from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)



def A__  (			lowercase:     Dict,		lowercase:     Union[str, Any]					)				->		Optional[int]:
       A						:     List[str]         =[]
       for part_id in partition_order:
              A						:     Optional[Any]         =df.where(F'SPARK_PARTITION_ID() = {part_id}'					).collect()
              for row_idx, row in enumerate(lowercase					):
                     expected_row_ids_and_row_dicts.append((F'{part_id}_{row_idx}', row.asDict())					)
       return expected_row_ids_and_row_dicts



@require_not_windows
@require_dill_gt_0_3_2
def A__  (			)				->		List[Any]:
       A						:     Optional[int]         =pyspark.sql.SparkSession.builder.master('local[*]'					).appName('pyspark'					).getOrCreate()
       A						:     Any         =spark.range(100					).repartition(1					)
       A						:     List[str]         =Spark(lowercase					)
       # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
       # that each partition can hold 2 rows.
       spark_builder._repartition_df_if_needed(max_shard_size=16					)
       # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
       assert spark_builder.df.rdd.getNumPartitions() == 50



@require_not_windows
@require_dill_gt_0_3_2
def A__  (			)				->		str:
       A						:     List[Any]         =pyspark.sql.SparkSession.builder.master('local[*]'					).appName('pyspark'					).getOrCreate()
       A						:     str         =spark.range(10					).repartition(2					)
       A						:     Union[str, Any]         =[1, 0]
       A						:     Optional[Any]         =_generate_iterable_examples(lowercase,		lowercase					)  # Reverse the partitions.
       A						:     Dict         =_get_expected_row_ids_and_row_dicts_for_partition_order(lowercase,		lowercase					)

       for i, (row_id, row_dict) in enumerate(generate_fn()					):
              A  ,  A						:     Any         =expected_row_ids_and_row_dicts[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def A__  (			)				->		Union[str, Any]:
       A						:     Dict         =pyspark.sql.SparkSession.builder.master('local[*]'					).appName('pyspark'					).getOrCreate()
       A						:     int         =spark.range(10					).repartition(1					)
       A						:     str         =SparkExamplesIterable(lowercase					)
       assert it.n_shards == 1
       for i, (row_id, row_dict) in enumerate(lowercase					):
              assert row_id == F'0_{i}'
              assert row_dict == {"id": i}



@require_not_windows
@require_dill_gt_0_3_2
def A__  (			)				->		int:
       A						:     str         =pyspark.sql.SparkSession.builder.master('local[*]'					).appName('pyspark'					).getOrCreate()
       A						:     Union[str, Any]         =spark.range(30					).repartition(3					)
       # Mock the generator so that shuffle reverses the partition indices.
       with patch('numpy.random.Generator'					) as generator_mock:
              A						:     Dict         =lambda lowercase					: x.reverse()
              A						:     List[str]         =_get_expected_row_ids_and_row_dicts_for_partition_order(lowercase,		[2, 1, 0]					)

              A						:     Tuple         =SparkExamplesIterable(lowercase					).shuffle_data_sources(lowercase					)
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(lowercase					):
                     A  ,  A						:     str         =expected_row_ids_and_row_dicts[i]
                     assert row_id == expected_row_id
                     assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def A__  (			)				->		Optional[int]:
       A						:     Optional[int]         =pyspark.sql.SparkSession.builder.master('local[*]'					).appName('pyspark'					).getOrCreate()
       A						:     Dict         =spark.range(20					).repartition(4					)

       # Partitions 0 and 2
       A						:     Dict         =SparkExamplesIterable(lowercase					).shard_data_sources(worker_id=0,		num_workers=2					)
       assert shard_it_a.n_shards == 2
       A						:     Union[str, Any]         =_get_expected_row_ids_and_row_dicts_for_partition_order(lowercase,		[0, 2]					)
       for i, (row_id, row_dict) in enumerate(lowercase					):
              A  ,  A						:     Dict         =expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict

       # Partitions 1 and 3
       A						:     Any         =SparkExamplesIterable(lowercase					).shard_data_sources(worker_id=1,		num_workers=2					)
       assert shard_it_a.n_shards == 2
       A						:     Any         =_get_expected_row_ids_and_row_dicts_for_partition_order(lowercase,		[1, 3]					)
       for i, (row_id, row_dict) in enumerate(lowercase					):
              A  ,  A						:     str         =expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def A__  (			)				->		Optional[int]:
       A						:     Optional[Any]         =pyspark.sql.SparkSession.builder.master('local[*]'					).appName('pyspark'					).getOrCreate()
       A						:     int         =spark.range(100					).repartition(1					)
       A						:     Optional[Any]         =Spark(lowercase					)
       # Choose a small max_shard_size for maximum partitioning.
       spark_builder._repartition_df_if_needed(max_shard_size=1					)
       # The new number of partitions should not be greater than the number of rows.
       assert spark_builder.df.rdd.getNumPartitions() == 100







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
_lowercase							:      str      							=False
_lowercase							:      Optional[Any]      							=False



def A__  (			lowercase:     Namespace					)				->		Optional[int]:
       return TrainCommand(lowercase					)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @staticmethod
  def 		SCREAMING_SNAKE_CASE_		(				SCREAMING_SNAKE_CASE__    :					ArgumentParser     )    ->				Dict:
         A						:     Optional[Any]         =parser.add_parser('train'      ,	help='CLI tool to train a model on a task.'     )

         train_parser.add_argument(
             '--train_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	required=SCREAMING_SNAKE_CASE__      ,	help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'      ,	)
         train_parser.add_argument(
             '--column_label'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0      ,	help='Column of the dataset csv file with example labels.'     )
         train_parser.add_argument(
             '--column_text'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1      ,	help='Column of the dataset csv file with example texts.'     )
         train_parser.add_argument(
             '--column_id'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=2      ,	help='Column of the dataset csv file with example ids.'     )
         train_parser.add_argument(
             '--skip_first_row'      ,	action='store_true'      ,	help='Skip the first row of the csv file (headers).'     )

         train_parser.add_argument('--validation_data'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=''      ,	help='path to validation dataset.'     )
         train_parser.add_argument(
             '--validation_split'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=0.1      ,	help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'      ,	)

         train_parser.add_argument('--output'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='./'      ,	help='path to saved the trained model.'     )

         train_parser.add_argument(
             '--task'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='text_classification'      ,	help='Task to train the model on.'     )
         train_parser.add_argument(
             '--model'      ,	type=SCREAMING_SNAKE_CASE__      ,	default='bert-base-uncased'      ,	help='Model\'s name or path to stored model.'     )
         train_parser.add_argument('--train_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=32      ,	help='Batch size for training.'     )
         train_parser.add_argument('--valid_batch_size'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=64      ,	help='Batch size for validation.'     )
         train_parser.add_argument('--learning_rate'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=3e-5      ,	help='Learning rate.'     )
         train_parser.add_argument('--adam_epsilon'      ,	type=SCREAMING_SNAKE_CASE__      ,	default=1e-08      ,	help='Epsilon for Adam optimizer.'     )
         train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__     )







  def __init__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Namespace     )    ->				List[Any]:
         A						:     Optional[int]         =logging.get_logger('transformers-cli/training'     )

         A						:     Dict         ='tf' if is_tf_available() else 'torch'

         os.makedirs(args.output      ,	exist_ok=SCREAMING_SNAKE_CASE__     )
         A						:     Optional[Any]         =args.output

         A						:     List[str]         =args.column_label
         A						:     int         =args.column_text
         A						:     Union[str, Any]         =args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'     )
         if args.task == "text_classification":
                A						:     Optional[Any]         =TextClassificationPipeline.from_pretrained(args.model     )
         elif args.task == "token_classification":
                raise NotImplementedError
         elif args.task == "question_answering":
                raise NotImplementedError

         self.logger.info(f'Loading dataset from {args.train_data}'     )
         A						:     Tuple         =Processor.create_from_csv(
             args.train_data      ,	column_label=args.column_label      ,	column_text=args.column_text      ,	column_id=args.column_id      ,	skip_first_row=args.skip_first_row      ,	)
         A						:     Dict         =None
         if args.validation_data:
                self.logger.info(f'Loading validation dataset from {args.validation_data}'     )
                A						:     List[Any]         =Processor.create_from_csv(
                    args.validation_data      ,	column_label=args.column_label      ,	column_text=args.column_text      ,	column_id=args.column_id      ,	skip_first_row=args.skip_first_row      ,	)

         A						:     Optional[Any]         =args.validation_split
         A						:     str         =args.train_batch_size
         A						:     Any         =args.valid_batch_size
         A						:     Dict         =args.learning_rate
         A						:     List[str]         =args.adam_epsilon







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:
         if self.framework == "tf":
                return self.run_tf()
         return self.run_torch()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				List[str]:
         raise NotImplementedError







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				str:
         self.pipeline.fit(
             self.train_dataset      ,	validation_data=self.valid_dataset      ,	validation_split=self.validation_split      ,	learning_rate=self.learning_rate      ,	adam_epsilon=self.adam_epsilon      ,	train_batch_size=self.train_batch_size      ,	valid_batch_size=self.valid_batch_size      ,	)

         # Save trained pipeline
         self.pipeline.save_pretrained(self.output     )






