


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


lowerCAmelCase__      		= logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCAmelCase__      		= {
    '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
lowerCAmelCase__      		= {
    '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
}
lowerCAmelCase__      		= sorted(arg_to_scheduler.keys())
lowerCAmelCase__      		= '{' + ', '.join(arg_to_scheduler_choices) + '}'







class      lowerCAmelCase__       (				pl.LightningModule):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase="base" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				int:
            super().__init__()
            # TODO: move to self.save_hyperparameters()
            # self.save_hyperparameters()
            # can also expand arguments into trainer signature for easier reading

            self.save_hyperparameters(__lowerCamelCase)
            _A      :       Tuple				    = 0
            _A      :       Union[str, Any]				    = Path(self.hparams.output_dir)
            _A      :       str				    = self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                   _A      :       List[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=__lowerCamelCase ,						**__lowerCamelCase ,						)
            else:
                   _A      :       PretrainedConfig				    = config

            _A      :       Optional[int]				    = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
            for p in extra_model_params:
                   if getattr(self.hparams ,						__lowerCamelCase ,						__lowerCamelCase):
                          assert hasattr(self.config ,						__lowerCamelCase), F"model config doesn't have a `{p}` attribute"
                          setattr(self.config ,						__lowerCamelCase ,						getattr(self.hparams ,						__lowerCamelCase))

            if tokenizer is None:
                   _A      :       Dict				    = AutoTokenizer.from_pretrained(
                       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,						cache_dir=__lowerCamelCase ,						)
            else:
                   _A      :       PreTrainedTokenizer				    = tokenizer
            _A      :       List[Any]				    = MODEL_MODES[mode]
            if model is None:
                   _A      :       Optional[Any]				    = 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=__lowerCamelCase ,						)
            else:
                   _A      :       Any				    = model


     def  _lowerCamelCase (						self ,						*__lowerCamelCase ,						**__lowerCamelCase)			->				Union[str, Any]:
            _A      :       List[str]				    = self.model_type.from_pretrained(*__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       Tuple				    = arg_to_scheduler[self.hparams.lr_scheduler]
            _A      :       int				    = get_schedule_func(
                self.opt ,						num_warmup_steps=self.hparams.warmup_steps ,						num_training_steps=self.total_steps())
            _A      :       Union[str, Any]				    = {"scheduler": scheduler, "interval": "step", "frequency": 1}
            return scheduler


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Any				    = self.model
            _A      :       Optional[int]				    = ["bias", "LayerNorm.weight"]
            _A      :       int				    = [
                {
                    "params": [
                        p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
                    ],  # check this named paramters
                    "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:
                   _A      :       Optional[int]				    = Adafactor(
                       __lowerCamelCase ,						lr=self.hparams.learning_rate ,						scale_parameter=__lowerCamelCase ,						relative_step=__lowerCamelCase)

            else:
                   _A      :       List[Any]				    = AdamW(
                       __lowerCamelCase ,						lr=self.hparams.learning_rate ,						eps=self.hparams.adam_epsilon)
            _A      :       Union[str, Any]				    = optimizer

            _A      :       Optional[Any]				    = self.get_lr_scheduler()

            return [optimizer], [scheduler]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Dict:
            return self.validation_step(__lowerCamelCase ,						__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[Any]:
            return self.validation_end(__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				int:
            _A      :       int				    = max(1 ,						self.hparams.gpus)  # TODO: consider num_tpu_cores
            _A      :       Union[str, Any]				    = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            if stage == "test":
                   _A      :       Tuple				    = len(self.test_dataloader().dataset)
            else:
                   _A      :       List[str]				    = self.get_dataloader("train" ,						self.hparams.train_batch_size ,						shuffle=__lowerCamelCase)
                   _A      :       List[str]				    = len(self.train_dataloader().dataset)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase = False)			->				Any:
            raise NotImplementedError("You must implement this for your task")


     def  _lowerCamelCase (						self)			->				Any:
            return self.train_loader


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.get_dataloader("dev" ,						self.hparams.eval_batch_size ,						shuffle=__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.get_dataloader("test" ,						self.hparams.eval_batch_size ,						shuffle=__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				int:
            return os.path.join(
                self.hparams.data_dir ,						"cached_{}_{}_{}".format(
                    __lowerCamelCase ,						list(filter(__lowerCamelCase ,						self.hparams.model_name_or_path.split("/"))).pop() ,						str(self.hparams.max_seq_length) ,						) ,						)


     @pl.utilities.rank_zero_only
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.output_dir.joinpath("best_tfmr")
            _A      :       Optional[int]				    = self.step_count
            self.model.save_pretrained(__lowerCamelCase)
            self.tokenizer.save_pretrained(__lowerCamelCase)


     @staticmethod
     def  _lowerCamelCase (						__lowerCamelCase ,						__lowerCamelCase)			->				List[str]:
            parser.add_argument(
                "--model_name_or_path" ,						default=__lowerCamelCase ,						type=__lowerCamelCase ,						required=__lowerCamelCase ,						help="Path to pretrained model or model identifier from huggingface.co/models" ,						)
            parser.add_argument(
                "--config_name" ,						default="" ,						type=__lowerCamelCase ,						help="Pretrained config name or path if not the same as model_name")
            parser.add_argument(
                "--tokenizer_name" ,						default=__lowerCamelCase ,						type=__lowerCamelCase ,						help="Pretrained tokenizer name or path if not the same as model_name" ,						)
            parser.add_argument(
                "--cache_dir" ,						default=str(Path(__lowerCamelCase).parent / "test_run" / "cache") ,						type=__lowerCamelCase ,						help="Where do you want to store the pre-trained models downloaded from huggingface.co" ,						)
            parser.add_argument(
                "--encoder_layerdrop" ,						type=__lowerCamelCase ,						help="Encoder layer dropout probability (Optional). Goes into model.config" ,						)
            parser.add_argument(
                "--decoder_layerdrop" ,						type=__lowerCamelCase ,						help="Decoder layer dropout probability (Optional). Goes into model.config" ,						)
            parser.add_argument(
                "--dropout" ,						type=__lowerCamelCase ,						help="Dropout probability (Optional). Goes into model.config" ,						)
            parser.add_argument(
                "--attention_dropout" ,						type=__lowerCamelCase ,						help="Attention dropout probability (Optional). Goes into model.config" ,						)
            parser.add_argument("--learning_rate" ,						default=5e-5 ,						type=__lowerCamelCase ,						help="The initial learning rate for Adam.")
            parser.add_argument(
                "--lr_scheduler" ,						default="linear" ,						choices=__lowerCamelCase ,						metavar=__lowerCamelCase ,						type=__lowerCamelCase ,						help="Learning rate scheduler" ,						)
            parser.add_argument("--weight_decay" ,						default=0.0 ,						type=__lowerCamelCase ,						help="Weight decay if we apply some.")
            parser.add_argument("--adam_epsilon" ,						default=1e-8 ,						type=__lowerCamelCase ,						help="Epsilon for Adam optimizer.")
            parser.add_argument("--warmup_steps" ,						default=0 ,						type=__lowerCamelCase ,						help="Linear warmup over warmup_steps.")
            parser.add_argument("--num_workers" ,						default=4 ,						type=__lowerCamelCase ,						help="kwarg passed to DataLoader")
            parser.add_argument("--num_train_epochs" ,						dest="max_epochs" ,						default=3 ,						type=__lowerCamelCase)
            parser.add_argument("--train_batch_size" ,						default=3_2 ,						type=__lowerCamelCase)
            parser.add_argument("--eval_batch_size" ,						default=3_2 ,						type=__lowerCamelCase)
            parser.add_argument("--adafactor" ,						action="store_true")







class      lowerCAmelCase__       (				pl.Callback):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[Any]:
            if (
                trainer.is_global_zero and trainer.global_rank == 0
            ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
                   pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class      lowerCAmelCase__       (				pl.Callback):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Union[str, Any]:
            # print(pl_module.model.rag)
            for name, param in pl_module.model.rag.named_parameters():
                   if param.grad is None:
                          print(__lowerCamelCase)







class      lowerCAmelCase__       (				pl.Callback):




     '''simple docstring'''


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Any:
            _A      :       int				    = trainer.lr_schedulers[0]["scheduler"]
            _A      :       Union[str, Any]				    = {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
            pl_module.logger.log_metrics(__lowerCamelCase)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[Any]:
            rank_zero_info("***** Validation results *****")
            _A      :       Union[str, Any]				    = trainer.callback_metrics
            # Log results
            for key in sorted(__lowerCamelCase):
                   if key not in ["log", "progress_bar"]:
                          rank_zero_info("{} = {}\n".format(__lowerCamelCase ,						str(metrics[key])))


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




def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  int						):
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       parser.add_argument(
           "--output_dir"				,							default=str(Path(UpperCamelCase__						).parent / "test_run" / "model_checkpoints"						)				,							type=UpperCamelCase__				,							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=UpperCamelCase__				,							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=UpperCamelCase__						)
       parser.add_argument("--max_grad_norm"				,							dest="gradient_clip_val"				,							default=1.0				,							type=UpperCamelCase__				,							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=UpperCamelCase__				,							default=1				,							help="Number of updates steps to accumulate before performing a backward/update pass."				,							)
       parser.add_argument("--seed"				,							type=UpperCamelCase__				,							default=42				,							help="random seed for initialization"						)
       parser.add_argument(
           "--data_dir"				,							default=str(Path(UpperCamelCase__						).parent / "test_run" / "dummy-train-data"						)				,							type=UpperCamelCase__				,							help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,							)






def 							_UpperCAmelCase				(UpperCamelCase__     :  BaseTransformer				,							UpperCamelCase__     :  argparse.Namespace				,							UpperCamelCase__     :  str=None				,							UpperCamelCase__     :  Dict=True				,							UpperCamelCase__     :  List[Any]=[]				,							UpperCamelCase__     :  Any=None				,							UpperCamelCase__     :  Optional[Any]=None				,							**UpperCamelCase__     :  Union[str, Any]				,							):
       pl.seed_everything(args.seed						)

       # init model
       _A      :       str				    = Path(model.hparams.output_dir						)
       odir.mkdir(exist_ok=UpperCamelCase__						)

       # add custom checkpoints
       if checkpoint_callback is None:
              _A      :       Optional[Any]				    = 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(UpperCamelCase__						)
       if logging_callback is None:
              _A      :       List[Any]				    = LoggingCallback()

       _A      :       int				    = {}

       if args.fpaa:
              _A      :       int				    = 16

       if args.gpus > 1:
              _A      :       str				    = "auto"
              _A      :       Optional[int]				    = "ddp"

       _A      :       List[str]				    = args.accumulate_grad_batches
       _A      :       str				    = None
       _A      :       Union[str, Any]				    = "auto"

       _A      :       List[str]				    = pl.Trainer.from_argparse_args(
           UpperCamelCase__				,							weights_summary=UpperCamelCase__				,							callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]				,							logger=UpperCamelCase__				,							val_check_interval=1				,							num_sanity_val_steps=2				,							**UpperCamelCase__				,							)

       if args.do_train:
              trainer.fit(UpperCamelCase__						)

       else:
              print("RAG modeling tests with new set functions successfuly executed!"						)
       return trainer




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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A    ,			_A      :       Any				    = image.size
       _A    ,			_A      :       str				    = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       _A      :       List[str]				    = image.resize((w, h)				,							resample=PIL_INTERPOLATION["lanczos"]						)
       _A      :       Any				    = np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
       _A      :       Optional[Any]				    = image[None].transpose(0				,							3				,							1				,							2						)
       _A      :       Union[str, Any]				    = torch.from_numpy(UpperCamelCase__						)
       return 2.0 * image - 1.0







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				Optional[int]:
            super().__init__()
            self.register_modules(vqvae=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase)


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 1_0_0 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						)			->				Union[Tuple, ImagePipelineOutput]:
            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Tuple				    = 1
            elif isinstance(__lowerCamelCase ,						torch.Tensor):
                   _A      :       Union[str, Any]				    = image.shape[0]
            else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")

            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Union[str, Any]				    = preprocess(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = image.shape[-2:]

            # in_channels should be 6: 3 for latents, 3 for low resolution image
            _A      :       Optional[Any]				    = (batch_size, self.unet.config.in_channels // 2, height, width)
            _A      :       str				    = next(self.unet.parameters()).dtype

            _A      :       Union[str, Any]				    = randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase)

            _A      :       List[Any]				    = image.to(device=self.device ,						dtype=__lowerCamelCase)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
            _A      :       Any				    = self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            _A      :       List[str]				    = latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _A      :       str				    = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            _A      :       Optional[int]				    = {}
            if accepts_eta:
                   _A      :       List[Any]				    = eta

            for t in self.progress_bar(__lowerCamelCase):
                   # concat latents and low resolution image in the channel dimension.
                   _A      :       List[Any]				    = torch.cat([latents, image] ,						dim=1)
                   _A      :       str				    = self.scheduler.scale_model_input(__lowerCamelCase ,						__lowerCamelCase)
                   # predict the noise residual
                   _A      :       Any				    = self.unet(__lowerCamelCase ,						__lowerCamelCase).sample
                   # compute the previous noisy sample x_t -> x_t-1
                   _A      :       Optional[int]				    = self.scheduler.step(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase).prev_sample

            # decode the image latents with the VQVAE
            _A      :       Union[str, Any]				    = self.vqvae.decode(__lowerCamelCase).sample
            _A      :       Dict				    = torch.clamp(__lowerCamelCase ,						-1.0 ,						1.0)
            _A      :       Tuple				    = image / 2 + 0.5
            _A      :       int				    = image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()

            if output_type == "pil":
                   _A      :       Optional[int]				    = self.numpy_to_pil(__lowerCamelCase)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__lowerCamelCase)
