"""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__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  {
    '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_    (			__lowercase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase=None		,    lowerCAmelCase=None		,    *lowerCAmelCase		,    **lowerCAmelCase    ):






										"""simple docstring"""

										super().__init__(*lowerCAmelCase		,    **lowerCAmelCase    )

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

										snake_case								=			data_args
										snake_case								=			self.config.tgt_vocab_size if isinstance(self.config		,    lowerCAmelCase    ) 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:
																snake_case								=			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id    )
										else:
																# dynamically import label_smoothed_nll_loss
																from utils import label_smoothed_nll_loss

																snake_case								=			label_smoothed_nll_loss

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if self.optimizer is None:
																snake_case								=			["bias", "LayerNorm.weight"]
																snake_case								=			[
																    {
																        "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,
																    },
																]
																snake_case								=			Adafactor if self.args.adafactor else AdamW
																if self.args.adafactor:
																						snake_case								=			Adafactor
																						snake_case								=			{"scale_parameter": False, "relative_step": False}
																else:
																						snake_case								=			AdamW
																						snake_case								=			{
																						    "betas": (self.args.adam_betaa, self.args.adam_betaa),
																						    "eps": self.args.adam_epsilon,
																						}
																snake_case								=			self.args.learning_rate
																if self.sharded_ddp:
																						snake_case								=			OSS(
																						    params=lowerCAmelCase		,    optim=lowerCAmelCase		,    **lowerCAmelCase		,    )
																else:
																						snake_case								=			optimizer_cls(lowerCAmelCase		,    **lowerCAmelCase    )

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			arg_to_scheduler[self.args.lr_scheduler]
										if self.args.lr_scheduler == "constant":
																snake_case								=			schedule_func(self.optimizer    )
										elif self.args.lr_scheduler == "constant_w_warmup":
																snake_case								=			schedule_func(self.optimizer		,    num_warmup_steps=self.args.warmup_steps    )
										else:
																snake_case								=			schedule_func(
																    self.optimizer		,    num_warmup_steps=self.args.warmup_steps		,    num_training_steps=lowerCAmelCase    )
										return scheduler

				def 		snake_case (				self    ):






										"""simple docstring"""

										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		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										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
																						snake_case								=			model(**lowerCAmelCase		,    use_cache=lowerCAmelCase    )[0]
																						snake_case								=			self.loss_fn(logits.view(-1		,    logits.shape[-1]    )		,    labels.view(-1    )    )
																else:
																						# compute usual loss via models
																						snake_case								=			model(**lowerCAmelCase		,    labels=lowerCAmelCase		,    use_cache=lowerCAmelCase    )[:2]
										else:
																# compute label smoothed loss
																snake_case								=			model(**lowerCAmelCase		,    use_cache=lowerCAmelCase    )[0]
																snake_case								=			torch.nn.functional.log_softmax(lowerCAmelCase		,    dim=-1    )
																snake_case								=			self.loss_fn(lowerCAmelCase		,    lowerCAmelCase		,    self.args.label_smoothing		,    ignore_index=self.config.pad_token_id    )
										return loss, logits

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			inputs.pop('labels'    )
										snake_case								=			self._compute_loss(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )
										return loss

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    ):






										"""simple docstring"""

										snake_case								=			self._prepare_inputs(lowerCAmelCase    )

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

										snake_case								=			inputs.pop('labels'    )
										with torch.no_grad():
																# compute loss on predict data
																snake_case								=			self._compute_loss(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

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

										snake_case								=			generated_tokens if self.args.predict_with_generate else logits

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

										return (loss, logits, labels)

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			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}"""    )

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







"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
					from .camera import create_pan_cameras
					from .pipeline_shap_e import ShapEPipeline
					from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
					from .renderer import (
					    BoundingBoxVolume,
					    ImportanceRaySampler,
					    MLPNeRFModelOutput,
					    MLPNeRSTFModel,
					    ShapEParamsProjModel,
					    ShapERenderer,
					    StratifiedRaySampler,
					    VoidNeRFModel,
					)


