nn.Module					,      lowercase_			:					Dict[str, Union[torch.Tensor, Any]]					,      lowercase_			:					bool					,      lowercase_			:					Optional[List[str]] = None					,      )						->     Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
      UpperCAmelCase	:     int						 =			self._prepare_inputs(lowercase_	)

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

      UpperCAmelCase	:     Any						 =			inputs.pop('labels'	)
      with torch.no_grad():
       # compute loss on predict data
       UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			self._compute_loss(lowercase_					,      lowercase_					,      lowercase_	)

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

      UpperCAmelCase	:     List[str]						 =			generated_tokens if self.args.predict_with_generate else logits

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

      return (loss, logits, labels)




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					int					,      lowercase_			:					Tuple	)						->     Any:
      # If PAD token is not defined at least EOS token has to be defined
      UpperCAmelCase	:     Optional[Any]						 =			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}"""	)

      UpperCAmelCase	:     List[str]						 =			pad_token_id * torch.ones(
          (tensor.shape[0], max_length)					,      dtype=tensor.dtype					,      device=tensor.device	)
      UpperCAmelCase	:     Any						 =			tensor
      return padded_tensor







'''simple docstring'''

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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	A_			(    _snake_case							):






     '''simple docstring'''



     def __init__(		self			:					List[Any]					,      lowercase_			:					VQModel					,      lowercase_			:					UNetaDModel					,      lowercase_			:					DDIMScheduler	)						->     int:
      super().__init__()
      self.register_modules(vqvae=lowercase_					,      unet=lowercase_					,      scheduler=lowercase_	)




     @torch.no_grad()
     def __call__(		self			:					str					,      lowercase_			:					int = 1					,      lowercase_			:					Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      lowercase_			:					float = 0.0					,      lowercase_			:					int = 50					,      lowercase_			:					Optional[str] = "pil"					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     Union[Tuple, ImagePipelineOutput]:
      UpperCAmelCase	:     str						 =			randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      generator=lowercase_					,      )
      UpperCAmelCase	:     Any						 =			latents.to(self.device	)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	:     Optional[Any]						 =			latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowercase_	)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      UpperCAmelCase	:     Optional[int]						 =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)

      UpperCAmelCase	:     Tuple						 =			{}
      if accepts_eta:
       UpperCAmelCase	:     List[str]						 =			eta

      for t in self.progress_bar(self.scheduler.timesteps	):
       UpperCAmelCase	:     Dict						 =			self.scheduler.scale_model_input(lowercase_					,      lowercase_	)
       # predict the noise residual
       UpperCAmelCase	:     Dict						 =			self.unet(lowercase_					,      lowercase_	).sample
       # compute the previous noisy sample x_t -> x_t-1
       UpperCAmelCase	:     Dict						 =			self.scheduler.step(lowercase_					,      lowercase_					,      lowercase_					,      **lowercase_	).prev_sample

      # decode the image latents with the VAE
      UpperCAmelCase	:     Any						 =			self.vqvae.decode(lowercase_	).sample

      UpperCAmelCase	:     Union[str, Any]						 =			(image / 2 + 0.5).clamp(0					,      1	)
      UpperCAmelCase	:     Optional[Any]						 =			image.cpu().permute(0					,      2					,      3					,      1	).numpy()
      if output_type == "pil":
       UpperCAmelCase	:     Tuple						 =			self.numpy_to_pil(lowercase_	)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowercase_	)




