from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class 		a__						(					snake_case__					):
					A	       =							42







class 		a__						(					snake_case__    ,		snake_case__					):

					@register_to_config
					def __init__(    self      :       Optional[Any],_A      :       int = 3,_A      :       int = 3,_A      :       Tuple[str] = ("DownEncoderBlock2D",),_A      :       Tuple[str] = ("UpDecoderBlock2D",),_A      :       Tuple[int] = (64,),_A      :       int = 1,_A      :       str = "silu",_A      :       int = 3,_A      :       int = 32,_A      :       int = 256,_A      :       int = 32,_A      :       Optional[int] = None,_A      :       float = 0.18215,_A      :       str = "group",):







							"""simple docstring"""






							super().__init__()

							# pass init params to Encoder
							SCREAMING_SNAKE_CASE_  :		Any				      =    Encoder(
							    in_channels=_A,out_channels=_A,down_block_types=_A,block_out_channels=_A,layers_per_block=_A,act_fn=_A,norm_num_groups=_A,double_z=_A,)

							SCREAMING_SNAKE_CASE_  :		Dict				      =    vq_embed_dim if vq_embed_dim is not None else latent_channels

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    nn.Convad(_A,_A,1    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    VectorQuantizer(_A,_A,beta=0.25,remap=_A,sane_index_shape=_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    nn.Convad(_A,_A,1    )

							# pass init params to Decoder
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    Decoder(
							    in_channels=_A,out_channels=_A,up_block_types=_A,block_out_channels=_A,layers_per_block=_A,act_fn=_A,norm_num_groups=_A,norm_type=_A,)

					@apply_forward_hook
					def 				__UpperCamelCase   (    self      :       List[str],_A      :       torch.FloatTensor,_A      :       bool = True    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.encoder(_A    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.quant_conv(_A    )

							if not return_dict:
									return (h,)

							return VQEncoderOutput(latents=_A    )

					@apply_forward_hook
					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       torch.FloatTensor,_A      :       bool = False,_A      :       bool = True    ):







							"""simple docstring"""






							if not force_not_quantize:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    self.quantize(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    h
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.post_quant_conv(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.decoder(_A,quant if self.config.norm_type == "spatial" else None    )

							if not return_dict:
									return (dec,)

							return DecoderOutput(sample=_A    )






					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       torch.FloatTensor,_A      :       bool = True    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    sample
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.encode(_A    ).latents
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.decode(_A    ).sample

							if not return_dict:
									return (dec,)

							return DecoderOutput(sample=_A    )

'''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

