
"""simple docstring"""




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    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.utils.versions import require_version


_A      :	str       		=	logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

_A      :	Tuple       		=	{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_A      :	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,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_A      :	List[Any]       		=	sorted(arg_to_scheduler.keys())
_A      :	Optional[Any]       		=	"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class 		a__						(				pl.LightningModule						):
   def __init__(      self     ,   _a     ,   _a=None     ,   _a="base"     ,   _a=None     ,   _a=None     ,   _a=None     ,   **_a     ,   ):
          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(_a    )
          lowercase   :    Optional[int]			=							0
          lowercase   :    Union[str, Any]			=							Path(self.hparams.output_dir    )
          lowercase   :    Optional[int]			=							self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
                 lowercase   :    Any			=							AutoConfig.from_pretrained(
                     self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path     ,   **({"num_labels": num_labels} if num_labels is not None else {})     ,   cache_dir=_a     ,   **_a     ,   )
          else:
                 lowercase   :    PretrainedConfig			=							config

          lowercase   :    int			=							("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
                 if getattr(self.hparams     ,   _a     ,   _a    ):
                        assert hasattr(self.config     ,   _a    ), f"""model config doesn't have a `{p}` attribute"""
                        setattr(self.config     ,   _a     ,   getattr(self.hparams     ,   _a    )    )

          if tokenizer is None:
                 lowercase   :    str			=							AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,   cache_dir=_a     ,   )
          else:
                 lowercase   :    PreTrainedTokenizer			=							tokenizer
          lowercase   :    Any			=							MODEL_MODES[mode]
          if model is None:
                 lowercase   :    int			=							self.model_type.from_pretrained(
                     self.hparams.model_name_or_path     ,   from_tf=bool(".ckpt" in self.hparams.model_name_or_path    )     ,   config=self.config     ,   cache_dir=_a     ,   )
          else:
                 lowercase   :    Optional[int]			=							model
   def       __magic_name__      (      self     ,   *_a     ,   **_a    ):
          lowercase   :    Optional[Any]			=							self.model_type.from_pretrained(*_a     ,   **_a    )
   def       __magic_name__      (      self    ):
          lowercase   :    List[str]			=							arg_to_scheduler[self.hparams.lr_scheduler]
          lowercase   :    int			=							get_schedule_func(
              self.opt     ,   num_warmup_steps=self.hparams.warmup_steps     ,   num_training_steps=self.total_steps()    )
          lowercase   :    List[Any]			=							{"scheduler": scheduler, "interval": "step", "frequency": 1}
          return scheduler
   def       __magic_name__      (      self    ):
          lowercase   :    Union[str, Any]			=							self.model
          lowercase   :    Optional[int]			=							["bias", "LayerNorm.weight"]
          lowercase   :    Dict			=							[
              {
                  "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )],
                  "weight_decay": self.hparams.weight_decay,
              },
              {
                  "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
                  "weight_decay": 0.0,
              },
          ]
          if self.hparams.adafactor:
                 lowercase   :    Tuple			=							Adafactor(
                     _a     ,   lr=self.hparams.learning_rate     ,   scale_parameter=_a     ,   relative_step=_a    )

          else:
                 lowercase   :    Optional[Any]			=							AdamW(
                     _a     ,   lr=self.hparams.learning_rate     ,   eps=self.hparams.adam_epsilon    )
          lowercase   :    Union[str, Any]			=							optimizer

          lowercase   :    int			=							self.get_lr_scheduler()

          return [optimizer], [scheduler]
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          return self.validation_step(_a     ,   _a    )
   def       __magic_name__      (      self     ,   _a    ):
          return self.validation_end(_a    )
   def       __magic_name__      (      self    ):
          lowercase   :    List[str]			=							max(1     ,   self.hparams.gpus    )  # TODO: consider num_tpu_cores
          lowercase   :    Dict			=							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
   def       __magic_name__      (      self     ,   _a    ):
          if mode == "test":
                 lowercase   :    int			=							len(self.test_dataloader().dataset    )
          else:
                 lowercase   :    Dict			=							self.get_dataloader("train"     ,   self.hparams.train_batch_size     ,   shuffle=_a    )
                 lowercase   :    Any			=							len(self.train_dataloader().dataset    )
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a = False    ):
          raise NotImplementedError("You must implement this for your task"    )
   def       __magic_name__      (      self    ):
          return self.train_loader
   def       __magic_name__      (      self    ):
          return self.get_dataloader("dev"     ,   self.hparams.eval_batch_size     ,   shuffle=_a    )
   def       __magic_name__      (      self    ):
          return self.get_dataloader("test"     ,   self.hparams.eval_batch_size     ,   shuffle=_a    )
   def       __magic_name__      (      self     ,   _a    ):
          return os.path.join(
              self.hparams.data_dir     ,   "cached_{}_{}_{}".format(
                  _a     ,   list(filter(_a     ,   self.hparams.model_name_or_path.split("/"    )    )    ).pop()     ,   str(self.hparams.max_seq_length    )     ,   )     ,   )
   @pl.utilities.rank_zero_only
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Optional[int]			=							self.output_dir.joinpath("best_tfmr"    )
          lowercase   :    Optional[Any]			=							self.step_count
          self.model.save_pretrained(_a    )
          self.tokenizer.save_pretrained(_a    )

   @staticmethod
   def       __magic_name__      (      _a     ,   _a    ):
          parser.add_argument(
              "--model_name_or_path"     ,   default=_a     ,   type=_a     ,   required=_a     ,   help="Path to pretrained model or model identifier from huggingface.co/models"     ,   )
          parser.add_argument(
              "--config_name"     ,   default=""     ,   type=_a     ,   help="Pretrained config name or path if not the same as model_name"    )
          parser.add_argument(
              "--tokenizer_name"     ,   default=_a     ,   type=_a     ,   help="Pretrained tokenizer name or path if not the same as model_name"     ,   )
          parser.add_argument(
              "--cache_dir"     ,   default=""     ,   type=_a     ,   help="Where do you want to store the pre-trained models downloaded from huggingface.co"     ,   )
          parser.add_argument(
              "--encoder_layerdrop"     ,   type=_a     ,   help="Encoder layer dropout probability (Optional). Goes into model.config"     ,   )
          parser.add_argument(
              "--decoder_layerdrop"     ,   type=_a     ,   help="Decoder layer dropout probability (Optional). Goes into model.config"     ,   )
          parser.add_argument(
              "--dropout"     ,   type=_a     ,   help="Dropout probability (Optional). Goes into model.config"     ,   )
          parser.add_argument(
              "--attention_dropout"     ,   type=_a     ,   help="Attention dropout probability (Optional). Goes into model.config"     ,   )
          parser.add_argument("--learning_rate"     ,   default=5E-5     ,   type=_a     ,   help="The initial learning rate for Adam."    )
          parser.add_argument(
              "--lr_scheduler"     ,   default="linear"     ,   choices=_a     ,   metavar=_a     ,   type=_a     ,   help="Learning rate scheduler"     ,   )
          parser.add_argument("--weight_decay"     ,   default=0.0     ,   type=_a     ,   help="Weight decay if we apply some."    )
          parser.add_argument("--adam_epsilon"     ,   default=1E-8     ,   type=_a     ,   help="Epsilon for Adam optimizer."    )
          parser.add_argument("--warmup_steps"     ,   default=0     ,   type=_a     ,   help="Linear warmup over warmup_steps."    )
          parser.add_argument("--num_workers"     ,   default=4     ,   type=_a     ,   help="kwarg passed to DataLoader"    )
          parser.add_argument("--num_train_epochs"     ,   dest="max_epochs"     ,   default=3     ,   type=_a    )
          parser.add_argument("--train_batch_size"     ,   default=32     ,   type=_a    )
          parser.add_argument("--eval_batch_size"     ,   default=32     ,   type=_a    )
          parser.add_argument("--adafactor"     ,   action="store_true"    )


class 		a__						(				pl.Callback						):
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          lowercase   :    List[Any]			=							trainer.lr_schedulers[0]["scheduler"]
          lowercase   :    Any			=							{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
          pl_module.logger.log_metrics(_a    )
   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          rank_zero_info("***** Validation results *****"    )
          lowercase   :    Union[str, Any]			=							trainer.callback_metrics
          # Log results
          for key in sorted(_a    ):
                 if key not in ["log", "progress_bar"]:
                        rank_zero_info("{} = {}\n".format(_a     ,   str(metrics[key]    )    )    )

   def       __magic_name__      (      self     ,   _a     ,   _a    ):
          rank_zero_info("***** Test results *****"    )
          lowercase   :    Optional[Any]			=							trainer.callback_metrics
          # Log and save results to file
          lowercase   :    Tuple			=							os.path.join(pl_module.hparams.output_dir     ,   "test_results.txt"    )
          with open(_a     ,   "w"    ) as writer:
                 for key in sorted(_a    ):
                        if key not in ["log", "progress_bar"]:
                               rank_zero_info("{} = {}\n".format(_a     ,   str(metrics[key]    )    )    )
                               writer.write("{} = {}\n".format(_a     ,   str(metrics[key]    )    )    )


def   __magic_name__     (				__snake_case    :	Union[str, Any]					,			__snake_case    :	List[Any]  )			->    None:
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           "--output_dir"					,			default=__snake_case					,			type=__snake_case					,			required=__snake_case					,			help="The output directory where the model predictions and checkpoints will be written."					,			)
       parser.add_argument(
           "--fp16"					,			action="store_true"					,			help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"					,			)

       parser.add_argument(
           "--fp16_opt_level"					,			type=__snake_case					,			default="O2"					,			help=(
               "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
               "See details at https://nvidia.github.io/apex/amp.html"
           )					,			)
       parser.add_argument("--n_tpu_cores"					,			dest="tpu_cores"					,			type=__snake_case  )
       parser.add_argument("--max_grad_norm"					,			dest="gradient_clip_val"					,			default=1.0					,			type=__snake_case					,			help="Max gradient norm"  )
       parser.add_argument("--do_train"					,			action="store_true"					,			help="Whether to run training."  )
       parser.add_argument("--do_predict"					,			action="store_true"					,			help="Whether to run predictions on the test set."  )
       parser.add_argument(
           "--gradient_accumulation_steps"					,			dest="accumulate_grad_batches"					,			type=__snake_case					,			default=1					,			help="Number of updates steps to accumulate before performing a backward/update pass."					,			)
       parser.add_argument("--seed"					,			type=__snake_case					,			default=42					,			help="random seed for initialization"  )
       parser.add_argument(
           "--data_dir"					,			default=__snake_case					,			type=__snake_case					,			required=__snake_case					,			help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."					,			)


def   __magic_name__     (				__snake_case    :	BaseTransformer					,			__snake_case    :	argparse.Namespace					,			__snake_case    :	Any=None					,			__snake_case    :	Any=True					,			__snake_case    :	str=[]					,			__snake_case    :	Union[str, Any]=None					,			__snake_case    :	Any=None					,			**__snake_case    :	Any					,			)			->    Any:
       pl.seed_everything(args.seed  )

       # init model
       lowercase   :    int			=							Path(model.hparams.output_dir  )
       odir.mkdir(exist_ok=__snake_case  )

       # add custom checkpoints
       if checkpoint_callback is None:
              lowercase   :    str			=							pl.callbacks.ModelCheckpoint(
                  filepath=args.output_dir					,			prefix="checkpoint"					,			monitor="val_loss"					,			mode="min"					,			save_top_k=1  )
       if early_stopping_callback:
              extra_callbacks.append(__snake_case  )
       if logging_callback is None:
              lowercase   :    Any			=							LoggingCallback()

       lowercase   :    Any			=							{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              lowercase   :    Union[str, Any]			=							16
              lowercase   :    Optional[Any]			=							args.fpaa_opt_level

       if args.gpus > 1:
              lowercase   :    Any			=							"ddp"

       lowercase   :    List[str]			=							args.accumulate_grad_batches
       lowercase   :    Dict			=							extra_train_kwargs.get("accelerator"					,			__snake_case  )
       lowercase   :    str			=							extra_train_kwargs.get("profiler"					,			__snake_case  )

       lowercase   :    List[str]			=							pl.Trainer.from_argparse_args(
           __snake_case					,			weights_summary=__snake_case					,			callbacks=[logging_callback] + extra_callbacks					,			logger=__snake_case					,			checkpoint_callback=__snake_case					,			**__snake_case					,			)

       if args.do_train:
              trainer.fit(__snake_case  )

       return trainer




"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class 		a__						(				unittest.TestCase						):
   @property
   def       __magic_name__      (      self    ):
          torch.manual_seed(0    )
          lowercase   :    int			=							UNetaDModel(
              block_out_channels=(32, 64)     ,   layers_per_block=2     ,   sample_size=32     ,   in_channels=3     ,   out_channels=3     ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")     ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")     ,   )
          return model
   def       __magic_name__      (      self    ):
          lowercase   :    int			=							"cpu"
          lowercase   :    Dict			=							self.dummy_uncond_unet
          lowercase   :    List[str]			=							DDPMScheduler()

          lowercase   :    Tuple			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Optional[int]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    Optional[int]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.Generator(device=_a    ).manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"     ,   return_dict=_a    )[0]

          lowercase   :    List[str]			=							image[0, -3:, -3:, -1]
          lowercase   :    Any			=							image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    List[str]			=							np.array(
              [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.0_0_0, 1.199E-03, 2.648E-04, 5.101E-04]    )

          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2

   def       __magic_name__      (      self    ):
          lowercase   :    Any			=							self.dummy_uncond_unet
          lowercase   :    Dict			=							DDPMScheduler(prediction_type="sample"    )

          lowercase   :    List[Any]			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Any			=							torch.manual_seed(0    )
          lowercase   :    int			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    ).images

          lowercase   :    List[str]			=							torch.manual_seed(0    )
          lowercase   :    Optional[Any]			=							ddpm(generator=_a     ,   num_inference_steps=2     ,   output_type="numpy"    )[0]

          lowercase   :    Any			=							image[0, -3:, -3:, -1]
          lowercase   :    Dict			=							image_eps[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Tuple			=							1E-2 if torch_device != "mps" else 3E-2
          assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance


@slow
@require_torch_gpu
class 		a__						(				unittest.TestCase						):
   def       __magic_name__      (      self    ):
          lowercase   :    Optional[Any]			=							"google/ddpm-cifar10-32"

          lowercase   :    Dict			=							UNetaDModel.from_pretrained(_a    )
          lowercase   :    str			=							DDPMScheduler.from_pretrained(_a    )

          lowercase   :    int			=							DDPMPipeline(unet=_a     ,   scheduler=_a    )
          ddpm.to(_a    )
          ddpm.set_progress_bar_config(disable=_a    )

          lowercase   :    Dict			=							torch.manual_seed(0    )
          lowercase   :    List[str]			=							ddpm(generator=_a     ,   output_type="numpy"    ).images

          lowercase   :    int			=							image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          lowercase   :    Optional[Any]			=							np.array([0.4_2_0_0, 0.3_5_8_8, 0.1_9_3_9, 0.3_8_4_7, 0.3_3_8_2, 0.2_6_4_7, 0.4_1_5_5, 0.3_5_8_2, 0.3_3_8_5]    )
          assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2


