import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf

if is_torch_available():
     import torch


__magic_name__								=logging.get_logger(__name__)



class     _A   :






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





       '''simple docstring'''





       if not conversation_id:
            UpperCamelCase__										=      uuid.uuida()
       if past_user_inputs is None:
            UpperCamelCase__										=      []
       if generated_responses is None:
            UpperCamelCase__										=      []

       UpperCamelCase__										=      conversation_id
       UpperCamelCase__										=      past_user_inputs
       UpperCamelCase__										=      generated_responses
       UpperCamelCase__										=      text






  def __eq__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            return False
       if self.uuid == other.uuid:
            return True
       return (
           self.new_user_input == other.new_user_input
           and self.past_user_inputs == other.past_user_inputs
           and self.generated_responses == other.generated_responses
       )






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





       '''simple docstring'''





       if self.new_user_input:
            if overwrite:
                 logger.warning(
                     F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
                     F"with: \"{text}\"."	)
                 UpperCamelCase__										=      text
            else:
                 logger.warning(
                     F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
                     F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"	)
       else:
            UpperCamelCase__										=      text






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       if self.new_user_input:
            self.past_user_inputs.append(self.new_user_input	)
       UpperCamelCase__										=      None






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       self.generated_responses.append(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       for user_input, generated_response in zip(self.past_user_inputs   ,				self.generated_responses	):
            yield True, user_input
            yield False, generated_response
       if self.new_user_input:
            yield True, self.new_user_input






  def __repr__(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      F"Conversation id: {self.uuid} \n"
       for is_user, text in self.iter_texts():
            UpperCamelCase__										=      '''user''' if is_user else '''bot'''
            output += F"{name} >> {text} \n"
       return output



@add_end_docstrings(
    __UpperCamelCase	, R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "	, )
class     _A   (    __UpperCamelCase							):






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





       '''simple docstring'''





       super().__init__(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
       if self.tokenizer.pad_token_id is None:
            UpperCamelCase__										=      self.tokenizer.eos_token






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      {}
       UpperCamelCase__										=      {}
       UpperCamelCase__										=      {}

       if min_length_for_response is not None:
            UpperCamelCase__										=      min_length_for_response
       if minimum_tokens is not None:
            UpperCamelCase__										=      minimum_tokens

       if "max_length" in generate_kwargs:
            UpperCamelCase__										=      generate_kwargs['''max_length''']
            # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
       if clean_up_tokenization_spaces is not None:
            UpperCamelCase__										=      clean_up_tokenization_spaces

       if generate_kwargs:
            forward_params.update(SCREAMING_SNAKE_CASE_	)
       return preprocess_params, forward_params, postprocess_params






  def __call__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=0   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      super().__call__(SCREAMING_SNAKE_CASE_   ,				num_workers=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
       if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) and len(SCREAMING_SNAKE_CASE_	) == 1:
            return outputs[0]
       return outputs






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=32	)		->      Dict[str, Any]:





       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            raise ValueError('''ConversationalPipeline, expects Conversation as inputs'''	)
       if conversation.new_user_input is None:
            raise ValueError(
                F"Conversation with UUID {type(conversation.uuid	)} does not contain new user input to process. "
                '''Add user inputs with the conversation\'s `add_user_input` method'''	)
       if hasattr(self.tokenizer   ,				'''_build_conversation_input_ids'''	):
            UpperCamelCase__										=      self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE_	)
       else:
            # If the tokenizer cannot handle conversations, we default to only the old version
            UpperCamelCase__										=      self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE_	)

       if self.framework == "pt":
            UpperCamelCase__										=      torch.LongTensor([input_ids]	)
       elif self.framework == "tf":
            UpperCamelCase__										=      tf.constant([input_ids]	)
       return {"input_ids": input_ids, "conversation": conversation}






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=10   ,				**SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      generate_kwargs.get('''max_length'''   ,				self.model.config.max_length	)

       UpperCamelCase__										=      model_inputs['''input_ids'''].shape[1]
       if max_length - minimum_tokens < n:
            logger.warning(F"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"	)
            UpperCamelCase__										=      max_length - minimum_tokens
            UpperCamelCase__										=      model_inputs['''input_ids'''][:, -trim:]
            if "attention_mask" in model_inputs:
                 UpperCamelCase__										=      model_inputs['''attention_mask'''][:, -trim:]
       UpperCamelCase__										=      model_inputs.pop('''conversation'''	)
       UpperCamelCase__										=      max_length
       UpperCamelCase__										=      self.model.generate(**SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
       if self.model.config.is_encoder_decoder:
            UpperCamelCase__										=      1
       else:
            UpperCamelCase__										=      n
       return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=True	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      model_outputs['''output_ids''']
       UpperCamelCase__										=      self.tokenizer.decode(
           output_ids[0]   ,				skip_special_tokens=SCREAMING_SNAKE_CASE_   ,				clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_   ,				)
       UpperCamelCase__										=      model_outputs['''conversation''']
       conversation.mark_processed()
       conversation.append_response(SCREAMING_SNAKE_CASE_	)
       return conversation






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.tokenizer.eos_token_id
       UpperCamelCase__										=      []
       for is_user, text in conversation.iter_texts():
            if eos_token_id is not None:
                 input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	) + [eos_token_id]	)
            else:
                 input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_	)	)

       if len(SCREAMING_SNAKE_CASE_	) > self.tokenizer.model_max_length:
            UpperCamelCase__										=      input_ids[-self.tokenizer.model_max_length :]
       return input_ids





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	)



