
"""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"""







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							for line in lines:
														__snake_case     			=       re.sub(r"#.*"							,    ""							,    SCREAMING_SNAKE_CASE )  # remove comments
														if line:
																					filtered_lines.append(SCREAMING_SNAKE_CASE )
							__snake_case     			=       "\n".join(SCREAMING_SNAKE_CASE )

							# Make a hash from all this code
							__snake_case     			=       full_str.encode("utf-8" )
							return shaaaa(SCREAMING_SNAKE_CASE ).hexdigest()


# get importable module names and hash for caching
_SCREAMING_SNAKE_CASE	       =      {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_SCREAMING_SNAKE_CASE	       =      {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_SCREAMING_SNAKE_CASE	       =      {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_SCREAMING_SNAKE_CASE	       =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
