import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY



@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class     _A   (    unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      hf_hub_download(
           repo_id='''nateraw/video-demo'''   ,				filename='''archery.mp4'''   ,				repo_type='''dataset'''	)
       UpperCamelCase__										=      VideoClassificationPipeline(model=_a   ,				image_processor=_a   ,				top_k=2	)
       UpperCamelCase__										=      [
           example_video_filepath,
           """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
       ]
       return video_classifier, examples






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       for example in examples:
            UpperCamelCase__										=      video_classifier(_a	)

            self.assertEqual(
                _a   ,				[
                    {'''score''': ANY(_a	), '''label''': ANY(_a	)},
                    {'''score''': ANY(_a	), '''label''': ANY(_a	)},
                ]   ,				)






  @require_torch
  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      """hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
       UpperCamelCase__										=      VideoMAEFeatureExtractor(
           size={'''shortest_edge''': 10}   ,				crop_size={'''height''': 10, '''width''': 10}	)
       UpperCamelCase__										=      pipeline(
           '''video-classification'''   ,				model=_a   ,				feature_extractor=_a   ,				frame_sampling_rate=4	)

       UpperCamelCase__										=      hf_hub_download(repo_id='''nateraw/video-demo'''   ,				filename='''archery.mp4'''   ,				repo_type='''dataset'''	)
       UpperCamelCase__										=      video_classifier(_a   ,				top_k=2	)
       self.assertEqual(
           nested_simplify(_a   ,				decimals=4	)   ,				[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}]   ,				)

       UpperCamelCase__										=      video_classifier(
           [
               video_file_path,
               video_file_path,
           ]   ,				top_k=2   ,				)
       self.assertEqual(
           nested_simplify(_a   ,				decimals=4	)   ,				[
               [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
               [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
           ]   ,				)






  @require_tf
  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       pass





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


__magic_name__								=datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
     import pyspark



@dataclass
class     _A   (    datasets.BuilderConfig							):
  SCREAMING_SNAKE_CASE_	:	Optional[datasets.Features]						=None







def  __UpperCamelCase    (						A ,							A ,							):
     import pyspark

     def generate_fn():
          UpperCamelCase__										=      df.select('''*''' ,							pyspark.sql.functions.spark_partition_id().alias('''part_id'''							)							)
          for partition_id in partition_order:
               UpperCamelCase__										=      df_with_partition_id.select('''*'''							).where(f"part_id = {partition_id}"							).drop('''part_id'''							)
               UpperCamelCase__										=      partition_df.collect()
               UpperCamelCase__										=      0
               for row in rows:
                    yield f"{partition_id}_{row_id}", row.asDict()
                    row_id += 1

     return generate_fn



class     _A   (    _BaseExamplesIterable							):






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      df
       UpperCamelCase__										=      partition_order or range(self.df.rdd.getNumPartitions()	)
       UpperCamelCase__										=      _generate_iterable_examples(self.df   ,				self.partition_order	)






  def __iter__(self	)		->      int:





       '''simple docstring'''





       yield from self.generate_examples_fn()






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      "SparkExamplesIterable":





       '''simple docstring'''





       UpperCamelCase__										=      list(range(self.df.rdd.getNumPartitions()	)	)
       generator.shuffle(SCREAMING_SNAKE_CASE_	)
       return SparkExamplesIterable(self.df   ,				partition_order=SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      "SparkExamplesIterable":





       '''simple docstring'''





       UpperCamelCase__										=      self.split_shard_indices_by_worker(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       return SparkExamplesIterable(self.df   ,				partition_order=SCREAMING_SNAKE_CASE_	)






  @property
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       return len(self.partition_order	)



class     _A   (    datasets.DatasetBuilder							):
  SCREAMING_SNAKE_CASE_	:	List[str]						=SparkConfig






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Union[str, Any]:





       '''simple docstring'''





       import pyspark

       UpperCamelCase__										=      pyspark.sql.SparkSession.builder.getOrCreate()
       UpperCamelCase__										=      df
       UpperCamelCase__										=      working_dir

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






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       def create_cache_and_write_probe(SCREAMING_SNAKE_CASE_	):
            # 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=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      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(SCREAMING_SNAKE_CASE_   ,				'''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:
            UpperCamelCase__										=      (
                self._spark.sparkContext.parallelize(range(1	)   ,				1	).mapPartitions(SCREAMING_SNAKE_CASE_	).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 		_a					(self	)		->      Any:





       '''simple docstring'''





       return datasets.DatasetInfo(features=self.config.features	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       return [datasets.SplitGenerator(name=datasets.Split.TRAIN	)]






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       import pyspark

       def get_arrow_batch_size(SCREAMING_SNAKE_CASE_	):
            for batch in it:
                 yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]}	)

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				)		->      Iterable[Tuple[int, bool, Union[int, tuple]]]:





       '''simple docstring'''





       import pyspark

       UpperCamelCase__										=      ParquetWriter if file_format == '''parquet''' else ArrowWriter
       UpperCamelCase__										=      os.path.join(self._working_dir   ,				os.path.basename(SCREAMING_SNAKE_CASE_	)	) if self._working_dir else fpath
       UpperCamelCase__										=      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.
       UpperCamelCase__										=      self.config.features
       UpperCamelCase__										=      self._writer_batch_size
       UpperCamelCase__										=      self._fs.storage_options

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

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

       UpperCamelCase__										=      (
           self.df.mapInArrow(SCREAMING_SNAKE_CASE_   ,				'''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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "arrow"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[int]:





       '''simple docstring'''





       self._validate_cache_dir()

       UpperCamelCase__										=      convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE	)
       self._repartition_df_if_needed(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      not is_remote_filesystem(self._fs	)
       UpperCamelCase__										=      os.path.join if is_local else posixpath.join

       UpperCamelCase__										=      '''-TTTTT-SSSSS-of-NNNNN'''
       UpperCamelCase__										=      F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
       UpperCamelCase__										=      path_join(self._output_dir   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      0
       UpperCamelCase__										=      0
       UpperCamelCase__										=      0
       UpperCamelCase__										=      []
       UpperCamelCase__										=      []

       for task_id, content in self._prepare_split_single(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            (
                (
                UpperCamelCase__
            )						,       (
                UpperCamelCase__
            )						,       (
                UpperCamelCase__
            )						,       (
                UpperCamelCase__
            )						,       
            )										=      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(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      total_num_examples
       UpperCamelCase__										=      total_num_bytes

       # should rename everything at the end
       logger.debug(F"Renaming {total_shards} shards."	)
       if total_shards > 1:
            UpperCamelCase__										=      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.
            UpperCamelCase__										=      self._fs

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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				)		->      SparkExamplesIterable:





       '''simple docstring'''





       return SparkExamplesIterable(self.df	)



