


'''simple docstring'''






import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def       _snake_case     (  A				,					A				,					A				,					A				,					A=True				,					A="pt" )		->					Tuple:
							lowerCAmelCase__      =			{'''add_prefix_space''': True} if isinstance(A				,					A ) and not line.startswith(''' ''' ) else {}
							lowerCAmelCase__      =			padding_side
							return tokenizer(
							    [line]				,					max_length=A				,					padding='''max_length''' if pad_to_max_length else None				,					truncation=A				,					return_tensors=A				,					add_special_tokens=A				,					**A				,					)


def       _snake_case     (  A				,					A				,					A=None				,					)		->					str:
							lowerCAmelCase__      =			input_ids.ne(A ).any(dim=0 )
							if attention_mask is None:
														return input_ids[:, keep_column_mask]
							else:
														return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_="train"      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					lowerCamelCase_=""      ,					) ->							str:
														super().__init__()
														lowerCAmelCase__      =			Path(lowerCamelCase_     ).joinpath(type_path + '''.source'''     )
														lowerCAmelCase__      =			Path(lowerCamelCase_     ).joinpath(type_path + '''.target'''     )
														lowerCAmelCase__      =			self.get_char_lens(self.src_file     )
														lowerCAmelCase__      =			max_source_length
														lowerCAmelCase__      =			max_target_length
														assert min(self.src_lens     ) > 0, F"""found empty line in {self.src_file}"""
														lowerCAmelCase__      =			tokenizer
														lowerCAmelCase__      =			prefix
														if n_obs is not None:
																					lowerCAmelCase__      =			self.src_lens[:n_obs]
														lowerCAmelCase__      =			src_lang
														lowerCAmelCase__      =			tgt_lang







							def __len__(	self     ) ->							Tuple:
														return len(self.src_lens     )







							def __getitem__(	self      ,					lowerCamelCase_     ) ->							Dict[str, torch.Tensor]:
														lowerCAmelCase__      =			index + 1  # linecache starts at 1
														lowerCAmelCase__      =			self.prefix + linecache.getline(str(self.src_file     )      ,					lowerCamelCase_     ).rstrip('''\n'''     )
														lowerCAmelCase__      =			linecache.getline(str(self.tgt_file     )      ,					lowerCamelCase_     ).rstrip('''\n'''     )
														assert source_line, F"""empty source line for index {index}"""
														assert tgt_line, F"""empty tgt line for index {index}"""

														# Need to add eos token manually for T5
														if isinstance(self.tokenizer      ,					lowerCamelCase_     ):
																					source_line += self.tokenizer.eos_token
																					tgt_line += self.tokenizer.eos_token

														# Pad source and target to the right
														lowerCAmelCase__      =			(
														    self.tokenizer.question_encoder if isinstance(self.tokenizer      ,					lowerCamelCase_     ) else self.tokenizer
														)
														lowerCAmelCase__      =			self.tokenizer.generator if isinstance(self.tokenizer      ,					lowerCamelCase_     ) else self.tokenizer

														lowerCAmelCase__      =			encode_line(lowerCamelCase_      ,					lowerCamelCase_      ,					self.max_source_length      ,					'''right'''     )
														lowerCAmelCase__      =			encode_line(lowerCamelCase_      ,					lowerCamelCase_      ,					self.max_target_length      ,					'''right'''     )

														lowerCAmelCase__      =			source_inputs['''input_ids'''].squeeze()
														lowerCAmelCase__      =			target_inputs['''input_ids'''].squeeze()
														lowerCAmelCase__      =			source_inputs['''attention_mask'''].squeeze()
														return {
														    "input_ids": source_ids,
														    "attention_mask": src_mask,
														    "decoder_input_ids": target_ids,
														}







							@staticmethod
							def 						__SCREAMING_SNAKE_CASE							(	lowerCamelCase_     ) ->							Dict:
														return [len(lowerCamelCase_     ) for x in Path(lowerCamelCase_     ).open().readlines()]







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Dict[str, torch.Tensor]:
														lowerCAmelCase__      =			torch.stack([x['''input_ids'''] for x in batch]     )
														lowerCAmelCase__      =			torch.stack([x['''attention_mask'''] for x in batch]     )
														lowerCAmelCase__      =			torch.stack([x['''decoder_input_ids'''] for x in batch]     )
														lowerCAmelCase__      =			(
														    self.tokenizer.generator.pad_token_id
														    if isinstance(self.tokenizer      ,					lowerCamelCase_     )
														    else self.tokenizer.pad_token_id
														)
														lowerCAmelCase__      =			(
														    self.tokenizer.question_encoder.pad_token_id
														    if isinstance(self.tokenizer      ,					lowerCamelCase_     )
														    else self.tokenizer.pad_token_id
														)
														lowerCAmelCase__      =			trim_batch(lowerCamelCase_      ,					lowerCamelCase_     )
														lowerCAmelCase__     ,      lowerCAmelCase__      =			trim_batch(lowerCamelCase_      ,					lowerCamelCase_      ,					attention_mask=lowerCamelCase_     )
														lowerCAmelCase__      =			{
														    '''input_ids''': source_ids,
														    '''attention_mask''': source_mask,
														    '''decoder_input_ids''': y,
														}
														return batch

__UpperCAmelCase					       =							getLogger(__name__)


def       _snake_case     (  A )		->					Tuple:
							return list(itertools.chain.from_iterable(A ) )


def       _snake_case     (  A )		->					None:
							lowerCAmelCase__      =			get_git_info()
							save_json(A				,					os.path.join(A				,					'''git_log.json''' ) )


def       _snake_case     (  A				,					A				,					A=4				,					**A )		->					Optional[int]:
							with open(A				,					'''w''' ) as f:
														json.dump(A				,					A				,					indent=A				,					**A )


def       _snake_case     (  A )		->					Optional[int]:
							with open(A ) as f:
														return json.load(A )


def       _snake_case     (  )		->					str:
							lowerCAmelCase__      =			git.Repo(search_parent_directories=A )
							lowerCAmelCase__      =			{
							    '''repo_id''': str(A ),
							    '''repo_sha''': str(repo.head.object.hexsha ),
							    '''repo_branch''': str(repo.active_branch ),
							    '''hostname''': str(socket.gethostname() ),
							}
							return repo_infos


def       _snake_case     (  A				,					A )		->					List:
							return list(map(A				,					A ) )


def       _snake_case     (  A				,					A )		->					str:
							with open(A				,					'''wb''' ) as f:
														return pickle.dump(A				,					A )


def       _snake_case     (  A )		->					int:

							def remove_articles(A ):
														return re.sub(R'''\b(a|an|the)\b'''				,					''' '''				,					A )

							def white_space_fix(A ):
														return " ".join(text.split() )

							def remove_punc(A ):
														lowerCAmelCase__      =			set(string.punctuation )
														return "".join(ch for ch in text if ch not in exclude )

							def lower(A ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(A ) ) ) )


def       _snake_case     (  A				,					A )		->					str:
							lowerCAmelCase__      =			normalize_answer(A ).split()
							lowerCAmelCase__      =			normalize_answer(A ).split()
							lowerCAmelCase__      =			Counter(A ) & Counter(A )
							lowerCAmelCase__      =			sum(common.values() )
							if num_same == 0:
														return 0
							lowerCAmelCase__      =			1.0 * num_same / len(A )
							lowerCAmelCase__      =			1.0 * num_same / len(A )
							lowerCAmelCase__      =			(2 * precision * recall) / (precision + recall)
							return fa


def       _snake_case     (  A				,					A )		->					str:
							return normalize_answer(A ) == normalize_answer(A )


def       _snake_case     (  A				,					A )		->					Dict:
							assert len(A ) == len(A )
							lowerCAmelCase__      =			0
							for hypo, pred in zip(A				,					A ):
														em += exact_match_score(A				,					A )
							if len(A ) > 0:
														em /= len(A )
							return {"em": em}


def       _snake_case     (  A )		->					Union[str, Any]:
							return model_prefix.startswith('''rag''' )






def       _snake_case     (  A				,					A				,					A )		->					Optional[Any]:
							lowerCAmelCase__      =			{p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							lowerCAmelCase__      =			'''dropout_rate'''
							for p in extra_params:
														if getattr(A				,					A				,					A ):
																					if not hasattr(A				,					A ) and not hasattr(A				,					equivalent_param[p] ):
																												logger.info('''config doesn\'t have a `{}` attribute'''.format(A ) )
																												delattr(A				,					A )
																												continue
																					lowerCAmelCase__      =			p if hasattr(A				,					A ) else equivalent_param[p]
																					setattr(A				,					A				,					getattr(A				,					A ) )
																					delattr(A				,					A )
							return hparams, config





'''simple docstring'''






from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)


def       _snake_case     (  A				,					A )		->					List[Any]:
							lowerCAmelCase__      =			[]
							for part_id in partition_order:
														lowerCAmelCase__      =			df.where(F"""SPARK_PARTITION_ID() = {part_id}""" ).collect()
														for row_idx, row in enumerate(A ):
																					expected_row_ids_and_row_dicts.append((F"""{part_id}_{row_idx}""", row.asDict()) )
							return expected_row_ids_and_row_dicts


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Tuple:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
							# that each partition can hold 2 rows.
							spark_builder._repartition_df_if_needed(max_shard_size=16 )
							# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
							assert spark_builder.df.rdd.getNumPartitions() == 50


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Optional[int]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(10 ).repartition(2 )
							lowerCAmelCase__      =			[1, 0]
							lowerCAmelCase__      =			_generate_iterable_examples(A				,					A )  # Reverse the partitions.
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					A )

							for i, (row_id, row_dict) in enumerate(generate_fn() ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Optional[Any]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(10 ).repartition(1 )
							lowerCAmelCase__      =			SparkExamplesIterable(A )
							assert it.n_shards == 1
							for i, (row_id, row_dict) in enumerate(A ):
														assert row_id == F"""0_{i}"""
														assert row_dict == {"id": i}


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Union[str, Any]:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(30 ).repartition(3 )
							# Mock the generator so that shuffle reverses the partition indices.
							with patch('''numpy.random.Generator''' ) as generator_mock:
														lowerCAmelCase__      =			lambda A : x.reverse()
														lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[2, 1, 0] )

														lowerCAmelCase__      =			SparkExamplesIterable(A ).shuffle_data_sources(A )
														assert shuffled_it.n_shards == 3
														for i, (row_id, row_dict) in enumerate(A ):
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts[i]
																					assert row_id == expected_row_id
																					assert row_dict == expected_row_dict


@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(20 ).repartition(4 )

							# Partitions 0 and 2
							lowerCAmelCase__      =			SparkExamplesIterable(A ).shard_data_sources(worker_id=0				,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[0, 2] )
							for i, (row_id, row_dict) in enumerate(A ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict

							# Partitions 1 and 3
							lowerCAmelCase__      =			SparkExamplesIterable(A ).shard_data_sources(worker_id=1				,					num_workers=2 )
							assert shard_it_a.n_shards == 2
							lowerCAmelCase__      =			_get_expected_row_ids_and_row_dicts_for_partition_order(A				,					[1, 3] )
							for i, (row_id, row_dict) in enumerate(A ):
														lowerCAmelCase__     ,      lowerCAmelCase__      =			expected_row_ids_and_row_dicts_a[i]
														assert row_id == expected_row_id
														assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def       _snake_case     (  )		->					Dict:
							lowerCAmelCase__      =			pyspark.sql.SparkSession.builder.master('''local[*]''' ).appName('''pyspark''' ).getOrCreate()
							lowerCAmelCase__      =			spark.range(100 ).repartition(1 )
							lowerCAmelCase__      =			Spark(A )
							# Choose a small max_shard_size for maximum partitioning.
							spark_builder._repartition_df_if_needed(max_shard_size=1 )
							# The new number of partitions should not be greater than the number of rows.
							assert spark_builder.df.rdd.getNumPartitions() == 100