




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_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      str      )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				for part_id in partition_order:
								__lowerCamelCase		       =    df.where(F"""SPARK_PARTITION_ID() = {part_id}"""      ).collect()
								for row_idx, row in enumerate(_UpperCAmelCase      ):
												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_		(       )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
				__lowerCamelCase		       =    spark.range(100      ).repartition(1      )
				__lowerCamelCase		       =    Spark(_UpperCAmelCase      )
				# 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_		(       )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase		       =    pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
				__lowerCamelCase		       =    spark.range(10      ).repartition(2      )
				__lowerCamelCase		       =    [1, 0]
				__lowerCamelCase		       =    _generate_iterable_examples(_UpperCAmelCase ,     _UpperCAmelCase      )  # Reverse the partitions.
				__lowerCamelCase		       =    _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase ,     _UpperCAmelCase      )

				for i, (row_id, row_dict) in enumerate(generate_fn()      ):
								__lowerCamelCase		       =    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"""


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







@require_not_windows
@require_dill_gt_0_3_2
def 			lowerCamelCase_		(       )     ->       str:





				"""simple docstring"""


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

								__lowerCamelCase		       =    SparkExamplesIterable(_UpperCAmelCase      ).shuffle_data_sources(_UpperCAmelCase      )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(_UpperCAmelCase      ):
												__lowerCamelCase		       =    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"""


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

				# Partitions 0 and 2
				__lowerCamelCase		       =    SparkExamplesIterable(_UpperCAmelCase      ).shard_data_sources(worker_id=0 ,     num_workers=2      )
				assert shard_it_a.n_shards == 2
				__lowerCamelCase		       =    _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase ,     [0, 2]      )
				for i, (row_id, row_dict) in enumerate(_UpperCAmelCase      ):
								__lowerCamelCase		       =    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		       =    SparkExamplesIterable(_UpperCAmelCase      ).shard_data_sources(worker_id=1 ,     num_workers=2      )
				assert shard_it_a.n_shards == 2
				__lowerCamelCase		       =    _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase ,     [1, 3]      )
				for i, (row_id, row_dict) in enumerate(_UpperCAmelCase      ):
								__lowerCamelCase		       =    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_		(       )     ->       Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    pyspark.sql.SparkSession.builder.master('local[*]'      ).appName('pyspark'      ).getOrCreate()
				__lowerCamelCase		       =    spark.range(100      ).repartition(1      )
				__lowerCamelCase		       =    Spark(_UpperCAmelCase      )
				# 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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileViTImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"center_crop"       )       )
             self.assertTrue(hasattr(A						,					"do_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 20}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Dict							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  List[Any]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					np.ndarray       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  List[str]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Any							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

