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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						Any			  =    []
							for part_id in partition_order:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    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      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Any			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    spark.range(100  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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      lowerCamelCase_()->    List[str]:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    spark.range(10  ).repartition(2  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [1, 0]
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    _generate_iterable_examples(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  # Reverse the partitions.
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    _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()  ):
														_SCREAMING_SNAKE_CASE		:						Union[str, 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      lowerCamelCase_()->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    spark.range(10  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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      lowerCamelCase_()->    Tuple:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						int			  =    spark.range(30  ).repartition(3  )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch("""numpy.random.Generator"""  ) as generator_mock:
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    lambda __SCREAMING_SNAKE_CASE  : x.reverse()
														_SCREAMING_SNAKE_CASE		:						int			  =    _get_expected_row_ids_and_row_dicts_for_partition_order(__SCREAMING_SNAKE_CASE     ,	[2, 1, 0]  )

														_SCREAMING_SNAKE_CASE		:						Tuple			  =    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  ):
																					_SCREAMING_SNAKE_CASE		:						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      lowerCamelCase_()->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Any			  =    spark.range(20  ).repartition(4  )

							# Partitions 0 and 2
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  ).shard_data_sources(worker_id=0     ,	num_workers=2  )
							assert shard_it_a.n_shards == 2
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    _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  ):
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    SparkExamplesIterable(__SCREAMING_SNAKE_CASE  ).shard_data_sources(worker_id=1     ,	num_workers=2  )
							assert shard_it_a.n_shards == 2
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    _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  ):
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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      lowerCamelCase_()->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Any			  =    pyspark.sql.SparkSession.builder.master("""local[*]"""  ).appName("""pyspark"""  ).getOrCreate()
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    spark.range(100  ).repartition(1  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    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() == 100



"""simple docstring"""





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     =	logging.get_logger(__name__)

lowerCAmelCase_     =	{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"sew"





				def __init__(      self							:							List[Any]      ,    _A							:							Tuple=3_2      ,    _A							:							str=7_6_8      ,    _A							:							Dict=1_2      ,    _A							:							Tuple=1_2      ,    _A							:							Optional[Any]=3_0_7_2      ,    _A							:							List[str]=2      ,    _A							:							Dict="gelu"      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.0      ,    _A							:							str=0.1      ,    _A							:							Tuple=0.1      ,    _A							:							Optional[int]=0.02      ,    _A							:							Dict=1e-5      ,    _A							:							str="group"      ,    _A							:							Tuple="gelu"      ,    _A							:							Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)      ,    _A							:							Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,    _A							:							Any=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,    _A							:							Tuple=False      ,    _A							:							Tuple=1_2_8      ,    _A							:							int=1_6      ,    _A							:							Union[str, Any]=True      ,    _A							:							Optional[Any]=0.05      ,    _A							:							List[Any]=1_0      ,    _A							:							Union[str, Any]=2      ,    _A							:							Tuple=0.0      ,    _A							:							Union[str, Any]=1_0      ,    _A							:							Optional[int]=0      ,    _A							:							Union[str, Any]="mean"      ,    _A							:							Optional[int]=False      ,    _A							:							List[Any]=False      ,    _A							:							int=2_5_6      ,    _A							:							str=0      ,    _A							:							Optional[int]=1      ,    _A							:							List[Any]=2      ,    **_A							:							Dict      ,    ):



											"""simple docstring"""







											super().__init__(**_A      ,    pad_token_id=_A      ,    bos_token_id=_A      ,    eos_token_id=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feat_extract_norm
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    feat_extract_activation
											_SCREAMING_SNAKE_CASE		:						Dict			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    list(_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    conv_bias
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_conv_pos_embeddings
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    num_conv_pos_embedding_groups
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    len(self.conv_dim)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						str			  =    squeeze_factor
											_SCREAMING_SNAKE_CASE		:						Dict			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						str			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Dict			  =    hidden_dropout
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    attention_dropout
											_SCREAMING_SNAKE_CASE		:						int			  =    activation_dropout
											_SCREAMING_SNAKE_CASE		:						Any			  =    feat_proj_dropout
											_SCREAMING_SNAKE_CASE		:						str			  =    final_dropout
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    layerdrop
											_SCREAMING_SNAKE_CASE		:						Any			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						int			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    vocab_size

											if (
											    (len(self.conv_stride) != self.num_feat_extract_layers)
											    or (len(self.conv_kernel) != self.num_feat_extract_layers)
											    or (len(self.conv_dim) != self.num_feat_extract_layers)
											):
																		raise ValueError(
																		    """Configuration for convolutional layers is incorrect."""
																		    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
																		    f"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
																		    f"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

											# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    apply_spec_augment
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_time_prob
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    mask_time_length
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_time_min_masks
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_feature_prob
											_SCREAMING_SNAKE_CASE		:						int			  =    mask_feature_length
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    mask_feature_min_masks

											# ctc loss
											_SCREAMING_SNAKE_CASE		:						int			  =    ctc_loss_reduction
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    ctc_zero_infinity

											# sequence classification
											_SCREAMING_SNAKE_CASE		:						Dict			  =    use_weighted_layer_sum
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    classifier_proj_size





				@property
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											return functools.reduce(operator.mul      ,    self.conv_stride      ,    1)


