






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase      :    Union[str, Any]   		=   logging.get_logger(__name__)



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	["pixel_values"]



					def __init__(      self  :						Tuple							,	__A  :						bool = True							,	__A  :						Optional[Dict[str, int]] = None							,	__A  :						PILImageResampling = PILImageResampling.BILINEAR							,	__A  :						bool = True							,	__A  :						Dict[str, int] = None							,	__A  :						bool = True							,	__A  :						Union[int, float] = 1 / 2_5_5							,	__A  :						bool = True							,	__A  :						Optional[Union[float, List[float]]] = None							,	__A  :						Optional[Union[float, List[float]]] = None							,	**__A  :						List[str]							,	):
						super().__init__(**__A )
						snake_case__				:   List[Any]     =						size if size is not None else {"shortest_edge": 2_5_6}
						snake_case__				:   List[str]     =						get_size_dict(__A							,	default_to_square=__A )
						snake_case__				:   str     =						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						snake_case__				:   Optional[int]     =						get_size_dict(__A )
						snake_case__				:   Dict     =						do_resize
						snake_case__				:   Union[str, Any]     =						size
						snake_case__				:   Optional[int]     =						resample
						snake_case__				:   Dict     =						do_center_crop
						snake_case__				:   int     =						crop_size
						snake_case__				:   str     =						do_rescale
						snake_case__				:   Optional[Any]     =						rescale_factor
						snake_case__				:   Dict     =						do_normalize
						snake_case__				:   List[Any]     =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						snake_case__				:   Union[str, Any]     =						image_std if image_std is not None else IMAGENET_STANDARD_STD



					def  _lowercase  (      self  :						Tuple							,	__A  :						np.ndarray							,	__A  :						Dict[str, int]							,	__A  :						PILImageResampling = PILImageResampling.BICUBIC							,	__A  :						Optional[Union[str, ChannelDimension]] = None							,	**__A  :						int							,	):
						snake_case__				:   Tuple     =						get_size_dict(__A							,	default_to_square=__A )
						if "shortest_edge" not in size:
							raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
						snake_case__				:   Dict     =						get_resize_output_image_size(__A							,	size=size["shortest_edge"]							,	default_to_square=__A )
						return resize(__A							,	size=__A							,	resample=__A							,	data_format=__A							,	**__A )



					def  _lowercase  (      self  :						str							,	__A  :						np.ndarray							,	__A  :						Dict[str, int]							,	__A  :						Optional[Union[str, ChannelDimension]] = None							,	**__A  :						str							,	):
						snake_case__				:   Dict     =						get_size_dict(__A )
						return center_crop(__A							,	size=(size["height"], size["width"])							,	data_format=__A							,	**__A )



					def  _lowercase  (      self  :						Dict							,	__A  :						np.ndarray							,	__A  :						float							,	__A  :						Optional[Union[str, ChannelDimension]] = None							,	**__A  :						str ):
						return rescale(__A							,	scale=__A							,	data_format=__A							,	**__A )



					def  _lowercase  (      self  :						Tuple							,	__A  :						np.ndarray							,	__A  :						Union[float, List[float]]							,	__A  :						Union[float, List[float]]							,	__A  :						Optional[Union[str, ChannelDimension]] = None							,	**__A  :						Optional[Any]							,	):
						return normalize(__A							,	mean=__A							,	std=__A							,	data_format=__A							,	**__A )






					def  _lowercase  (      self  :						Tuple							,	__A  :						ImageInput							,	__A  :						Optional[bool] = None							,	__A  :						Dict[str, int] = None							,	__A  :						PILImageResampling = None							,	__A  :						bool = None							,	__A  :						Dict[str, int] = None							,	__A  :						Optional[bool] = None							,	__A  :						Optional[float] = None							,	__A  :						Optional[bool] = None							,	__A  :						Optional[Union[float, List[float]]] = None							,	__A  :						Optional[Union[float, List[float]]] = None							,	__A  :						Optional[Union[str, TensorType]] = None							,	__A  :						Union[str, ChannelDimension] = ChannelDimension.FIRST							,	**__A  :						Optional[int]							,	):
						snake_case__				:   Any     =						do_resize if do_resize is not None else self.do_resize
						snake_case__				:   Union[str, Any]     =						size if size is not None else self.size
						snake_case__				:   Optional[Any]     =						get_size_dict(__A							,	default_to_square=__A )
						snake_case__				:   Dict     =						resample if resample is not None else self.resample
						snake_case__				:   Optional[int]     =						do_center_crop if do_center_crop is not None else self.do_center_crop
						snake_case__				:   List[str]     =						crop_size if crop_size is not None else self.crop_size
						snake_case__				:   Optional[int]     =						get_size_dict(__A )
						snake_case__				:   int     =						do_rescale if do_rescale is not None else self.do_rescale
						snake_case__				:   Any     =						rescale_factor if rescale_factor is not None else self.rescale_factor
						snake_case__				:   List[str]     =						do_normalize if do_normalize is not None else self.do_normalize
						snake_case__				:   Optional[int]     =						image_mean if image_mean is not None else self.image_mean
						snake_case__				:   List[str]     =						image_std if image_std is not None else self.image_std

						snake_case__				:   Optional[Any]     =						make_list_of_images(__A )

						if not valid_images(__A ):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray." )

						if do_resize and size is None:
							raise ValueError("Size must be specified if do_resize is True." )

						if do_center_crop and crop_size is None:
							raise ValueError("Crop size must be specified if do_center_crop is True." )

						if do_rescale and rescale_factor is None:
							raise ValueError("Rescale factor must be specified if do_rescale is True." )

						if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("Image mean and std must be specified if do_normalize is True." )

						# All transformations expect numpy arrays.
						snake_case__				:   Union[str, Any]     =						[to_numpy_array(__A ) for image in images]

						if do_resize:
							snake_case__				:   int     =						[self.resize(image=__A							,	size=__A							,	resample=__A ) for image in images]

						if do_center_crop:
							snake_case__				:   Dict     =						[self.center_crop(image=__A							,	size=__A ) for image in images]

						if do_rescale:
							snake_case__				:   str     =						[self.rescale(image=__A							,	scale=__A ) for image in images]

						if do_normalize:
							snake_case__				:   Tuple     =						[self.normalize(image=__A							,	mean=__A							,	std=__A ) for image in images]

						snake_case__				:   str     =						[to_channel_dimension_format(__A							,	__A ) for image in images]

						snake_case__				:   List[Any]     =						{"pixel_values": images}
						return BatchFeature(data=__A							,	tensor_type=__A )











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   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							Union[str, Any]     ):
	snake_case__				:   Optional[int]     =						[]
	for part_id in partition_order:
		snake_case__				:   List[Any]     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Tuple     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Union[str, Any]     =						spark.range(100     ).repartition(1     )
	snake_case__				:   Any     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Optional[Any]     =						spark.range(10     ).repartition(2     )
	snake_case__				:   Optional[Any]     =						[1, 0]
	snake_case__				:   Dict     =						_generate_iterable_examples(snake_case_  ,						snake_case_     )  # Reverse the partitions.
	snake_case__				:   Tuple     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						snake_case_     )

	for i, (row_id, row_dict) in enumerate(generate_fn()     ):
		snake_case__,		snake_case__				:   Tuple     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Optional[int]     =						spark.range(10     ).repartition(1     )
	snake_case__				:   Union[str, Any]     =						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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   str     =						spark.range(30     ).repartition(3     )
	# Mock the generator so that shuffle reverses the partition indices.
	with patch("numpy.random.Generator"     ) as generator_mock:
		snake_case__				:   Union[str, Any]     =						lambda snake_case_     : x.reverse()
		snake_case__				:   Optional[int]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[2, 1, 0]     )

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

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

	# Partitions 1 and 3
	snake_case__				:   Any     =						SparkExamplesIterable(snake_case_     ).shard_data_sources(worker_id=1  ,						num_workers=2     )
	assert shard_it_a.n_shards == 2
	snake_case__				:   List[Any]     =						_get_expected_row_ids_and_row_dicts_for_partition_order(snake_case_  ,						[1, 3]     )
	for i, (row_id, row_dict) in enumerate(snake_case_     ):
		snake_case__,		snake_case__				:   Optional[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   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Dict     =						pyspark.sql.SparkSession.builder.master("local[*]"     ).appName("pyspark"     ).getOrCreate()
	snake_case__				:   Tuple     =						spark.range(100     ).repartition(1     )
	snake_case__				:   Union[str, Any]     =						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



