





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				)							-> List[str]:




    """simple docstring"""




    _A     =							[]
    for part_id in partition_order:
        _A     =							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(       )							-> str:




    """simple docstring"""




    _A     =							pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    _A     =							spark.range(100				).repartition(1				)
    _A     =							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(       )							-> int:




    """simple docstring"""




    _A     =							pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    _A     =							spark.range(10				).repartition(2				)
    _A     =							[1, 0]
    _A     =							_generate_iterable_examples(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)  # Reverse the partitions.
    _A     =							_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()				):
        _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 				__lowerCAmelCase(       )							-> Optional[int]:




    """simple docstring"""




    _A     =							pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    _A     =							spark.range(10				).repartition(1				)
    _A     =							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(       )							-> Any:




    """simple docstring"""




    _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 _SCREAMING_SNAKE_CASE				: x.reverse()
        _A     =							_get_expected_row_ids_and_row_dicts_for_partition_order(_SCREAMING_SNAKE_CASE							,     [2, 1, 0]				)

        _A     =							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				):
            _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 				__lowerCAmelCase(       )							-> List[str]:




    """simple docstring"""




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

    # Partitions 0 and 2
    _A     =							SparkExamplesIterable(_SCREAMING_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(_SCREAMING_SNAKE_CASE							,     [0, 2]				)
    for i, (row_id, row_dict) in enumerate(_SCREAMING_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(_SCREAMING_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(_SCREAMING_SNAKE_CASE							,     [1, 3]				)
    for i, (row_id, row_dict) in enumerate(_SCREAMING_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 				__lowerCAmelCase(       )							-> int:




    """simple docstring"""




    _A     =							pyspark.sql.SparkSession.builder.master('local[*]'				).appName('pyspark'				).getOrCreate()
    _A     =							spark.range(100				).repartition(1				)
    _A     =							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 os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		["""image_processor""", """tokenizer"""]
					_UpperCamelCase							=		"""BlipImageProcessor"""
					_UpperCamelCase							=		"""AutoTokenizer"""



					def __init__(	self     :						Union[str, Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						List[str])    ->							Union[str, Any]:
								super().__init__(_lowercase      ,       _lowercase)

								# add QFormer tokenizer
								A_									= qformer_tokenizer



					def __call__(	self     :						Optional[int]      ,       _lowercase     :						ImageInput = None      ,       _lowercase     :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,       _lowercase     :						bool = True      ,       _lowercase     :						Union[bool, str, PaddingStrategy] = False      ,       _lowercase     :						Union[bool, str, TruncationStrategy] = None      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						int = 0      ,       _lowercase     :						Optional[int] = None      ,       _lowercase     :						Optional[bool] = None      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = False      ,       _lowercase     :						bool = True      ,       _lowercase     :						Optional[Union[str, TensorType]] = None      ,       **_lowercase     :						List[Any]      ,       )    ->							BatchFeature:
								if images is None and text is None:
											raise ValueError('You have to specify at least images or text.')

								A_									= BatchFeature()

								if text is not None:
											A_									= self.tokenizer(
											    text=_lowercase      ,       add_special_tokens=_lowercase      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=_lowercase      ,       stride=_lowercase      ,       pad_to_multiple_of=_lowercase      ,       return_attention_mask=_lowercase      ,       return_overflowing_tokens=_lowercase      ,       return_special_tokens_mask=_lowercase      ,       return_offsets_mapping=_lowercase      ,       return_token_type_ids=_lowercase      ,       return_length=_lowercase      ,       verbose=_lowercase      ,       return_tensors=_lowercase      ,       **_lowercase      ,       )
											encoding.update(_lowercase)
											A_									= self.qformer_tokenizer(
											    text=_lowercase      ,       add_special_tokens=_lowercase      ,       padding=_lowercase      ,       truncation=_lowercase      ,       max_length=_lowercase      ,       stride=_lowercase      ,       pad_to_multiple_of=_lowercase      ,       return_attention_mask=_lowercase      ,       return_overflowing_tokens=_lowercase      ,       return_special_tokens_mask=_lowercase      ,       return_offsets_mapping=_lowercase      ,       return_token_type_ids=_lowercase      ,       return_length=_lowercase      ,       verbose=_lowercase      ,       return_tensors=_lowercase      ,       **_lowercase      ,       )
											A_									= qformer_text_encoding.pop('input_ids')
											A_									= qformer_text_encoding.pop('attention_mask')

								if images is not None:
											A_									= self.image_processor(_lowercase      ,       return_tensors=_lowercase)
											encoding.update(_lowercase)

								return encoding



					def     __snake_case						(	self     :						List[Any]      ,       *_lowercase     :						Optional[Any]      ,       **_lowercase     :						List[Any])    ->							Union[str, Any]:
								return self.tokenizer.batch_decode(*_lowercase      ,       **_lowercase)



					def     __snake_case						(	self     :						Optional[int]      ,       *_lowercase     :						List[Any]      ,       **_lowercase     :						List[str])    ->							str:
								return self.tokenizer.decode(*_lowercase      ,       **_lowercase)



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def     __snake_case						(	self     :						Any)    ->							Optional[int]:
								A_									= self.tokenizer.model_input_names
								A_									= self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



					def     __snake_case						(	self     :						Optional[Any]      ,       _lowercase     :						Optional[int]      ,       **_lowercase     :						int)    ->							Tuple:
								if os.path.isfile(_lowercase):
											raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
								os.makedirs(_lowercase      ,       exist_ok=_lowercase)
								A_									= os.path.join(_lowercase      ,       'qformer_tokenizer')
								self.qformer_tokenizer.save_pretrained(_lowercase)
								return super().save_pretrained(_lowercase      ,       **_lowercase)



					@classmethod
					def     __snake_case						(	cls     :						Dict      ,       _lowercase     :						Dict      ,       **_lowercase     :						int)    ->							Any:
								A_									= AutoTokenizer.from_pretrained(_lowercase      ,       subfolder='qformer_tokenizer')
								A_									= cls._get_arguments_from_pretrained(_lowercase      ,       **_lowercase)
								args.append(_lowercase)
								return cls(*_lowercase)




