'''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


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     Union[str, 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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

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

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

             _UpperCAmelCase     :  List[Any]							      =    data_args
             _UpperCAmelCase     :  str							      =    self.config.tgt_vocab_size if isinstance(self.config						,					A       ) 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     :  Optional[Any]							      =    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     :  Dict							      =    label_smoothed_nll_loss




      def      _A	(       self						:				Tuple						,					A						:				int       ):
             if self.optimizer is None:
                    _UpperCAmelCase     :  Tuple							      =    ["bias", "LayerNorm.weight"]
                    _UpperCAmelCase     :  str							      =    [
                        {
                            "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     :  int							      =    Adafactor if self.args.adafactor else AdamW
                    if self.args.adafactor:
                           _UpperCAmelCase     :  List[str]							      =    Adafactor
                           _UpperCAmelCase     :  List[Any]							      =    {"scale_parameter": False, "relative_step": False}
                    else:
                           _UpperCAmelCase     :  List[str]							      =    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     :  List[Any]							      =    OSS(
                               params=A						,					optim=A						,					**A						,					)
                    else:
                           _UpperCAmelCase     :  Union[str, Any]							      =    optimizer_cls(A						,					**A       )

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




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  List[str]							      =    arg_to_scheduler[self.args.lr_scheduler]
             if self.args.lr_scheduler == "constant":
                    _UpperCAmelCase     :  Optional[Any]							      =    schedule_func(self.optimizer       )
             elif self.args.lr_scheduler == "constant_w_warmup":
                    _UpperCAmelCase     :  str							      =    schedule_func(self.optimizer						,					num_warmup_steps=self.args.warmup_steps       )
             else:
                    _UpperCAmelCase     :  str							      =    schedule_func(
                        self.optimizer						,					num_warmup_steps=self.args.warmup_steps						,					num_training_steps=A       )
             return scheduler




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




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    inputs.pop("labels"       )
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    self._compute_loss(A						,					A						,					A       )
             return loss




      def      _A	(       self						:				List[str]						,					A						:				nn.Module						,					A						:				Dict[str, Union[torch.Tensor, Any]]						,					A						:				bool						,					A						:				Optional[List[str]] = None						,					):
             _UpperCAmelCase     :  List[str]							      =    self._prepare_inputs(A       )

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

             _UpperCAmelCase     :  Any							      =    inputs.pop("labels"       )
             with torch.no_grad():
                    # compute loss on predict data
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    self._compute_loss(A						,					A						,					A       )

             _UpperCAmelCase     :  List[str]							      =    loss.mean().detach()
             if self.args.prediction_loss_only:
                    return (loss, None, None)

             _UpperCAmelCase     :  str							      =    generated_tokens if self.args.predict_with_generate else logits

             if labels.shape[-1] < gen_kwargs["max_length"]:
                    _UpperCAmelCase     :  Optional[Any]							      =    self._pad_tensors_to_max_len(A						,					gen_kwargs["max_length"]       )

             return (loss, logits, labels)





      def      _A	(       self						:				Dict						,					A						:				int						,					A						:				List[str]       ):
             # If PAD token is not defined at least EOS token has to be defined
             _UpperCAmelCase     :  Union[str, 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     :  Tuple							      =    pad_token_id * torch.ones(
                 (tensor.shape[0], max_length)						,					dtype=tensor.dtype						,					device=tensor.device       )
             _UpperCAmelCase     :  Tuple							      =    tensor
             return padded_tensor






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      (DDIMParallelScheduler,)
    __lowerCamelCase			: int					=      (("eta", 0.0), ("num_inference_steps", 50))






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       int          =   {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase__		)
        return config






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       Dict          =   self.scheduler_classes[0]
        A :       str          =   self.get_scheduler_config(**lowerCamelCase__		)
        A :       Tuple          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       Tuple          =   10, 0.0

        A :       Optional[int]          =   self.dummy_model()
        A :       List[str]          =   self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase__		)

        for t in scheduler.timesteps:
            A :       Optional[int]          =   model(lowerCamelCase__,		lowerCamelCase__		)
            A :       str          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        return sample






    def 			_lowerCAmelCase					(       self		):
        for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=lowerCamelCase__		)

        A :       Tuple          =   self.scheduler_classes[0]
        A :       Optional[Any]          =   self.get_scheduler_config(steps_offset=1		)
        A :       List[Any]          =   scheduler_class(**lowerCamelCase__		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps,		torch.LongTensor([801, 601, 401, 201, 1]		)		)






    def 			_lowerCAmelCase					(       self		):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],		[0.002, 0.02, 0.2, 2]		):
            self.check_over_configs(beta_start=lowerCamelCase__,		beta_end=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        self.check_over_configs(thresholding=lowerCamelCase__		)
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=lowerCamelCase__,		prediction_type=lowerCamelCase__,		sample_max_value=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        for t in [1, 10, 49]:
            self.check_over_forward(time_step=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for t, num_inference_steps in zip([1, 10, 50],		[10, 50, 500]		):
            self.check_over_forward(time_step=lowerCamelCase__,		num_inference_steps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for t, eta in zip([1, 10, 49],		[0.0, 0.5, 1.0]		):
            self.check_over_forward(time_step=lowerCamelCase__,		eta=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.scheduler_classes[0]
        A :       List[str]          =   self.get_scheduler_config()
        A :       Dict          =   scheduler_class(**lowerCamelCase__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,		400		) - 0.1_4771		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,		960		) - 0.3_2460		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,		486		) - 0.0_0979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,		998		) - 0.02		)		) < 1e-5






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.scheduler_classes[0]
        A :       Any          =   self.get_scheduler_config()
        A :       Union[str, Any]          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       List[str]          =   10, 0.0
        scheduler.set_timesteps(lowerCamelCase__		)

        A :       Any          =   self.dummy_model()
        A :       Dict          =   self.dummy_sample_deter
        A :       Dict          =   self.dummy_sample_deter + 0.1
        A :       Tuple          =   self.dummy_sample_deter - 0.1

        A :       Tuple          =   samplea.shape[0]
        A :       List[Any]          =   torch.stack([samplea, samplea, samplea],		dim=0		)
        A :       str          =   torch.arange(lowerCamelCase__		)[0:3, None].repeat(1,		lowerCamelCase__		)

        A :       Any          =   model(samples.flatten(0,		1		),		timesteps.flatten(0,		1		)		)
        A :       Any          =   scheduler.batch_step_no_noise(lowerCamelCase__,		timesteps.flatten(0,		1		),		samples.flatten(0,		1		),		lowerCamelCase__		)

        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 1147.7904		) < 1e-2
        assert abs(result_mean.item() - 0.4982		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.full_loop()

        A :       str          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 172.0067		) < 1e-2
        assert abs(result_mean.item() - 0.22_3967		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.full_loop(prediction_type="""v_prediction"""		)

        A :       Any          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 52.5302		) < 1e-2
        assert abs(result_mean.item() - 0.0684		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       Any          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       Dict          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       str          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 149.8295		) < 1e-2
        assert abs(result_mean.item() - 0.1951		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       int          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       List[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 149.0784		) < 1e-2
        assert abs(result_mean.item() - 0.1941		) < 1e-3


