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


UpperCamelCase__											=					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCamelCase__											=					{
    """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
UpperCamelCase__											=					{
    """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
}
UpperCamelCase__											=					sorted(arg_to_scheduler.keys())
UpperCamelCase__											=					"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""





class      A							(				pl.LightningModule     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						argparse.Namespace       ,  __UpperCAmelCase     :						int=None       ,  __UpperCAmelCase     :						Tuple="base"       ,  __UpperCAmelCase     :						Tuple=None       ,  __UpperCAmelCase     :						Union[str, Any]=None       ,  __UpperCAmelCase     :						int=None       ,  **__UpperCAmelCase     :						Union[str, Any]       ,  )						->				Union[str, Any]:
         """simple docstring"""



         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__		)
         UpperCAmelCase__								=  0
         UpperCAmelCase__								=  Path(self.hparams.output_dir		)
         UpperCAmelCase__								=  self.hparams.cache_dir if self.hparams.cache_dir else None
         if config is None:
               UpperCAmelCase__								=  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:
               UpperCAmelCase__								=  config

         UpperCAmelCase__								=  ("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:
               UpperCAmelCase__								=  AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       ,  cache_dir=lowerCAmelCase__       ,  )
         else:
               UpperCAmelCase__								=  tokenizer
         UpperCAmelCase__								=  MODEL_MODES[mode]
         if model is None:
               UpperCAmelCase__								=  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:
               UpperCAmelCase__								=  model


   def 			lowercase_      (self     :						Optional[int]       ,  *__UpperCAmelCase     :						List[Any]       ,  **__UpperCAmelCase     :						Any		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_type.from_pretrained(*lowerCAmelCase__       ,  **lowerCAmelCase__		)


   def 			lowercase_      (self     :						List[str]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  arg_to_scheduler[self.hparams.lr_scheduler]
         UpperCAmelCase__								=  get_schedule_func(
             self.opt       ,  num_warmup_steps=self.hparams.warmup_steps       ,  num_training_steps=self.total_steps()		)
         UpperCAmelCase__								=  {"scheduler": scheduler, "interval": "step", "frequency": 1}
         return scheduler


   def 			lowercase_      (self     :						Optional[int]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.model
         UpperCAmelCase__								=  ["bias", "LayerNorm.weight"]
         UpperCAmelCase__								=  [
             {
                 "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:
               UpperCAmelCase__								=  Adafactor(
                   lowerCAmelCase__       ,  lr=self.hparams.learning_rate       ,  scale_parameter=lowerCAmelCase__       ,  relative_step=lowerCAmelCase__		)

         else:
               UpperCAmelCase__								=  AdamW(
                   lowerCAmelCase__       ,  lr=self.hparams.learning_rate       ,  eps=self.hparams.adam_epsilon		)
         UpperCAmelCase__								=  optimizer

         UpperCAmelCase__								=  self.get_lr_scheduler()

         return [optimizer], [scheduler]


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						str		)						->				List[Any]:
         """simple docstring"""



         return self.validation_step(lowerCAmelCase__       ,  lowerCAmelCase__		)


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						List[str]		)						->				Dict:
         """simple docstring"""



         return self.validation_end(lowerCAmelCase__		)


   def 			lowercase_      (self     :						Optional[int]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  max(1       ,  self.hparams.gpus		)  # TODO: consider num_tpu_cores
         UpperCAmelCase__								=  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
         return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Any:
         """simple docstring"""



         if stage == "test":
               UpperCAmelCase__								=  len(self.test_dataloader().dataset		)
         else:
               UpperCAmelCase__								=  self.get_dataloader("train"       ,  self.hparams.train_batch_size       ,  shuffle=lowerCAmelCase__		)
               UpperCAmelCase__								=  len(self.train_dataloader().dataset		)


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						bool = False		)						->				Any:
         """simple docstring"""



         raise NotImplementedError("You must implement this for your task"		)


   def 			lowercase_      (self     :						str		)						->				int:
         """simple docstring"""



         return self.train_loader


   def 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



         return self.get_dataloader("dev"       ,  self.hparams.eval_batch_size       ,  shuffle=lowerCAmelCase__		)


   def 			lowercase_      (self     :						Optional[Any]		)						->				Optional[int]:
         """simple docstring"""



         return self.get_dataloader("test"       ,  self.hparams.eval_batch_size       ,  shuffle=lowerCAmelCase__		)


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Any		)						->				Optional[int]:
         """simple docstring"""



         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 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Dict[str, Any]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.output_dir.joinpath("best_tfmr"		)
         UpperCAmelCase__								=  self.step_count
         self.model.save_pretrained(lowerCAmelCase__		)
         self.tokenizer.save_pretrained(lowerCAmelCase__		)





   @staticmethod
   def 			lowercase_      (__UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[Any]		)						->				Optional[Any]:
         """simple docstring"""



         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      A							(				pl.Callback     ):


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						str		)						->				Optional[Any]:
         """simple docstring"""



         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      A							(				pl.Callback     ):


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				List[str]:
         """simple docstring"""



         for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                     print(lowerCAmelCase__		)





class      A							(				pl.Callback     ):


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Optional[Any]		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  trainer.lr_schedulers[0]["scheduler"]
         UpperCAmelCase__								=  {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
         pl_module.logger.log_metrics(lowerCAmelCase__		)


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						pl.Trainer       ,  __UpperCAmelCase     :						pl.LightningModule		)						->				Optional[int]:
         """simple docstring"""



         rank_zero_info("***** Validation results *****"		)
         UpperCAmelCase__								=  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 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						pl.Trainer       ,  __UpperCAmelCase     :						pl.LightningModule		)						->				Optional[Any]:
         """simple docstring"""



         rank_zero_info("***** Test results *****"		)
         UpperCAmelCase__								=  trainer.callback_metrics
         # Log and save results to file
         UpperCAmelCase__								=  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    lowerCAmelCase_ (     __A,  __A							)     ->				List[str]:







      '''simple docstring'''


      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    lowerCAmelCase_ (     __A,  __A,  __A=None,  __A=True,  __A=[],  __A=None,  __A=None,  **__A,  )     ->				Tuple:







      '''simple docstring'''


      pl.seed_everything(args.seed							)

      # init model
      UpperCAmelCase__								=  Path(model.hparams.output_dir							)
      odir.mkdir(exist_ok=_UpperCAmelCase							)

      # add custom checkpoints
      if checkpoint_callback is None:
            UpperCAmelCase__								=  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:
            UpperCAmelCase__								=  LoggingCallback()

      UpperCAmelCase__								=  {}

      if args.fpaa:
            UpperCAmelCase__								=  16

      if args.gpus > 1:
            UpperCAmelCase__								=  "auto"
            UpperCAmelCase__								=  "ddp"

      UpperCAmelCase__								=  args.accumulate_grad_batches
      UpperCAmelCase__								=  None
      UpperCAmelCase__								=  "auto"

      UpperCAmelCase__								=  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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel






