





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


_A :   Dict  			=  datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
       import pyspark







@dataclass
class        __SCREAMING_SNAKE_CASE      (			datasets.BuilderConfig ):
       _UpperCAmelCase				:  str			   =   None



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  )    ->    Any:







   """simple docstring"""
   import pyspark

   def generate_fn():
      lowerCamelCase__       :						str        =							df.select('''*'''      ,  pyspark.sql.functions.spark_partition_id().alias('''part_id'''					)					)
      for partition_id in partition_order:
         lowerCamelCase__       :						Optional[Any]        =							df_with_partition_id.select('''*'''					).where(f"part_id = {partition_id}"					).drop('''part_id'''					)
         lowerCamelCase__       :						Tuple        =							partition_df.collect()
         lowerCamelCase__       :						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 ):







       def __init__(				self					:     List[str]		, A					:     "pyspark.sql.DataFrame"		, A					:     int=None		, )       ->List[Any]:
          lowerCamelCase__       :						List[Any]        =							df
          lowerCamelCase__       :						int        =							partition_order or range(self.df.rdd.getNumPartitions()      )
          lowerCamelCase__       :						int        =							_generate_iterable_examples(self.df		, self.partition_order      )







       def __iter__(				self					:     Union[str, Any]      )       ->Optional[Any]:
          yield from self.generate_examples_fn()







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     np.random.Generator      )       ->"SparkExamplesIterable":
          lowerCamelCase__       :						List[str]        =							list(range(self.df.rdd.getNumPartitions()      )      )
          generator.shuffle(UpperCamelCase__      )
          return SparkExamplesIterable(self.df		, partition_order=UpperCamelCase__      )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     int		, A					:     int      )       ->"SparkExamplesIterable":
          lowerCamelCase__       :						int        =							self.split_shard_indices_by_worker(UpperCamelCase__		, UpperCamelCase__      )
          return SparkExamplesIterable(self.df		, partition_order=UpperCamelCase__      )







       @property
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->int:
          return len(self.partition_order      )







class        __SCREAMING_SNAKE_CASE      (			datasets.DatasetBuilder ):
       _UpperCAmelCase				:  Any			   =   SparkConfig







       def __init__(				self					:     int		, A					:     "pyspark.sql.DataFrame"		, A					:     str = None		, A					:     str = None		, **A					:     Optional[Any]		, )       ->str:
          import pyspark

          lowerCamelCase__       :						Optional[int]        =							pyspark.sql.SparkSession.builder.getOrCreate()
          lowerCamelCase__       :						Union[str, Any]        =							df
          lowerCamelCase__       :						str        =							working_dir

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







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:







          def create_cache_and_write_probe(A					:     Tuple      ):
             # 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=UpperCamelCase__      )
             lowerCamelCase__       :						List[Any]        =							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(UpperCamelCase__		, '''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:
             lowerCamelCase__       :						Union[str, Any]        =							(
                 self._spark.sparkContext.parallelize(range(1      )		, 1      ).mapPartitions(UpperCamelCase__      ).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 						__lowerCamelCase   (				self					:     Optional[int]      )       ->List[Any]:
          return datasets.DatasetInfo(features=self.config.features      )







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     datasets.download.download_manager.DownloadManager      )       ->Tuple:
          return [datasets.SplitGenerator(name=datasets.Split.TRAIN      )]







       def 						__lowerCamelCase   (				self					:     str		, A					:     Optional[int]      )       ->Union[str, Any]:
          import pyspark

          def get_arrow_batch_size(A					:     Union[str, Any]      ):
             for batch in it:
                yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}      )

          lowerCamelCase__       :						Optional[Any]        =							self.df.count()
          lowerCamelCase__       :						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.
          lowerCamelCase__       :						Optional[Any]        =							(
              self.df.limit(UpperCamelCase__      )
              .repartition(1      )
              .mapInArrow(UpperCamelCase__		, '''batch_bytes: long'''      )
              .agg(pyspark.sql.functions.sum('''batch_bytes'''      ).alias('''sample_bytes'''      )      )
              .collect()[0]
              .sample_bytes
              / sample_num_rows
          )
          lowerCamelCase__       :						Any        =							approx_bytes_per_row * df_num_rows
          if approx_total_size > max_shard_size:
             # Make sure there is at least one row per partition.
             lowerCamelCase__       :						Optional[int]        =							min(UpperCamelCase__		, int(approx_total_size / max_shard_size      )      )
             lowerCamelCase__       :						Tuple        =							self.df.repartition(UpperCamelCase__      )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     str		, A					:     str		, A					:     int		, )       ->Iterable[Tuple[int, bool, Union[int, tuple]]]:
          import pyspark

          lowerCamelCase__       :						int        =							ParquetWriter if file_format == '''parquet''' else ArrowWriter
          lowerCamelCase__       :						List[Any]        =							os.path.join(self._working_dir		, os.path.basename(UpperCamelCase__      )      ) if self._working_dir else fpath
          lowerCamelCase__       :						Optional[int]        =							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.
          lowerCamelCase__       :						Dict        =							self.config.features
          lowerCamelCase__       :						int        =							self._writer_batch_size
          lowerCamelCase__       :						List[Any]        =							self._fs.storage_options

          def write_arrow(A					:     List[Any]      ):
             # Within the same SparkContext, no two task attempts will share the same attempt ID.
             lowerCamelCase__       :						Optional[Any]        =							pyspark.TaskContext().taskAttemptId()
             lowerCamelCase__       :						Tuple        =							next(UpperCamelCase__		, UpperCamelCase__      )
             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''']		, )
             lowerCamelCase__       :						List[Any]        =							0
             lowerCamelCase__       :						Optional[int]        =							writer_class(
                 features=UpperCamelCase__		, path=working_fpath.replace('''SSSSS'''		, F"{shard_id:05d}"      ).replace('''TTTTT'''		, F"{task_id:05d}"      )		, writer_batch_size=UpperCamelCase__		, storage_options=UpperCamelCase__		, embed_local_files=UpperCamelCase__		, )
             lowerCamelCase__       :						Dict        =							pa.Table.from_batches([first_batch]      )
             writer.write_table(UpperCamelCase__      )
             for batch in it:
                if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                   lowerCamelCase__       :						List[str]        =							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
                   lowerCamelCase__       :						Dict        =							writer_class(
                       features=writer._features		, path=working_fpath.replace('''SSSSS'''		, F"{shard_id:05d}"      ).replace('''TTTTT'''		, F"{task_id:05d}"      )		, writer_batch_size=UpperCamelCase__		, storage_options=UpperCamelCase__		, embed_local_files=UpperCamelCase__		, )
                lowerCamelCase__       :						str        =							pa.Table.from_batches([batch]      )
                writer.write_table(UpperCamelCase__      )

             if writer._num_bytes > 0:
                lowerCamelCase__       :						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(UpperCamelCase__      )      ):
                   lowerCamelCase__       :						str        =							os.path.join(os.path.dirname(UpperCamelCase__      )		, os.path.basename(UpperCamelCase__      )      )
                   shutil.move(UpperCamelCase__		, UpperCamelCase__      )

          lowerCamelCase__       :						Any        =							(
              self.df.mapInArrow(UpperCamelCase__		, '''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 						__lowerCamelCase   (				self					:     Optional[Any]		, A					:     "datasets.SplitGenerator"		, A					:     str = "arrow"		, A					:     Optional[Union[str, int]] = None		, A					:     Optional[int] = None		, **A					:     Optional[int]		, )       ->Any:
          self._validate_cache_dir()

          lowerCamelCase__       :						Dict        =							convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE      )
          self._repartition_df_if_needed(UpperCamelCase__      )
          lowerCamelCase__       :						Optional[Any]        =							not is_remote_filesystem(self._fs      )
          lowerCamelCase__       :						Union[str, Any]        =							os.path.join if is_local else posixpath.join

          lowerCamelCase__       :						Tuple        =							'''-TTTTT-SSSSS-of-NNNNN'''
          lowerCamelCase__       :						List[str]        =							F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
          lowerCamelCase__       :						Union[str, Any]        =							path_join(self._output_dir		, UpperCamelCase__      )

          lowerCamelCase__       :						List[str]        =							0
          lowerCamelCase__       :						int        =							0
          lowerCamelCase__       :						Dict        =							0
          lowerCamelCase__       :						Dict        =							[]
          lowerCamelCase__       :						Dict        =							[]

          for task_id, content in self._prepare_split_single(UpperCamelCase__		, UpperCamelCase__		, UpperCamelCase__      ):
             (
                 lowerCamelCase__
             )       :						Union[str, 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(UpperCamelCase__      )

          lowerCamelCase__       :						Union[str, Any]        =							total_num_examples
          lowerCamelCase__       :						List[Any]        =							total_num_bytes

          # should rename everything at the end
          logger.debug(F"Renaming {total_shards} shards."      )
          if total_shards > 1:
             lowerCamelCase__       :						List[str]        =							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.
             lowerCamelCase__       :						Union[str, Any]        =							self._fs

             # use the -SSSSS-of-NNNNN pattern
             def _rename_shard(
                 A					:     int		, A					:     int		, A					:     int		, ):
                rename(
                    UpperCamelCase__		, 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}"      )		, )

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







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     "datasets.SplitGenerator"		, )       ->SparkExamplesIterable:
          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 DetrImageProcessor


class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







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







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Dict:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_pad": self.do_pad,
          }







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     int		, A					:     List[str]=False      )       ->int:
          if not batched:
             lowerCamelCase__       :						Union[str, Any]        =							image_inputs[0]
             if isinstance(A		, Image.Image      ):
                lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							image.size
             else:
                lowerCamelCase__       ,							lowerCamelCase__       :						Optional[int]        =							image.shape[1], image.shape[2]
             if w < h:
                lowerCamelCase__       :						List[Any]        =							int(self.size['''shortest_edge'''] * h / w      )
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
             elif w > h:
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
                lowerCamelCase__       :						Dict        =							int(self.size['''shortest_edge'''] * w / h      )
             else:
                lowerCamelCase__       :						List[Any]        =							self.size['''shortest_edge''']
                lowerCamelCase__       :						Any        =							self.size['''shortest_edge''']

          else:
             lowerCamelCase__       :						Optional[Any]        =							[]
             for image in image_inputs:
                lowerCamelCase__       ,							lowerCamelCase__       :						Any        =							self.get_expected_values([image]      )
                expected_values.append((expected_height, expected_width)      )
             lowerCamelCase__       :						Dict        =							max(A		, key=lambda A      : item[0]      )[0]
             lowerCamelCase__       :						Dict        =							max(A		, key=lambda A      : item[1]      )[1]

          return expected_height, expected_width


@require_torch
@require_vision
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Optional[Any]			   =   DetrImageProcessor if is_vision_available() else None







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessingTester(self      )







       @property
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          return self.image_processor_tester.prepare_image_processor_dict()







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						Optional[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_rescale'''      )      )
          self.assertTrue(hasattr(A		, '''rescale_factor'''      )      )
          self.assertTrue(hasattr(A		, '''do_resize'''      )      )
          self.assertTrue(hasattr(A		, '''size'''      )      )
          self.assertTrue(hasattr(A		, '''do_pad'''      )      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Union[str, Any]:
          lowerCamelCase__       :						List[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      )

          lowerCamelCase__       :						str        =							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 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[Any]:
          pass







       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[Any]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          lowerCamelCase__       :						str        =							prepare_image_inputs(self.image_processor_tester		, equal_resolution=A      )
          for image in image_inputs:
             self.assertIsInstance(A		, Image.Image      )

          # Test not batched input
          lowerCamelCase__       :						Optional[Any]        =							image_processing(image_inputs[0]		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						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
          lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.image_processor_tester.get_expected_values(A		, batched=A      )

          lowerCamelCase__       :						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,
                  expected_height,
                  expected_width,
              )		, )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
          # Initialize image_processing
          lowerCamelCase__       :						Any        =							self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          lowerCamelCase__       :						int        =							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
          lowerCamelCase__       :						List[str]        =							image_processing(image_inputs[0]		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						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
          lowerCamelCase__       :						List[Any]        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[Any]        =							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 						__lowerCamelCase   (				self					:     Any      )       ->Any:
          # Initialize image_processing
          lowerCamelCase__       :						Optional[Any]        =							self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          lowerCamelCase__       :						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
          lowerCamelCase__       :						str        =							image_processing(image_inputs[0]		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						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
          lowerCamelCase__       :						str        =							image_processing(A		, return_tensors='''pt'''      ).pixel_values

          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							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 						__lowerCamelCase   (				self					:     Tuple      )       ->List[Any]:
          # prepare image and target
          lowerCamelCase__       :						Union[str, Any]        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		, '''r'''      ) as f:
             lowerCamelCase__       :						Union[str, Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						List[str]        =							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

          # encode them
          lowerCamelCase__       :						Optional[int]        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''      )
          lowerCamelCase__       :						List[Any]        =							image_processing(images=A		, annotations=A		, return_tensors='''pt'''      )

          # verify pixel values
          lowerCamelCase__       :						Optional[int]        =							torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
          self.assertEqual(encoding['''pixel_values'''].shape		, A      )

          lowerCamelCase__       :						Any        =							torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A		, atol=1e-4      )      )

          # verify area
          lowerCamelCase__       :						Dict        =							torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, A      )      )
          # verify boxes
          lowerCamelCase__       :						Any        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						str        =							torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A		, atol=1e-3      )      )
          # verify image_id
          lowerCamelCase__       :						str        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						List[str]        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						List[Any]        =							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
          lowerCamelCase__       :						str        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						str        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )







       @slow
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[str]:
          # prepare image, target and masks_path
          lowerCamelCase__       :						Any        =							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		, '''r'''      ) as f:
             lowerCamelCase__       :						Optional[Any]        =							json.loads(f.read()      )

          lowerCamelCase__       :						Union[str, Any]        =							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

          lowerCamelCase__       :						List[Any]        =							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

          # encode them
          lowerCamelCase__       :						Any        =							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''      )
          lowerCamelCase__       :						Tuple        =							image_processing(images=A		, annotations=A		, masks_path=A		, return_tensors='''pt'''      )

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

          lowerCamelCase__       :						str        =							torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		, A		, atol=1e-4      )      )

          # verify area
          lowerCamelCase__       :						int        =							torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		, A      )      )
          # verify boxes
          lowerCamelCase__       :						Optional[Any]        =							torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		, A      )
          lowerCamelCase__       :						Optional[Any]        =							torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		, A		, atol=1e-3      )      )
          # verify image_id
          lowerCamelCase__       :						int        =							torch.tensor([3_9_7_6_9]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		, A      )      )
          # verify is_crowd
          lowerCamelCase__       :						Dict        =							torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		, A      )      )
          # verify class_labels
          lowerCamelCase__       :						int        =							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
          lowerCamelCase__       :						Union[str, Any]        =							8_2_2_8_7_3
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		, A      )
          # verify orig_size
          lowerCamelCase__       :						List[Any]        =							torch.tensor([4_8_0, 6_4_0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		, A      )      )
          # verify size
          lowerCamelCase__       :						Tuple        =							torch.tensor([8_0_0, 1_0_6_6]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		, A      )      )



