
"""simple docstring"""


import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


A_    							=					datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
						import pyspark




@dataclass
class  __lowerCamelCase  ( datasets.BuilderConfig			):
				a__:      Optional[datasets.Features]          =    None






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,):
			import pyspark

			def generate_fn():
						lowerCamelCase_					=  df.select('''*'''	,pyspark.sql.functions.spark_partition_id().alias('''part_id'''						)						)
						for partition_id in partition_order:
									lowerCamelCase_					=  df_with_partition_id.select('''*'''						).where(f"part_id = {partition_id}"						).drop('''part_id'''						)
									lowerCamelCase_					=  partition_df.collect()
									lowerCamelCase_					=  0
									for row in rows:
												yield f"{partition_id}_{row_id}", row.asDict()
												row_id += 1

			return generate_fn




class  __lowerCamelCase  ( _BaseExamplesIterable			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=None							,						):
							lowerCamelCase_					=  df
							lowerCamelCase_					=  partition_order or range(self.df.rdd.getNumPartitions()   )
							lowerCamelCase_					=  _generate_iterable_examples(self.df							,						self.partition_order   )




				def __iter__(				self   ):
							yield from self.generate_examples_fn()




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  list(range(self.df.rdd.getNumPartitions()   )   )
							generator.shuffle(UpperCAmelCase   )
							return SparkExamplesIterable(self.df							,						partition_order=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  self.split_shard_indices_by_worker(UpperCAmelCase							,						UpperCAmelCase   )
							return SparkExamplesIterable(self.df							,						partition_order=UpperCAmelCase   )





				@property
				def        UpperCAmelCase__						(				self   ):
							return len(self.partition_order   )




class  __lowerCamelCase  ( datasets.DatasetBuilder			):
				a__:      Optional[Any]          =    SparkConfig




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							import pyspark

							lowerCamelCase_					=  pyspark.sql.SparkSession.builder.getOrCreate()
							lowerCamelCase_					=  df
							lowerCamelCase_					=  working_dir

							super().__init__(
							    cache_dir=UpperCAmelCase							,						config_name=str(self.df.semanticHash()   )							,						**UpperCAmelCase							,						)




				def        UpperCAmelCase__						(				self   ):
							# Returns the path of the created file.
							def create_cache_and_write_probe(UpperCAmelCase   ):
										# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
										# already exist.
										os.makedirs(self._cache_dir							,						exist_ok=UpperCAmelCase   )
										lowerCamelCase_					=  os.path.join(self._cache_dir							,						'''fs_test''' + uuid.uuida().hex   )
										# Opening the file in append mode will create a new file unless it already exists, in which case it will not
										# change the file contents.
										open(UpperCAmelCase							,						'''a'''   )
										return [probe_file]

							if self._spark.conf.get('''spark.master'''							,						''''''   ).startswith('''local'''   ):
										return

							# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
							# accessible to the driver.
							# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
							if self._cache_dir:
										lowerCamelCase_					=  (
										    self._spark.sparkContext.parallelize(range(1   )							,						1   ).mapPartitions(UpperCAmelCase   ).collect()
										)
										if os.path.isfile(probe[0]   ):
													return

							raise ValueError(
							    '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'''   )




				def        UpperCAmelCase__						(				self   ):
							return datasets.DatasetInfo(features=self.config.features   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							return [datasets.SplitGenerator(name=datasets.Split.TRAIN   )]




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							import pyspark

							def get_arrow_batch_size(UpperCAmelCase   ):
										for batch in it:
													yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}   )

							lowerCamelCase_					=  self.df.count()
							lowerCamelCase_					=  df_num_rows if df_num_rows <= 100 else 100
							# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
							lowerCamelCase_					=  (
							    self.df.limit(UpperCAmelCase   )
							    .repartition(1   )
							    .mapInArrow(UpperCAmelCase							,						'''batch_bytes: long'''   )
							    .agg(pyspark.sql.functions.sum('''batch_bytes'''   ).alias('''sample_bytes'''   )   )
							    .collect()[0]
							    .sample_bytes
							    / sample_num_rows
							)
							lowerCamelCase_					=  approx_bytes_per_row * df_num_rows
							if approx_total_size > max_shard_size:
										# Make sure there is at least one row per partition.
										lowerCamelCase_					=  min(UpperCAmelCase							,						int(approx_total_size / max_shard_size   )   )
										lowerCamelCase_					=  self.df.repartition(UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						):
							import pyspark

							lowerCamelCase_					=  ParquetWriter if file_format == '''parquet''' else ArrowWriter
							lowerCamelCase_					=  os.path.join(self._working_dir							,						os.path.basename(UpperCAmelCase   )   ) if self._working_dir else fpath
							lowerCamelCase_					=  file_format == '''parquet'''

							# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
							# pickling the SparkContext.
							lowerCamelCase_					=  self.config.features
							lowerCamelCase_					=  self._writer_batch_size
							lowerCamelCase_					=  self._fs.storage_options

							def write_arrow(UpperCAmelCase   ):
										# Within the same SparkContext, no two task attempts will share the same attempt ID.
										lowerCamelCase_					=  pyspark.TaskContext().taskAttemptId()
										lowerCamelCase_					=  next(UpperCAmelCase							,						UpperCAmelCase   )
										if first_batch is None:
													# Some partitions might not receive any data.
													return pa.RecordBatch.from_arrays(
													    [[task_id], [0], [0]]							,						names=['''task_id''', '''num_examples''', '''num_bytes''']							,						)
										lowerCamelCase_					=  0
										lowerCamelCase_					=  writer_class(
										    features=UpperCAmelCase							,						path=working_fpath.replace('''SSSSS'''							,						f"{shard_id:05d}"   ).replace('''TTTTT'''							,						f"{task_id:05d}"   )							,						writer_batch_size=UpperCAmelCase							,						storage_options=UpperCAmelCase							,						embed_local_files=UpperCAmelCase							,						)
										lowerCamelCase_					=  pa.Table.from_batches([first_batch]   )
										writer.write_table(UpperCAmelCase   )
										for batch in it:
													if max_shard_size is not None and writer._num_bytes >= max_shard_size:
																lowerCamelCase_		, lowerCamelCase_					=  writer.finalize()
																writer.close()
																yield pa.RecordBatch.from_arrays(
																    [[task_id], [num_examples], [num_bytes]]							,						names=['''task_id''', '''num_examples''', '''num_bytes''']							,						)
																shard_id += 1
																lowerCamelCase_					=  writer_class(
																    features=writer._features							,						path=working_fpath.replace('''SSSSS'''							,						f"{shard_id:05d}"   ).replace('''TTTTT'''							,						f"{task_id:05d}"   )							,						writer_batch_size=UpperCAmelCase							,						storage_options=UpperCAmelCase							,						embed_local_files=UpperCAmelCase							,						)
													lowerCamelCase_					=  pa.Table.from_batches([batch]   )
													writer.write_table(UpperCAmelCase   )

										if writer._num_bytes > 0:
													lowerCamelCase_		, lowerCamelCase_					=  writer.finalize()
													writer.close()
													yield pa.RecordBatch.from_arrays(
													    [[task_id], [num_examples], [num_bytes]]							,						names=['''task_id''', '''num_examples''', '''num_bytes''']							,						)

										if working_fpath != fpath:
													for file in os.listdir(os.path.dirname(UpperCAmelCase   )   ):
																lowerCamelCase_					=  os.path.join(os.path.dirname(UpperCAmelCase   )							,						os.path.basename(UpperCAmelCase   )   )
																shutil.move(UpperCAmelCase							,						UpperCAmelCase   )

							lowerCamelCase_					=  (
							    self.df.mapInArrow(UpperCAmelCase							,						'''task_id: long, num_examples: long, num_bytes: long'''   )
							    .groupBy('''task_id'''   )
							    .agg(
							        pyspark.sql.functions.sum('''num_examples'''   ).alias('''total_num_examples'''   )							,						pyspark.sql.functions.sum('''num_bytes'''   ).alias('''total_num_bytes'''   )							,						pyspark.sql.functions.count('''num_bytes'''   ).alias('''num_shards'''   )							,						pyspark.sql.functions.collect_list('''num_examples'''   ).alias('''shard_lengths'''   )							,						)
							    .collect()
							)
							for row in stats:
										yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase = "arrow"							,						UpperCAmelCase = None							,						UpperCAmelCase = None							,						**UpperCAmelCase							,						):
							self._validate_cache_dir()

							lowerCamelCase_					=  convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE   )
							self._repartition_df_if_needed(UpperCAmelCase   )
							lowerCamelCase_					=  not is_remote_filesystem(self._fs   )
							lowerCamelCase_					=  os.path.join if is_local else posixpath.join

							lowerCamelCase_					=  '''-TTTTT-SSSSS-of-NNNNN'''
							lowerCamelCase_					=  f"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
							lowerCamelCase_					=  path_join(self._output_dir							,						UpperCAmelCase   )

							lowerCamelCase_					=  0
							lowerCamelCase_					=  0
							lowerCamelCase_					=  0
							lowerCamelCase_					=  []
							lowerCamelCase_					=  []

							for task_id, content in self._prepare_split_single(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
										(
										    (
										    lowerCamelCase_
										)		, (
										    lowerCamelCase_
										)		, (
										    lowerCamelCase_
										)		, (
										    lowerCamelCase_
										)		, 
										)					=  content
										if num_bytes > 0:
													total_num_examples += num_examples
													total_num_bytes += num_bytes
													total_shards += num_shards
													task_id_and_num_shards.append((task_id, num_shards)   )
													all_shard_lengths.extend(UpperCAmelCase   )

							lowerCamelCase_					=  total_num_examples
							lowerCamelCase_					=  total_num_bytes

							# should rename everything at the end
							logger.debug(f"Renaming {total_shards} shards."   )
							if total_shards > 1:
										lowerCamelCase_					=  all_shard_lengths

										# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
										# pickling error due to pickling the SparkContext.
										lowerCamelCase_					=  self._fs

										# use the -SSSSS-of-NNNNN pattern
										def _rename_shard(
										    UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						):
													rename(
													    UpperCAmelCase							,						fpath.replace('''SSSSS'''							,						f"{shard_id:05d}"   ).replace('''TTTTT'''							,						f"{task_id:05d}"   )							,						fpath.replace('''TTTTT-SSSSS'''							,						f"{global_shard_id:05d}"   ).replace('''NNNNN'''							,						f"{total_shards:05d}"   )							,						)

										lowerCamelCase_					=  []
										lowerCamelCase_					=  0
										for i in range(len(UpperCAmelCase   )   ):
													lowerCamelCase_		, lowerCamelCase_					=  task_id_and_num_shards[i]
													for shard_id in range(UpperCAmelCase   ):
																args.append([task_id, shard_id, global_shard_id]   )
																global_shard_id += 1
										self._spark.sparkContext.parallelize(UpperCAmelCase							,						len(UpperCAmelCase   )   ).map(lambda UpperCAmelCase   : _rename_shard(*UpperCAmelCase   )   ).collect()
							else:
										# don't use any pattern
										lowerCamelCase_					=  0
										lowerCamelCase_					=  task_id_and_num_shards[0][0]
										self._rename(
										    fpath.replace('''SSSSS'''							,						f"{shard_id:05d}"   ).replace('''TTTTT'''							,						f"{task_id:05d}"   )							,						fpath.replace(UpperCAmelCase							,						''''''   )							,						)





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						):
							return SparkExamplesIterable(self.df   )







"""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


__snake_case       =       logging.getLogger(__name__)

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

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







class _lowerCAmelCase							( pl.LightningModule      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__=None		,      UpperCamelCase__="base"		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      UpperCamelCase__=None		,      **UpperCamelCase__		,      )      ->						Optional[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(UpperCamelCase__		)
       snake_case       : Tuple    =       0
       snake_case       : List[str]    =       Path(self.hparams.output_dir		)
       snake_case       : Any    =       self.hparams.cache_dir if self.hparams.cache_dir else None
       if config is None:
           snake_case       : int    =       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=UpperCamelCase__		,      **UpperCamelCase__		,      )
       else:
           snake_case       : PretrainedConfig    =       config

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

       if tokenizer is None:
           snake_case       : Dict    =       AutoTokenizer.from_pretrained(
               self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,      cache_dir=UpperCamelCase__		,      )
       else:
           snake_case       : PreTrainedTokenizer    =       tokenizer
       snake_case       : List[str]    =       MODEL_MODES[mode]
       if model is None:
           snake_case       : Union[str, 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=UpperCamelCase__		,      )
       else:
           snake_case       : List[Any]    =       model







   def 					lowerCamelCase						(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : List[Any]    =       self.model_type.from_pretrained(*UpperCamelCase__		,      **UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : Dict    =       self.model
       snake_case       : Tuple    =       ["bias", "LayerNorm.weight"]
       snake_case       : Any    =       [
           {
               "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       : Optional[int]    =       Adafactor(
               UpperCamelCase__		,      lr=self.hparams.learning_rate		,      scale_parameter=UpperCamelCase__		,      relative_step=UpperCamelCase__		)

       else:
           snake_case       : Optional[Any]    =       AdamW(
               UpperCamelCase__		,      lr=self.hparams.learning_rate		,      eps=self.hparams.adam_epsilon		)
       snake_case       : Any    =       optimizer

       snake_case       : List[Any]    =       self.get_lr_scheduler()

       return [optimizer], [scheduler]







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.validation_step(UpperCamelCase__		,      UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       return self.validation_end(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : List[str]    =       max(1		,      self.hparams.gpus		)  # TODO: consider num_tpu_cores
       snake_case       : List[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		,      UpperCamelCase__		)      ->						Union[str, Any]:

       '''simple docstring'''




       if stage == "test":
           snake_case       : Dict    =       len(self.test_dataloader().dataset		)
       else:
           snake_case       : str    =       self.get_dataloader("train"		,      self.hparams.train_batch_size		,      shuffle=UpperCamelCase__		)
           snake_case       : Dict    =       len(self.train_dataloader().dataset		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__ = False		)      ->						int:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       return self.train_loader







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




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







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						List[str]:

       '''simple docstring'''




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







   @pl.utilities.rank_zero_only
   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       snake_case       : str    =       self.output_dir.joinpath("best_tfmr"		)
       snake_case       : int    =       self.step_count
       self.model.save_pretrained(UpperCamelCase__		)
       self.tokenizer.save_pretrained(UpperCamelCase__		)







   @staticmethod
   def 					lowerCamelCase						(      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




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







class _lowerCAmelCase							( pl.Callback      ):







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[int]:

       '''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 _lowerCAmelCase							( pl.Callback      ):







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Any:

       '''simple docstring'''




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







class _lowerCAmelCase							( pl.Callback      ):







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						List[Any]:

       '''simple docstring'''




       snake_case       : List[str]    =       trainer.lr_schedulers[0]["scheduler"]
       snake_case       : str    =       {F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
       pl_module.logger.log_metrics(UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       rank_zero_info("***** Validation results *****"		)
       snake_case       : List[str]    =       trainer.callback_metrics
       # Log results
       for key in sorted(UpperCamelCase__		):
           if key not in ["log", "progress_bar"]:
               rank_zero_info("{} = {}\n".format(UpperCamelCase__		,      str(metrics[key]		)		)		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       rank_zero_info("***** Test results *****"		)
       snake_case       : Dict    =       trainer.callback_metrics
       # Log and save results to file
       snake_case       : Union[str, Any]    =       os.path.join(pl_module.hparams.output_dir		,      "test_results.txt"		)
       with open(UpperCamelCase__		,      "w"		) as writer:
           for key in sorted(UpperCamelCase__		):
               if key not in ["log", "progress_bar"]:
                   rank_zero_info("{} = {}\n".format(UpperCamelCase__		,      str(metrics[key]		)		)		)
                   writer.write("{} = {}\n".format(UpperCamelCase__		,      str(metrics[key]		)		)		)







def     __lowerCAmelCase		(		lowercase    :    Any		,				lowercase    :    str					)      ->		None:


    """simple docstring"""





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







def     __lowerCAmelCase		(		lowercase    :    BaseTransformer		,				lowercase    :    argparse.Namespace		,				lowercase    :    Any=None		,				lowercase    :    List[str]=True		,				lowercase    :    List[Any]=[]		,				lowercase    :    Any=None		,				lowercase    :    Optional[int]=None		,				**lowercase    :    List[Any]		,				)      ->		Tuple:


    """simple docstring"""





    pl.seed_everything(args.seed					)

    # init model
    snake_case       : Optional[int]    =       Path(model.hparams.output_dir					)
    odir.mkdir(exist_ok=lowercase					)

    # add custom checkpoints
    if checkpoint_callback is None:
        snake_case       : int    =       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(lowercase					)
    if logging_callback is None:
        snake_case       : Tuple    =       LoggingCallback()

    snake_case       : str    =       {}

    if args.fpaa:
        snake_case       : Union[str, Any]    =       16

    if args.gpus > 1:
        snake_case       : List[str]    =       "auto"
        snake_case       : int    =       "ddp"

    snake_case       : Dict    =       args.accumulate_grad_batches
    snake_case       : Tuple    =       None
    snake_case       : Any    =       "auto"

    snake_case       : int    =       pl.Trainer.from_argparse_args(
        lowercase		,				weights_summary=lowercase		,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,				logger=lowercase		,				val_check_interval=1		,				num_sanity_val_steps=2		,				**lowercase		,				)

    if args.do_train:
        trainer.fit(lowercase					)

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





