


'''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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      []
      for part_id in partition_order:
            UpperCamelCase       						=      df.where(F"SPARK_PARTITION_ID() = {part_id}"					).collect()
            for row_idx, row in enumerate(__UpperCamelCase					):
                  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 					lowercase__				(   )->				str:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(100					).repartition(1					)
      UpperCamelCase       						=      Spark(__UpperCamelCase					)
      # 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 					lowercase__				(   )->				Any:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(10					).repartition(2					)
      UpperCamelCase       						=      [1, 0]
      UpperCamelCase       						=      _generate_iterable_examples(__UpperCamelCase	,		__UpperCamelCase					)  # Reverse the partitions.
      UpperCamelCase       						=      _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase	,		__UpperCamelCase					)

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







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

            UpperCamelCase       						=      SparkExamplesIterable(__UpperCamelCase					).shuffle_data_sources(__UpperCamelCase					)
            assert shuffled_it.n_shards == 3
            for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
                  UpperCamelCase						,UpperCamelCase       						=      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 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(20					).repartition(4					)

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

      # Partitions 1 and 3
      UpperCamelCase       						=      SparkExamplesIterable(__UpperCamelCase					).shard_data_sources(worker_id=1	,		num_workers=2					)
      assert shard_it_a.n_shards == 2
      UpperCamelCase       						=      _get_expected_row_ids_and_row_dicts_for_partition_order(__UpperCamelCase	,		[1, 3]					)
      for i, (row_id, row_dict) in enumerate(__UpperCamelCase					):
            UpperCamelCase						,UpperCamelCase       						=      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 					lowercase__				(   )->				Tuple:
      UpperCamelCase       						=      pyspark.sql.SparkSession.builder.master("""local[*]"""					).appName("""pyspark"""					).getOrCreate()
      UpperCamelCase       						=      spark.range(100					).repartition(1					)
      UpperCamelCase       						=      Spark(__UpperCamelCase					)
      # 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








'''simple docstring'''

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
SCREAMING_SNAKE_CASE__											=     tokenizer(['Making tiny model'], return_tensors='pt')
SCREAMING_SNAKE_CASE__											=     tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
SCREAMING_SNAKE_CASE__											=     'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de
