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

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






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

		A						   =   SparkExamplesIterable(snake_case__     ).shuffle_data_sources(snake_case__     )
		assert shuffled_it.n_shards == 3
		for i, (row_id, row_dict) in enumerate(snake_case__     ):
			A   ,      A						   =   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       _snake_case				(		):
	A						   =   pyspark.sql.SparkSession.builder.master('local[*]'     ).appName('pyspark'     ).getOrCreate()
	A						   =   spark.range(20     ).repartition(4     )

	# Partitions 0 and 2
	A						   =   SparkExamplesIterable(snake_case__     ).shard_data_sources(worker_id=0	,      num_workers=2     )
	assert shard_it_a.n_shards == 2
	A						   =   _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__	,      [0, 2]     )
	for i, (row_id, row_dict) in enumerate(snake_case__     ):
		A   ,      A						   =   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						   =   SparkExamplesIterable(snake_case__     ).shard_data_sources(worker_id=1	,      num_workers=2     )
	assert shard_it_a.n_shards == 2
	A						   =   _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__	,      [1, 3]     )
	for i, (row_id, row_dict) in enumerate(snake_case__     ):
		A   ,      A						   =   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       _snake_case				(		):
	A						   =   pyspark.sql.SparkSession.builder.master('local[*]'     ).appName('pyspark'     ).getOrCreate()
	A						   =   spark.range(100     ).repartition(1     )
	A						   =   Spark(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() == 100

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[str]      =    '''yolos'''


						def __init__( self     :      Dict  ,A_     :      Optional[Any]=768  ,A_     :      int=12  ,A_     :      List[str]=12  ,A_     :      str=3072  ,A_     :      Tuple="gelu"  ,A_     :      Dict=0.0  ,A_     :      List[Any]=0.0  ,A_     :      Any=0.02  ,A_     :      str=1e-12  ,A_     :      List[Any]=[512, 864]  ,A_     :      Union[str, Any]=16  ,A_     :      List[str]=3  ,A_     :      Optional[int]=True  ,A_     :      Tuple=100  ,A_     :      str=True  ,A_     :      Optional[Any]=False  ,A_     :      Any=1  ,A_     :      Optional[Any]=5  ,A_     :      Optional[Any]=2  ,A_     :      Optional[int]=5  ,A_     :      List[Any]=2  ,A_     :      Union[str, Any]=0.1  ,**A_     :      Tuple  ,)     ->   Any:
							super().__init__(**A_      )

							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   hidden_act
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   initializer_range
							A						   =   layer_norm_eps
							A						   =   image_size
							A						   =   patch_size
							A						   =   num_channels
							A						   =   qkv_bias
							A						   =   num_detection_tokens
							A						   =   use_mid_position_embeddings
							A						   =   auxiliary_loss
							# Hungarian matcher
							A						   =   class_cost
							A						   =   bbox_cost
							A						   =   giou_cost
							# Loss coefficients
							A						   =   bbox_loss_coefficient
							A						   =   giou_loss_coefficient
							A						   =   eos_coefficient







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    version.parse('''1.11'''    )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   float:
							return 1e-4




						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   int:
							return 12