
"""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


_SCREAMING_SNAKE_CASE	       =      logging.getLogger(__name__)

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

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







class 							__magic_name__     (		pl.LightningModule   ):





					def __init__(				self   :							Optional[int]							,						snake_case_   :							argparse.Namespace							,						snake_case_   :							Union[str, Any]=None							,						snake_case_   :							Tuple="base"							,						snake_case_   :							str=None							,						snake_case_   :							Optional[Any]=None							,						snake_case_   :							List[str]=None							,						**snake_case_   :							List[str]							,						):
												super().__init__()
												# TODO: move to self.save_hyperparameters()
												# self.save_hyperparameters()
												# can also expand arguments into trainer signature for easier reading

												self.save_hyperparameters(snake_case_    )
												__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=snake_case_							,						**snake_case_							,						)
												else:
																			__snake_case     			=       config

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

												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=snake_case_							,						)
												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=snake_case_							,						)
												else:
																			__snake_case     			=       model





					def 							lowerCAmelCase      (				self   :							int							,						*snake_case_   :							Any							,						**snake_case_   :							Optional[int]    ):
												__snake_case     			=       self.model_type.from_pretrained(*snake_case_							,						**snake_case_    )





					def 							lowerCAmelCase      (				self   :							int    ):
												__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 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__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(
																			    snake_case_							,						lr=self.hparams.learning_rate							,						scale_parameter=snake_case_							,						relative_step=snake_case_    )

												else:
																			__snake_case     			=       AdamW(
																			    snake_case_							,						lr=self.hparams.learning_rate							,						eps=self.hparams.adam_epsilon    )
												__snake_case     			=       optimizer

												__snake_case     			=       self.get_lr_scheduler()

												return [optimizer], [scheduler]





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]    ):
												return self.validation_step(snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Union[str, Any]    ):
												return self.validation_end(snake_case_    )





					def 							lowerCAmelCase      (				self   :							str    ):
												__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 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Union[str, Any]    ):
												if stage == "test":
																			__snake_case     			=       len(self.test_dataloader().dataset    )
												else:
																			__snake_case     			=       self.get_dataloader("train"							,						self.hparams.train_batch_size							,						shuffle=snake_case_    )
																			__snake_case     			=       len(self.train_dataloader().dataset    )





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							str							,						snake_case_   :							int							,						snake_case_   :							bool = False    ):
												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=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return self.get_dataloader("test"							,						self.hparams.eval_batch_size							,						shuffle=snake_case_    )





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							Dict    ):
												return os.path.join(
												    self.hparams.data_dir							,						"cached_{}_{}_{}".format(
												        snake_case_							,						list(filter(snake_case_							,						self.hparams.model_name_or_path.split("/"    )    )    ).pop()							,						str(self.hparams.max_seq_length    )							,						)							,						)





					@pl.utilities.rank_zero_only
					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							Dict[str, Any]    ):
												__snake_case     			=       self.output_dir.joinpath("best_tfmr"    )
												__snake_case     			=       self.step_count
												self.model.save_pretrained(snake_case_    )
												self.tokenizer.save_pretrained(snake_case_    )





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







class 							__magic_name__     (		pl.Callback   ):





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							List[Any]							,						snake_case_   :							Union[str, 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 							__magic_name__     (		pl.Callback   ):





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Optional[int]							,						snake_case_   :							Any    ):
												# print(pl_module.model.rag)
												for name, param in pl_module.model.rag.named_parameters():
																			if param.grad is None:
																										print(snake_case_    )







class 							__magic_name__     (		pl.Callback   ):





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							Dict							,						snake_case_   :							Union[str, Any]    ):
												__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(snake_case_    )





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							pl.Trainer							,						snake_case_   :							pl.LightningModule    ):
												rank_zero_info("***** Validation results *****"    )
												__snake_case     			=       trainer.callback_metrics
												# Log results
												for key in sorted(snake_case_    ):
																			if key not in ["log", "progress_bar"]:
																										rank_zero_info("{} = {}\n".format(snake_case_							,						str(metrics[key]    )    )    )





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							pl.Trainer							,						snake_case_   :							pl.LightningModule    ):
												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(snake_case_							,						"w"    ) as writer:
																			for key in sorted(snake_case_    ):
																										if key not in ["log", "progress_bar"]:
																																	rank_zero_info("{} = {}\n".format(snake_case_							,						str(metrics[key]    )    )    )
																																	writer.write("{} = {}\n".format(snake_case_							,						str(metrics[key]    )    )    )




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     None:







							"""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   __UpperCamelCase   (					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							,    )  ->     int:







							"""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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """GroupViTModel""",
		    """GroupViTPreTrainedModel""",
		    """GroupViTTextModel""",
		    """GroupViTVisionModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFGroupViTModel""",
		    """TFGroupViTPreTrainedModel""",
		    """TFGroupViTTextModel""",
		    """TFGroupViTVisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_groupvit import (
		    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    GroupViTConfig,
		    GroupViTOnnxConfig,
		    GroupViTTextConfig,
		    GroupViTVisionConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_groupvit import (
				    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GroupViTModel,
				    GroupViTPreTrainedModel,
				    GroupViTTextModel,
				    GroupViTVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_groupvit import (
				    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGroupViTModel,
				    TFGroupViTPreTrainedModel,
				    TFGroupViTTextModel,
				    TFGroupViTVisionModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
