





'''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


__magic_name__   :						int    =			logging.getLogger(__name__)

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

__magic_name__   :						Union[str, Any]    =			{
    """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
__magic_name__   :						List[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
}
__magic_name__   :						Tuple    =			sorted(arg_to_scheduler.keys())
__magic_name__   :						Tuple    =			"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""


class       __SCREAMING_SNAKE_CASE	(						pl.LightningModule			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=None						,   lowerCamelCase="base"						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None						,   **lowerCamelCase						,   ):
       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	)
       _snake_case     		=				0
       _snake_case     		=				Path(self.hparams.output_dir	)
       _snake_case     		=				self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
         _snake_case     		=				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:
         _snake_case     		=				config

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

     def 	UpperCamelCase(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       _snake_case     		=				self.model_type.from_pretrained(*lowerCamelCase						,   **lowerCamelCase	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model
       _snake_case     		=				["bias", "LayerNorm.weight"]
       _snake_case     		=				[
           {
               "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:
         _snake_case     		=				Adafactor(
             lowerCamelCase						,   lr=self.hparams.learning_rate						,   scale_parameter=lowerCamelCase						,   relative_step=lowerCamelCase	)

       else:
         _snake_case     		=				AdamW(
             lowerCamelCase						,   lr=self.hparams.learning_rate						,   eps=self.hparams.adam_epsilon	)
       _snake_case     		=				optimizer

       _snake_case     		=				self.get_lr_scheduler()

       return [optimizer], [scheduler]

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       return self.validation_step(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       return self.validation_end(lowerCamelCase	)

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

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       if stage == "test":
         _snake_case     		=				len(self.test_dataloader().dataset	)
       else:
         _snake_case     		=				self.get_dataloader("train"						,   self.hparams.train_batch_size						,   shuffle=lowerCamelCase	)
         _snake_case     		=				len(self.train_dataloader().dataset	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = False	):
       raise NotImplementedError("You must implement this for your task"	)

     def 	UpperCamelCase(					self	):
       return self.train_loader

     def 	UpperCamelCase(					self	):
       return self.get_dataloader("dev"						,   self.hparams.eval_batch_size						,   shuffle=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       return self.get_dataloader("test"						,   self.hparams.eval_batch_size						,   shuffle=lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       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 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				self.output_dir.joinpath("best_tfmr"	)
       _snake_case     		=				self.step_count
       self.model.save_pretrained(lowerCamelCase	)
       self.tokenizer.save_pretrained(lowerCamelCase	)






     @staticmethod
     def 	UpperCamelCase(					lowerCamelCase						,   lowerCamelCase	):
       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=32						,   type=lowerCamelCase	)
       parser.add_argument("--eval_batch_size"						,   default=32						,   type=lowerCamelCase	)
       parser.add_argument("--adafactor"						,   action="store_true"	)







class       __SCREAMING_SNAKE_CASE	(						pl.Callback			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       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       __SCREAMING_SNAKE_CASE	(						pl.Callback			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       # print(pl_module.model.rag)
       for name, param in pl_module.model.rag.named_parameters():
         if param.grad is None:
           print(lowerCamelCase	)







class       __SCREAMING_SNAKE_CASE	(						pl.Callback			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				trainer.lr_schedulers[0]["scheduler"]
       _snake_case     		=				{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
       pl_module.logger.log_metrics(lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       rank_zero_info("***** Validation results *****"	)
       _snake_case     		=				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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       rank_zero_info("***** Test results *****"	)
       _snake_case     		=				trainer.callback_metrics
       # Log and save results to file
       _snake_case     		=				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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  parser.add_argument(
      "--output_dir"					,	default=str(Path(SCREAMING_SNAKE_CASE__       ).parent / "test_run" / "model_checkpoints"       )					,	type=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__       )
  parser.add_argument("--max_grad_norm"					,	dest="gradient_clip_val"					,	default=1.0					,	type=SCREAMING_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=SCREAMING_SNAKE_CASE__					,	default=1					,	help="Number of updates steps to accumulate before performing a backward/update pass."					,	)
  parser.add_argument("--seed"					,	type=SCREAMING_SNAKE_CASE__					,	default=42					,	help="random seed for initialization"       )
  parser.add_argument(
      "--data_dir"					,	default=str(Path(SCREAMING_SNAKE_CASE__       ).parent / "test_run" / "dummy-train-data"       )					,	type=SCREAMING_SNAKE_CASE__					,	help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."					,	)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=True					,	SCREAMING_SNAKE_CASE__=[]					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None					,	**SCREAMING_SNAKE_CASE__					,	):







  '''simple docstring'''



  pl.seed_everything(args.seed       )

  # init model
  _snake_case     		=				Path(model.hparams.output_dir       )
  odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )

  # add custom checkpoints
  if checkpoint_callback is None:
    _snake_case     		=				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(SCREAMING_SNAKE_CASE__       )
  if logging_callback is None:
    _snake_case     		=				LoggingCallback()

  _snake_case     		=				{}

  if args.fpaa:
    _snake_case     		=				16

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

  _snake_case     		=				args.accumulate_grad_batches
  _snake_case     		=				None
  _snake_case     		=				"auto"

  _snake_case     		=				pl.Trainer.from_argparse_args(
      SCREAMING_SNAKE_CASE__					,	weights_summary=SCREAMING_SNAKE_CASE__					,	callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					,	logger=SCREAMING_SNAKE_CASE__					,	val_check_interval=1					,	num_sanity_val_steps=2					,	**SCREAMING_SNAKE_CASE__					,	)

  if args.do_train:
    trainer.fit(SCREAMING_SNAKE_CASE__       )

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









'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__   :						Dict    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   '''mobilenet_v1'''

     def __init__(					self						,   lowerCamelCase=3						,   lowerCamelCase=224						,   lowerCamelCase=1.0						,   lowerCamelCase=8						,   lowerCamelCase="relu6"						,   lowerCamelCase=True						,   lowerCamelCase=0.999						,   lowerCamelCase=0.02						,   lowerCamelCase=0.001						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)

       if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."	)

       _snake_case     		=				num_channels
       _snake_case     		=				image_size
       _snake_case     		=				depth_multiplier
       _snake_case     		=				min_depth
       _snake_case     		=				hidden_act
       _snake_case     		=				tf_padding
       _snake_case     		=				classifier_dropout_prob
       _snake_case     		=				initializer_range
       _snake_case     		=				layer_norm_eps







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   version.parse('''1.11'''			)

     @property
     def 	UpperCamelCase(					self	):
       return OrderedDict([("pixel_values", {0: "batch"})]	)

     @property
     def 	UpperCamelCase(					self	):
       if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]	)
       else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]	)






     @property
     def 	UpperCamelCase(					self	):
       return 1e-4


