






import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


__lowerCamelCase      :    int   		=   datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
	import pyspark



@dataclass
class 							SCREAMING_SNAKE_CASE__		(       datasets.BuilderConfig		):





					"""simple docstring"""






					a_			=	None







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							"pyspark.sql.DataFrame"  ,						snake_case_      :							List[int]  ,						):
	import pyspark

	def generate_fn():
		snake_case__				:   str     =						df.select("*"  ,						pyspark.sql.functions.spark_partition_id().alias("part_id"     )     )
		for partition_id in partition_order:
			snake_case__				:   Tuple     =						df_with_partition_id.select("*"     ).where(F'''part_id = {partition_id}'''     ).drop("part_id"     )
			snake_case__				:   Any     =						partition_df.collect()
			snake_case__				:   List[str]     =						0
			for row in rows:
				yield F'''{partition_id}_{row_id}''', row.asDict()
				row_id += 1

	return generate_fn



class 							SCREAMING_SNAKE_CASE__		(       _BaseExamplesIterable		):





					"""simple docstring"""






					def __init__(      self  :						Optional[Any]							,	__A  :						"pyspark.sql.DataFrame"							,	__A  :						List[str]=None							,	):
						snake_case__				:   Any     =						df
						snake_case__				:   Union[str, Any]     =						partition_order or range(self.df.rdd.getNumPartitions() )
						snake_case__				:   Dict     =						_generate_iterable_examples(self.df							,	self.partition_order )



					def __iter__(      self  :						Optional[int] ):
						yield from self.generate_examples_fn()



					def  _lowercase  (      self  :						Tuple							,	__A  :						np.random.Generator ):
						snake_case__				:   Dict     =						list(range(self.df.rdd.getNumPartitions() ) )
						generator.shuffle(__A )
						return SparkExamplesIterable(self.df							,	partition_order=__A )



					def  _lowercase  (      self  :						Tuple							,	__A  :						int							,	__A  :						int ):
						snake_case__				:   Union[str, Any]     =						self.split_shard_indices_by_worker(__A							,	__A )
						return SparkExamplesIterable(self.df							,	partition_order=__A )






					@property
					def  _lowercase  (      self  :						Union[str, Any] ):
						return len(self.partition_order )



class 							SCREAMING_SNAKE_CASE__		(       datasets.DatasetBuilder		):





					"""simple docstring"""






					a_			=	SparkConfig



					def __init__(      self  :						Union[str, Any]							,	__A  :						"pyspark.sql.DataFrame"							,	__A  :						str = None							,	__A  :						str = None							,	**__A  :						Any							,	):
						import pyspark

						snake_case__				:   List[str]     =						pyspark.sql.SparkSession.builder.getOrCreate()
						snake_case__				:   int     =						df
						snake_case__				:   Dict     =						working_dir

						super().__init__(
						    cache_dir=__A							,	config_name=str(self.df.semanticHash() )							,	**__A							,	)



					def  _lowercase  (      self  :						List[Any] ):
						# Returns the path of the created file.
						def create_cache_and_write_probe(__A  :						Optional[int] ):
							# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
							# already exist.
							os.makedirs(self._cache_dir							,	exist_ok=__A )
							snake_case__				:   List[str]     =						os.path.join(self._cache_dir							,	"fs_test" + uuid.uuida().hex )
							# Opening the file in append mode will create a new file unless it already exists, in which case it will not
							# change the file contents.
							open(__A							,	"a" )
							return [probe_file]

						if self._spark.conf.get("spark.master"							,	"" ).startswith("local" ):
							return

						# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
						# accessible to the driver.
						# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
						if self._cache_dir:
							snake_case__				:   int     =						(
							    self._spark.sparkContext.parallelize(range(1 )							,	1 ).mapPartitions(__A ).collect()
							)
							if os.path.isfile(probe[0] ):
								return

						raise ValueError(
						    "When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir" )



					def  _lowercase  (      self  :						Tuple ):
						return datasets.DatasetInfo(features=self.config.features )



					def  _lowercase  (      self  :						Tuple							,	__A  :						datasets.download.download_manager.DownloadManager ):
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]



					def  _lowercase  (      self  :						Tuple							,	__A  :						List[str] ):
						import pyspark

						def get_arrow_batch_size(__A  :						List[str] ):
							for batch in it:
								yield pa.RecordBatch.from_pydict({"batch_bytes": [batch.nbytes]} )

						snake_case__				:   List[str]     =						self.df.count()
						snake_case__				:   Dict     =						df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
						# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
						snake_case__				:   int     =						(
						    self.df.limit(__A )
						    .repartition(1 )
						    .mapInArrow(__A							,	"batch_bytes: long" )
						    .agg(pyspark.sql.functions.sum("batch_bytes" ).alias("sample_bytes" ) )
						    .collect()[0]
						    .sample_bytes
						    / sample_num_rows
						)
						snake_case__				:   List[str]     =						approx_bytes_per_row * df_num_rows
						if approx_total_size > max_shard_size:
							# Make sure there is at least one row per partition.
							snake_case__				:   int     =						min(__A							,	int(approx_total_size / max_shard_size ) )
							snake_case__				:   Any     =						self.df.repartition(__A )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						str							,	__A  :						str							,	__A  :						int							,	):
						import pyspark

						snake_case__				:   str     =						ParquetWriter if file_format == "parquet" else ArrowWriter
						snake_case__				:   List[str]     =						os.path.join(self._working_dir							,	os.path.basename(__A ) ) if self._working_dir else fpath
						snake_case__				:   Tuple     =						file_format == "parquet"

						# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
						# pickling the SparkContext.
						snake_case__				:   str     =						self.config.features
						snake_case__				:   Tuple     =						self._writer_batch_size
						snake_case__				:   str     =						self._fs.storage_options

						def write_arrow(__A  :						Optional[int] ):
							# Within the same SparkContext, no two task attempts will share the same attempt ID.
							snake_case__				:   Tuple     =						pyspark.TaskContext().taskAttemptId()
							snake_case__				:   int     =						next(__A							,	__A )
							if first_batch is None:
								# Some partitions might not receive any data.
								return pa.RecordBatch.from_arrays(
								    [[task_id], [0], [0]]							,	names=["task_id", "num_examples", "num_bytes"]							,	)
							snake_case__				:   int     =						0
							snake_case__				:   Dict     =						writer_class(
							    features=__A							,	path=working_fpath.replace("SSSSS"							,	f'''{shard_id:05d}''' ).replace("TTTTT"							,	f'''{task_id:05d}''' )							,	writer_batch_size=__A							,	storage_options=__A							,	embed_local_files=__A							,	)
							snake_case__				:   Union[str, Any]     =						pa.Table.from_batches([first_batch] )
							writer.write_table(__A )
							for batch in it:
								if max_shard_size is not None and writer._num_bytes >= max_shard_size:
									snake_case__,		snake_case__				:   Any     =						writer.finalize()
									writer.close()
									yield pa.RecordBatch.from_arrays(
									    [[task_id], [num_examples], [num_bytes]]							,	names=["task_id", "num_examples", "num_bytes"]							,	)
									shard_id += 1
									snake_case__				:   Dict     =						writer_class(
									    features=writer._features							,	path=working_fpath.replace("SSSSS"							,	f'''{shard_id:05d}''' ).replace("TTTTT"							,	f'''{task_id:05d}''' )							,	writer_batch_size=__A							,	storage_options=__A							,	embed_local_files=__A							,	)
								snake_case__				:   int     =						pa.Table.from_batches([batch] )
								writer.write_table(__A )

							if writer._num_bytes > 0:
								snake_case__,		snake_case__				:   List[Any]     =						writer.finalize()
								writer.close()
								yield pa.RecordBatch.from_arrays(
								    [[task_id], [num_examples], [num_bytes]]							,	names=["task_id", "num_examples", "num_bytes"]							,	)

							if working_fpath != fpath:
								for file in os.listdir(os.path.dirname(__A ) ):
									snake_case__				:   Optional[int]     =						os.path.join(os.path.dirname(__A )							,	os.path.basename(__A ) )
									shutil.move(__A							,	__A )

						snake_case__				:   Dict     =						(
						    self.df.mapInArrow(__A							,	"task_id: long, num_examples: long, num_bytes: long" )
						    .groupBy("task_id" )
						    .agg(
						        pyspark.sql.functions.sum("num_examples" ).alias("total_num_examples" )							,	pyspark.sql.functions.sum("num_bytes" ).alias("total_num_bytes" )							,	pyspark.sql.functions.count("num_bytes" ).alias("num_shards" )							,	pyspark.sql.functions.collect_list("num_examples" ).alias("shard_lengths" )							,	)
						    .collect()
						)
						for row in stats:
							yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						"datasets.SplitGenerator"							,	__A  :						str = "arrow"							,	__A  :						Optional[Union[str, int]] = None							,	__A  :						Optional[int] = None							,	**__A  :						str							,	):
						self._validate_cache_dir()

						snake_case__				:   str     =						convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
						self._repartition_df_if_needed(__A )
						snake_case__				:   int     =						not is_remote_filesystem(self._fs )
						snake_case__				:   List[Any]     =						os.path.join if is_local else posixpath.join

						snake_case__				:   List[Any]     =						"-TTTTT-SSSSS-of-NNNNN"
						snake_case__				:   Optional[int]     =						f'''{self.name}-{split_generator.name}{SUFFIX}.{file_format}'''
						snake_case__				:   Union[str, Any]     =						path_join(self._output_dir							,	__A )

						snake_case__				:   Tuple     =						0
						snake_case__				:   Tuple     =						0
						snake_case__				:   Optional[int]     =						0
						snake_case__				:   Union[str, Any]     =						[]
						snake_case__				:   Dict     =						[]

						for task_id, content in self._prepare_split_single(__A							,	__A							,	__A ):
							(
							    (
							    snake_case__
							),		(
							    snake_case__
							),		(
							    snake_case__
							),		(
							    snake_case__
							),		
							)				:   Optional[Any]     =						content
							if num_bytes > 0:
								total_num_examples += num_examples
								total_num_bytes += num_bytes
								total_shards += num_shards
								task_id_and_num_shards.append((task_id, num_shards) )
								all_shard_lengths.extend(__A )

						snake_case__				:   Dict     =						total_num_examples
						snake_case__				:   Tuple     =						total_num_bytes

						# should rename everything at the end
						logger.debug(f'''Renaming {total_shards} shards.''' )
						if total_shards > 1:
							snake_case__				:   Union[str, Any]     =						all_shard_lengths

							# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
							# pickling error due to pickling the SparkContext.
							snake_case__				:   Union[str, Any]     =						self._fs

							# use the -SSSSS-of-NNNNN pattern
							def _rename_shard(
							    __A  :						int							,	__A  :						int							,	__A  :						int							,	):
								rename(
								    __A							,	fpath.replace("SSSSS"							,	f'''{shard_id:05d}''' ).replace("TTTTT"							,	f'''{task_id:05d}''' )							,	fpath.replace("TTTTT-SSSSS"							,	f'''{global_shard_id:05d}''' ).replace("NNNNN"							,	f'''{total_shards:05d}''' )							,	)

							snake_case__				:   Optional[Any]     =						[]
							snake_case__				:   List[str]     =						0
							for i in range(len(__A ) ):
								snake_case__,		snake_case__				:   Optional[int]     =						task_id_and_num_shards[i]
								for shard_id in range(__A ):
									args.append([task_id, shard_id, global_shard_id] )
									global_shard_id += 1
							self._spark.sparkContext.parallelize(__A							,	len(__A ) ).map(lambda __A : _rename_shard(*__A ) ).collect()
						else:
							# don't use any pattern
							snake_case__				:   Dict     =						0
							snake_case__				:   Optional[int]     =						task_id_and_num_shards[0][0]
							self._rename(
							    fpath.replace("SSSSS"							,	f'''{shard_id:05d}''' ).replace("TTTTT"							,	f'''{task_id:05d}''' )							,	fpath.replace(__A							,	"" )							,	)






					def  _lowercase  (      self  :						Tuple							,	__A  :						"datasets.SplitGenerator"							,	):
						return SparkExamplesIterable(self.df )











import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DeformableDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						int							,	__A  :						List[str]							,	__A  :						Union[str, Any]=7							,	__A  :						Any=3							,	__A  :						Optional[Any]=3_0							,	__A  :						List[str]=4_0_0							,	__A  :						str=True							,	__A  :						Optional[Any]=None							,	__A  :						Optional[int]=True							,	__A  :						int=[0.5, 0.5, 0.5]							,	__A  :						Dict=[0.5, 0.5, 0.5]							,	__A  :						Optional[int]=True							,	__A  :						int=1 / 2_5_5							,	__A  :						List[str]=True							,	):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__				:   List[str]     =						size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__				:   Optional[Any]     =						parent
						snake_case__				:   str     =						batch_size
						snake_case__				:   Union[str, Any]     =						num_channels
						snake_case__				:   Optional[Any]     =						min_resolution
						snake_case__				:   List[str]     =						max_resolution
						snake_case__				:   Tuple     =						do_resize
						snake_case__				:   str     =						size
						snake_case__				:   str     =						do_normalize
						snake_case__				:   Optional[Any]     =						image_mean
						snake_case__				:   List[str]     =						image_std
						snake_case__				:   List[str]     =						do_rescale
						snake_case__				:   Tuple     =						rescale_factor
						snake_case__				:   Tuple     =						do_pad



					def  _lowercase  (      self  :						str ):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}






					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						List[Any]							,	__A  :						List[Any]=False ):
						if not batched:
							snake_case__				:   List[Any]     =						image_inputs[0]
							if isinstance(__A							,	Image.Image ):
								snake_case__,		snake_case__				:   str     =						image.size
							else:
								snake_case__,		snake_case__				:   Dict     =						image.shape[1], image.shape[2]
							if w < h:
								snake_case__				:   Any     =						int(self.size["shortest_edge"] * h / w )
								snake_case__				:   Any     =						self.size["shortest_edge"]
							elif w > h:
								snake_case__				:   Optional[int]     =						self.size["shortest_edge"]
								snake_case__				:   Any     =						int(self.size["shortest_edge"] * w / h )
							else:
								snake_case__				:   Tuple     =						self.size["shortest_edge"]
								snake_case__				:   int     =						self.size["shortest_edge"]

						else:
							snake_case__				:   Any     =						[]
							for image in image_inputs:
								snake_case__,		snake_case__				:   Optional[Any]     =						self.get_expected_values([image] )
								expected_values.append((expected_height, expected_width) )
							snake_case__				:   List[Any]     =						max(__A							,	key=lambda __A : item[0] )[0]
							snake_case__				:   int     =						max(__A							,	key=lambda __A : item[1] )[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	DeformableDetrImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						str ):
						snake_case__				:   Optional[Any]     =						DeformableDetrImageProcessingTester(self )



					@property
					def  _lowercase  (      self  :						List[Any] ):
						return self.image_processor_tester.prepare_image_processor_dict()



					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Any     =						self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(__A							,	"image_mean" ) )
						self.assertTrue(hasattr(__A							,	"image_std" ) )
						self.assertTrue(hasattr(__A							,	"do_normalize" ) )
						self.assertTrue(hasattr(__A							,	"do_resize" ) )
						self.assertTrue(hasattr(__A							,	"do_rescale" ) )
						self.assertTrue(hasattr(__A							,	"do_pad" ) )
						self.assertTrue(hasattr(__A							,	"size" ) )



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Union[str, Any]     =						self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 1_8, "longest_edge": 1_3_3_3} )
						self.assertEqual(image_processor.do_pad							,	__A )

						snake_case__				:   Tuple     =						self.image_processing_class.from_dict(
						    self.image_processor_dict							,	size=4_2							,	max_size=8_4							,	pad_and_return_pixel_mask=__A )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 4_2, "longest_edge": 8_4} )
						self.assertEqual(image_processor.do_pad							,	__A )



					def  _lowercase  (      self  :						str ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						# Initialize image_processing
						snake_case__				:   Any     =						self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						snake_case__				:   Optional[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
						snake_case__				:   Tuple     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   List[str]     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__,		snake_case__				:   List[Any]     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						snake_case__				:   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,
						        expected_height,
						        expected_width,
						    )							,	)



					def  _lowercase  (      self  :						int ):
						# Initialize image_processing
						snake_case__				:   Any     =						self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						snake_case__				:   str     =						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
						snake_case__				:   Optional[Any]     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__				:   Tuple     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					def  _lowercase  (      self  :						Union[str, Any] ):
						# Initialize image_processing
						snake_case__				:   Optional[int]     =						self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						snake_case__				:   List[str]     =						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
						snake_case__				:   str     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Union[str, Any]     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__				:   Tuple     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Tuple     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					@slow
					def  _lowercase  (      self  :						Optional[Any] ):
						# prepare image and target
						snake_case__				:   Tuple     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,	"r" ) as f:
							snake_case__				:   Tuple     =						json.loads(f.read() )

						snake_case__				:   Union[str, Any]     =						{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__				:   str     =						DeformableDetrImageProcessor()
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   Optional[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Union[str, Any]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   str     =						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Union[str, Any]     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   List[Any]     =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   Any     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   int     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify orig_size
						snake_case__				:   List[str]     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   Tuple     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






					@slow
					def  _lowercase  (      self  :						Optional[int] ):
						# prepare image, target and masks_path
						snake_case__				:   Dict     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,	"r" ) as f:
							snake_case__				:   Any     =						json.loads(f.read() )

						snake_case__				:   Dict     =						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__				:   int     =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

						# encode them
						snake_case__				:   List[str]     =						DeformableDetrImageProcessor(format="coco_panoptic" )
						snake_case__				:   List[Any]     =						image_processing(images=__A							,	annotations=__A							,	masks_path=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   List[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Optional[int]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Tuple     =						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Any     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   Any     =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   List[str]     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Any     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   List[str]     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify masks
						snake_case__				:   Union[str, Any]     =						8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	__A )
						# verify orig_size
						snake_case__				:   int     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   Union[str, Any]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )



