
"""simple docstring"""







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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Dict:







							"""simple docstring"""


							__snake_case     			=       []
							for part_id in partition_order:
														__snake_case     			=       df.where(F'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
														for row_idx, row in enumerate(SCREAMING_SNAKE_CASE ):
																					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   __UpperCamelCase   (					)  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
							__snake_case     			=       spark.range(1_00 ).repartition(1 )
							__snake_case     			=       Spark(SCREAMING_SNAKE_CASE )
							# 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   __UpperCamelCase   (					)  ->     Any:







							"""simple docstring"""


							__snake_case     			=       pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
							__snake_case     			=       spark.range(10 ).repartition(2 )
							__snake_case     			=       [1, 0]
							__snake_case     			=       _generate_iterable_examples(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  # Reverse the partitions.
							__snake_case     			=       _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							for i, (row_id, row_dict) in enumerate(generate_fn() ):
														__snake_case			,   __snake_case     			=       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   __UpperCamelCase   (					)  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
							__snake_case     			=       spark.range(10 ).repartition(1 )
							__snake_case     			=       SparkExamplesIterable(SCREAMING_SNAKE_CASE )
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE ):
														assert row_id == F'''0_{i}'''
														assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def   __UpperCamelCase   (					)  ->     Tuple:







							"""simple docstring"""


							__snake_case     			=       pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
							__snake_case     			=       spark.range(30 ).repartition(3 )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch("numpy.random.Generator" ) as generator_mock:
														__snake_case     			=       lambda SCREAMING_SNAKE_CASE : x.reverse()
														__snake_case     			=       _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE							,    [2, 1, 0] )

														__snake_case     			=       SparkExamplesIterable(SCREAMING_SNAKE_CASE ).shuffle_data_sources(SCREAMING_SNAKE_CASE )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE ):
																					__snake_case			,   __snake_case     			=       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   __UpperCamelCase   (					)  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
							__snake_case     			=       spark.range(20 ).repartition(4 )

							# Partitions 0 and 2
							__snake_case     			=       SparkExamplesIterable(SCREAMING_SNAKE_CASE ).shard_data_sources(worker_id=0							,    num_workers=2 )
							assert shard_it_a.n_shards == 2
							__snake_case     			=       _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE							,    [0, 2] )
							for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE ):
														__snake_case			,   __snake_case     			=       expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							__snake_case     			=       SparkExamplesIterable(SCREAMING_SNAKE_CASE ).shard_data_sources(worker_id=1							,    num_workers=2 )
							assert shard_it_a.n_shards == 2
							__snake_case     			=       _get_expected_row_ids_and_row_dicts_for_partition_order(SCREAMING_SNAKE_CASE							,    [1, 3] )
							for i, (row_id, row_dict) in enumerate(SCREAMING_SNAKE_CASE ):
														__snake_case			,   __snake_case     			=       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   __UpperCamelCase   (					)  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
							__snake_case     			=       spark.range(1_00 ).repartition(1 )
							__snake_case     			=       Spark(SCREAMING_SNAKE_CASE )
							# 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() == 1_00


"""simple docstring"""







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE )
							# set absolute/relative position embeddings parameter
							__snake_case     			=       reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WTQ":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       True
														# hparam_utils.py hparams
														__snake_case     			=       0.664_694
														__snake_case     			=       0.207_951
														__snake_case     			=       0.121_194
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       False
														__snake_case     			=       0.0_352_513

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       False
														# hparam_utils.py hparams
														__snake_case     			=       36.4_519
														__snake_case     			=       0.903_421
														__snake_case     			=       222.088
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       0.763_141

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "TABFACT":
														__snake_case     			=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE )
							elif task == "MLM":
														__snake_case     			=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE )
							elif task == "INTERMEDIATE_PRETRAINING":
														__snake_case     			=       TapasModel(config=SCREAMING_SNAKE_CASE )
							else:
														raise ValueError(F'''Task {task} not supported.''' )

							print(F'''Building PyTorch model from configuration: {config}''' )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							# Save pytorch-model (weights and configuration)
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							model.save_pretrained(SCREAMING_SNAKE_CASE )

							# Save tokenizer files
							print(F'''Save tokenizer files to {pytorch_dump_path}''' )
							__snake_case     			=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"							,    model_max_length=5_12 )
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE )

							print("Used relative position embeddings:"							,    model.config.reset_position_index_per_cell )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
		)
		parser.add_argument(
		    """--reset_position_index_per_cell""",
		    default=False,
		    action="""store_true""",
		    help="""Whether to use relative position embeddings or not. Defaults to True.""",
		)
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--tapas_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained TAPAS model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		_SCREAMING_SNAKE_CASE	       =      parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
