
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


lowercase  : Tuple						   =							datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
      import pyspark

@dataclass
class __snake_case		(				datasets.BuilderConfig					):
    _a		:						Optional[datasets.Features]=    None







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     )			->   Dict:
       import pyspark

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

       return generate_fn

class __snake_case		(				_BaseExamplesIterable					):
    def __init__(  self		,snake_case		,snake_case=None		,):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				df
           lowercase							:					Optional[Any]	     =				partition_order or range(self.df.rdd.getNumPartitions()				)
           lowercase							:					Dict	     =				_generate_iterable_examples(self.df		,self.partition_order				)
    def __iter__(  self				):



           '''simple docstring'''







           yield from self.generate_examples_fn()
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				list(range(self.df.rdd.getNumPartitions()				)				)
           generator.shuffle(snake_case				)
           return SparkExamplesIterable(self.df		,partition_order=snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.split_shard_indices_by_worker(snake_case		,snake_case				)
           return SparkExamplesIterable(self.df		,partition_order=snake_case				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return len(self.partition_order				)






class __snake_case		(				datasets.DatasetBuilder					):
    _a		:						int=    SparkConfig
    def __init__(  self		,snake_case		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           import pyspark

           lowercase							:					str	     =				pyspark.sql.SparkSession.builder.getOrCreate()
           lowercase							:					List[Any]	     =				df
           lowercase							:					List[str]	     =				working_dir

           super().__init__(
               cache_dir=snake_case		,config_name=str(self.df.semanticHash()				)		,**snake_case		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           def create_cache_and_write_probe(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=snake_case				)
                  lowercase							:					Tuple	     =				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(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:
                  lowercase							:					Optional[Any]	     =				(
                      self._spark.sparkContext.parallelize(range(1				)		,1				).mapPartitions(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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return datasets.DatasetInfo(features=self.config.features				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           return [datasets.SplitGenerator(name=datasets.Split.TRAIN				)]
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case				):



           '''simple docstring'''







           import pyspark

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

           lowercase							:					Dict	     =				self.df.count()
           lowercase							:					Dict	     =				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.
           lowercase							:					Optional[Any]	     =				(
               self.df.limit(snake_case				)
               .repartition(1				)
               .mapInArrow(snake_case		,"""batch_bytes: long"""				)
               .agg(pyspark.sql.functions.sum("""batch_bytes"""				).alias("""sample_bytes"""				)				)
               .collect()[0]
               .sample_bytes
               / sample_num_rows
           )
           lowercase							:					Optional[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.
                  lowercase							:					str	     =				min(snake_case		,int(approx_total_size / max_shard_size				)				)
                  lowercase							:					str	     =				self.df.repartition(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







           import pyspark

           lowercase							:					Optional[int]	     =				ParquetWriter if file_format == """parquet""" else ArrowWriter
           lowercase							:					int	     =				os.path.join(self._working_dir		,os.path.basename(snake_case				)				) if self._working_dir else fpath
           lowercase							:					List[str]	     =				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.
           lowercase							:					int	     =				self.config.features
           lowercase							:					List[str]	     =				self._writer_batch_size
           lowercase							:					str	     =				self._fs.storage_options

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

                  if writer._num_bytes > 0:
                         lowercase							:					str	     =				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(snake_case				)				):
                                lowercase							:					Dict	     =				os.path.join(os.path.dirname(snake_case				)		,os.path.basename(snake_case				)				)
                                shutil.move(snake_case		,snake_case				)

           lowercase							:					Optional[Any]	     =				(
               self.df.mapInArrow(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 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = "arrow"		,snake_case = None		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           self._validate_cache_dir()

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

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

           lowercase							:					Optional[int]	     =				0
           lowercase							:					Dict	     =				0
           lowercase							:					Any	     =				0
           lowercase							:					Tuple	     =				[]
           lowercase							:					List[Any]	     =				[]

           for task_id, content in self._prepare_split_single(snake_case		,snake_case		,snake_case				):
                  (
                      lowercase      
                  )							:					Tuple	     =				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(snake_case				)

           lowercase							:					Union[str, Any]	     =				total_num_examples
           lowercase							:					int	     =				total_num_bytes

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

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

                  lowercase							:					Any	     =				[]
                  lowercase							:					int	     =				0
                  for i in range(len(snake_case				)				):
                         lowercase							:					Any	     =				task_id_and_num_shards[i]
                         for shard_id in range(snake_case				):
                                args.append([task_id, shard_id, global_shard_id]				)
                                global_shard_id += 1
                  self._spark.sparkContext.parallelize(snake_case		,len(snake_case				)				).map(lambda snake_case				: _rename_shard(*snake_case				)				).collect()
           else:
                  # don't use any pattern
                  lowercase							:					Dict	     =				0
                  lowercase							:					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(snake_case		,""""""				)		,)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,):



           '''simple docstring'''







           return SparkExamplesIterable(self.df				)


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				tempfile.mkdtemp()

           lowercase							:					List[str]	     =				[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """的""",
               """价""",
               """格""",
               """是""",
               """15""",
               """便""",
               """alex""",
               """##andra""",
               """，""",
               """。""",
               """-""",
               """t""",
               """shirt""",
           ]
           lowercase							:					Dict	     =				os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""]				)
           with open(self.vocab_file		,"""w"""		,encoding="""utf-8"""				) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

           lowercase							:					Any	     =				{
               """do_resize""": True,
               """size""": {"""height""": 224, """width""": 224},
               """do_center_crop""": True,
               """crop_size""": {"""height""": 18, """width""": 18},
               """do_normalize""": True,
               """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
               """do_convert_rgb""": True,
           }
           lowercase							:					List[str]	     =				os.path.join(self.tmpdirname		,snake_case				)
           with open(self.image_processor_file		,"""w"""		,encoding="""utf-8"""				) as fp:
                  json.dump(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizer.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           shutil.rmtree(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				[np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta				)]

           lowercase							:					str	     =				[Image.fromarray(np.moveaxis(snake_case		,0		,-1				)				) for x in image_inputs]

           return image_inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.get_tokenizer()
           lowercase							:					Dict	     =				self.get_rust_tokenizer()
           lowercase							:					Union[str, Any]	     =				self.get_image_processor()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_slow.save_pretrained(self.tmpdirname				)
           lowercase							:					Any	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,use_fast=snake_case				)

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_fast.save_pretrained(self.tmpdirname				)
           lowercase							:					Tuple	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname				)

           self.assertEqual(processor_slow.tokenizer.get_vocab()		,tokenizer_slow.get_vocab()				)
           self.assertEqual(processor_fast.tokenizer.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertEqual(tokenizer_slow.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertIsInstance(processor_slow.tokenizer		,snake_case				)
           self.assertIsInstance(processor_fast.tokenizer		,snake_case				)

           self.assertEqual(processor_slow.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertEqual(processor_fast.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertIsInstance(processor_slow.image_processor		,snake_case				)
           self.assertIsInstance(processor_fast.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           lowercase							:					int	     =				self.get_tokenizer(cls_token="""(CLS)"""		,sep_token="""(SEP)"""				)
           lowercase							:					Dict	     =				self.get_image_processor(do_normalize=snake_case				)

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor.from_pretrained(
               self.tmpdirname		,cls_token="""(CLS)"""		,sep_token="""(SEP)"""		,do_normalize=snake_case				)

           self.assertEqual(processor.tokenizer.get_vocab()		,tokenizer_add_kwargs.get_vocab()				)
           self.assertIsInstance(processor.tokenizer		,snake_case				)

           self.assertEqual(processor.image_processor.to_json_string()		,image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					List[str]	     =				self.get_tokenizer()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Optional[int]	     =				self.prepare_image_inputs()

           lowercase							:					Tuple	     =				image_processor(snake_case		,return_tensors="""np"""				)
           lowercase							:					Optional[int]	     =				processor(images=snake_case		,return_tensors="""np"""				)

           for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()		,input_processor[key].sum()		,delta=1e-2				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					Union[str, Any]	     =				self.get_tokenizer()

           lowercase							:					Any	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Any	     =				"""Alexandra，T-shirt的价格是15便士。"""

           lowercase							:					int	     =				processor(text=snake_case				)

           lowercase							:					Dict	     =				tokenizer(snake_case				)

           for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]		,encoded_processor[key]				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.get_image_processor()
           lowercase							:					Optional[Any]	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					List[str]	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Any	     =				self.prepare_image_inputs()

           lowercase							:					Optional[Any]	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

           # test if it raises when no input is passed
           with pytest.raises(snake_case				):
                  processor()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					str	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:					Any	     =				processor.batch_decode(snake_case				)
           lowercase							:					str	     =				tokenizer.batch_decode(snake_case				)

           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.get_image_processor()
           lowercase							:					Dict	     =				self.get_tokenizer()

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Optional[Any]	     =				self.prepare_image_inputs()

           lowercase							:					Dict	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,processor.model_input_names				)
