

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_ (__A	:		Optional[int]   ,  __A	:		Dict	)     ->      List[Any]:
   __lowerCAmelCase    :     Optional[int]          =		[]
   for part_id in partition_order:
      __lowerCAmelCase    :     str          =		df.where(f'''SPARK_PARTITION_ID() = {part_id}'''	).collect()
      for row_idx, row in enumerate(_a	):
         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_ ()     ->      List[str]:
   __lowerCAmelCase    :     str          =		pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
   __lowerCAmelCase    :     Union[str, Any]          =		spark.range(1_0_0	).repartition(1	)
   __lowerCAmelCase    :     Dict          =		Spark(_a	)
   # 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=1_6	)
   # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
   assert spark_builder.df.rdd.getNumPartitions() == 5_0





@require_not_windows
@require_dill_gt_0_3_2
def       snake_case_ ()     ->      Dict:
   __lowerCAmelCase    :     Tuple          =		pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
   __lowerCAmelCase    :     Any          =		spark.range(1_0	).repartition(2	)
   __lowerCAmelCase    :     Tuple          =		[1, 0]
   __lowerCAmelCase    :     Union[str, Any]          =		_generate_iterable_examples(_a   ,  _a	)  # Reverse the partitions.
   __lowerCAmelCase    :     Optional[Any]          =		_get_expected_row_ids_and_row_dicts_for_partition_order(_a   ,  _a	)

   for i, (row_id, row_dict) in enumerate(generate_fn()	):
      __lowerCAmelCase    :     Optional[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       snake_case_ ()     ->      Optional[Any]:
   __lowerCAmelCase    :     Optional[int]          =		pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
   __lowerCAmelCase    :     List[Any]          =		spark.range(1_0	).repartition(1	)
   __lowerCAmelCase    :     Tuple          =		SparkExamplesIterable(_a	)
   assert it.n_shards == 1
   for i, (row_id, row_dict) in enumerate(_a	):
      assert row_id == f'''0_{i}'''
      assert row_dict == {"id": i}





@require_not_windows
@require_dill_gt_0_3_2
def       snake_case_ ()     ->      Tuple:
   __lowerCAmelCase    :     int          =		pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
   __lowerCAmelCase    :     int          =		spark.range(3_0	).repartition(3	)
   # Mock the generator so that shuffle reverses the partition indices.
   with patch("""numpy.random.Generator"""	) as generator_mock:
      __lowerCAmelCase    :     int          =		lambda __A	: x.reverse()
      __lowerCAmelCase    :     List[Any]          =		_get_expected_row_ids_and_row_dicts_for_partition_order(_a   ,  [2, 1, 0]	)

      __lowerCAmelCase    :     str          =		SparkExamplesIterable(_a	).shuffle_data_sources(_a	)
      assert shuffled_it.n_shards == 3
      for i, (row_id, row_dict) in enumerate(_a	):
         __lowerCAmelCase    :     Optional[int]          =		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_ ()     ->      Tuple:
   __lowerCAmelCase    :     Optional[int]          =		pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
   __lowerCAmelCase    :     Optional[Any]          =		spark.range(2_0	).repartition(4	)

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

   # Partitions 1 and 3
   __lowerCAmelCase    :     Any          =		SparkExamplesIterable(_a	).shard_data_sources(worker_id=1   ,  num_workers=2	)
   assert shard_it_a.n_shards == 2
   __lowerCAmelCase    :     List[Any]          =		_get_expected_row_ids_and_row_dicts_for_partition_order(_a   ,  [1, 3]	)
   for i, (row_id, row_dict) in enumerate(_a	):
      __lowerCAmelCase    :     Union[str, Any]          =		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_ ()     ->      Dict:
   __lowerCAmelCase    :     str          =		pyspark.sql.SparkSession.builder.master("""local[*]"""	).appName("""pyspark"""	).getOrCreate()
   __lowerCAmelCase    :     List[str]          =		spark.range(1_0_0	).repartition(1	)
   __lowerCAmelCase    :     int          =		Spark(_a	)
   # 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_0_0





from collections import OrderedDict
from typing import Mapping

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)