






import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class    lowerCAmelCase__				(			__lowerCAmelCase       ):






						"""simple docstring"""




						def 					_UpperCamelCase			(   self				,							a_				,							a_=False     ):
							lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							str      =       (
							    filename_skip_take["filename"],
							    filename_skip_take["skip"] if "skip" in filename_skip_take else None,
							    filename_skip_take["take"] if "take" in filename_skip_take else None,
							)
							open(os.path.join(__SCREAMING_SNAKE_CASE     )				,							"wb"     ).close()
							lowerCamelCase_			:							Union[str, Any]      =       pa.Table.from_pydict({"filename": [Path(__SCREAMING_SNAKE_CASE     ).name] * 100}     )
							if take == -1:
								lowerCamelCase_			:							int      =       len(__SCREAMING_SNAKE_CASE     ) - skip
							if skip is not None and take is not None:
								lowerCamelCase_			:							Tuple      =       pa_table.slice(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE     )
							return pa_table





class    lowerCAmelCase__				(			__lowerCAmelCase       ):






						"""simple docstring"""




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Dict      =       "my_name"
							lowerCamelCase_			:							int      =       SplitInfo(name="train"				,							num_examples=100     )
							lowerCamelCase_			:							int      =       SplitInfo(name="test"				,							num_examples=100     )
							lowerCamelCase_			:							List[Any]      =       [train_info, test_info]
							lowerCamelCase_			:							Optional[Any]      =       SplitDict()
							split_dict.add(__SCREAMING_SNAKE_CASE     )
							split_dict.add(__SCREAMING_SNAKE_CASE     )
							lowerCamelCase_			:							Optional[Any]      =       DatasetInfo(splits=__SCREAMING_SNAKE_CASE     )

							with tempfile.TemporaryDirectory() as tmp_dir:
								lowerCamelCase_			:							Dict      =       ReaderTest(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE     )

								lowerCamelCase_			:							List[Any]      =       "test[:33%]"
								lowerCamelCase_			:							str      =       Dataset(**reader.read(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE     )     )
								self.assertEqual(dset["filename"][0]				,							F"""{name}-test"""     )
								self.assertEqual(dset.num_rows				,							33     )
								self.assertEqual(dset.num_columns				,							1     )

								lowerCamelCase_			:							int      =       ["train", "test[:33%]"]
								lowerCamelCase_			:							Optional[int]      =       [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"     )]
								for instructions in [instructionsa, instructionsa]:
									lowerCamelCase_			:							Tuple      =       [reader.read(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE     ) for instr in instructions]
									lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       (Dataset(**__SCREAMING_SNAKE_CASE     ) for dataset_kwargs in datasets_kwargs)
									self.assertEqual(train_dset["filename"][0]				,							F"""{name}-train"""     )
									self.assertEqual(train_dset.num_rows				,							100     )
									self.assertEqual(train_dset.num_columns				,							1     )
									self.assertIsInstance(train_dset.split				,							__SCREAMING_SNAKE_CASE     )
									self.assertEqual(str(train_dset.split     )				,							"train"     )
									self.assertEqual(test_dset["filename"][0]				,							F"""{name}-test"""     )
									self.assertEqual(test_dset.num_rows				,							33     )
									self.assertEqual(test_dset.num_columns				,							1     )
									self.assertIsInstance(test_dset.split				,							__SCREAMING_SNAKE_CASE     )
									self.assertEqual(str(test_dset.split     )				,							"test[:33%]"     )
									del train_dset, test_dset




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[str]      =       "my_name"
							lowerCamelCase_			:							Any      =       SplitInfo(name="train"				,							num_examples=1000				,							shard_lengths=[100] * 10     )
							lowerCamelCase_			:							Dict      =       [train_info]
							lowerCamelCase_			:							Dict      =       SplitDict()
							split_dict.add(__SCREAMING_SNAKE_CASE     )
							lowerCamelCase_			:							int      =       DatasetInfo(splits=__SCREAMING_SNAKE_CASE     )

							with tempfile.TemporaryDirectory() as tmp_dir:
								lowerCamelCase_			:							List[str]      =       ReaderTest(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE     )

								lowerCamelCase_			:							Optional[Any]      =       "train[:33%]"
								lowerCamelCase_			:							Union[str, Any]      =       Dataset(**reader.read(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE     )     )
								self.assertEqual(dset["filename"][0]				,							F"""{name}-train-00000-of-00010"""     )
								self.assertEqual(dset["filename"][-1]				,							F"""{name}-train-00003-of-00010"""     )
								self.assertEqual(dset.num_rows				,							330     )
								self.assertEqual(dset.num_columns				,							1     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Union[str, Any]      =       SplitInfo(name="train"				,							num_examples=100     )
							lowerCamelCase_			:							List[str]      =       SplitInfo(name="test"				,							num_examples=100     )
							lowerCamelCase_			:							Optional[int]      =       SplitDict()
							split_dict.add(__SCREAMING_SNAKE_CASE     )
							split_dict.add(__SCREAMING_SNAKE_CASE     )
							lowerCamelCase_			:							List[Any]      =       DatasetInfo(splits=__SCREAMING_SNAKE_CASE     )

							with tempfile.TemporaryDirectory() as tmp_dir:
								lowerCamelCase_			:							Optional[int]      =       ReaderTest(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE     )

								lowerCamelCase_			:							str      =       [
								    {"filename": os.path.join(__SCREAMING_SNAKE_CASE				,							"train"     )},
								    {"filename": os.path.join(__SCREAMING_SNAKE_CASE				,							"test"     ), "skip": 10, "take": 10},
								]
								lowerCamelCase_			:							Optional[Any]      =       Dataset(**reader.read_files(__SCREAMING_SNAKE_CASE				,							original_instructions="train+test[10:20]"     )     )
								self.assertEqual(dset.num_rows				,							110     )
								self.assertEqual(dset.num_columns				,							1     )
								del dset







@pytest.mark.parametrize("in_memory"      ,					[False, True])
def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		lowerCamelCase_			:							Optional[Any]      =       ArrowReader.read_table(_lowerCamelCase      ,					in_memory=_lowerCamelCase)
	assert table.shape == dataset.data.shape
	assert set(table.column_names) == set(dataset.data.column_names)
	assert dict(table.to_pydict()) == dict(dataset.data.to_pydict())  # to_pydict returns OrderedDict







@pytest.mark.parametrize("in_memory"      ,					[False, True])
def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							str      =       arrow_file
	lowerCamelCase_			:							Any      =       ArrowReader(""      ,					_lowerCamelCase)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		lowerCamelCase_			:							Dict      =       reader.read_files([{"filename": filename}]      ,					in_memory=_lowerCamelCase)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	lowerCamelCase_			:							Any      =       dataset_kwargs["arrow_table"]
	assert table.shape == dataset.data.shape
	assert set(table.column_names) == set(dataset.data.column_names)
	assert dict(table.to_pydict()) == dict(dataset.data.to_pydict())  # to_pydict returns OrderedDict







def     __magic_name__  (      ):
	'''simple docstring'''


	assert ReadInstruction("train"      ,					to=10      ,					unit="abs").to_spec() == "train[:10]"
	assert ReadInstruction("train"      ,					from_=-80      ,					to=10      ,					unit="%").to_spec() == "train[-80%:10%]"

	lowerCamelCase_			:							Dict      =       "train+test"
	assert ReadInstruction.from_spec(_lowerCamelCase).to_spec() == spec_train_test

	lowerCamelCase_			:							List[str]      =       "train[2:10]"
	assert ReadInstruction.from_spec(_lowerCamelCase).to_spec() == spec_train_abs

	lowerCamelCase_			:							str      =       "train[15%:-20%]"
	assert ReadInstruction.from_spec(_lowerCamelCase).to_spec() == spec_train_pct

	lowerCamelCase_			:							Optional[int]      =       "train[:10%](closest)"
	assert ReadInstruction.from_spec(_lowerCamelCase).to_spec() == "train[:10%]"

	lowerCamelCase_			:							Union[str, Any]      =       "train[:10%](pct1_dropremainder)"
	assert ReadInstruction.from_spec(_lowerCamelCase).to_spec() == spec_train_pct_rounding

	lowerCamelCase_			:							Dict      =       "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
	assert ReadInstruction.from_spec(_lowerCamelCase).to_spec() == spec_train_test_pct_rounding







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[int]      =       "dummy"
	lowerCamelCase_			:							List[Any]      =       [SplitInfo(name="train"      ,					num_examples=100)]
	lowerCamelCase_			:							str      =       "train[:33%]"
	lowerCamelCase_			:							Optional[Any]      =       "arrow"
	lowerCamelCase_			:							Dict      =       "prefix"

	lowerCamelCase_			:							List[str]      =       make_file_instructions(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase)
	assert isinstance(_lowerCamelCase      ,					_lowerCamelCase)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(_lowerCamelCase      ,					F"""{name}-train.arrow"""), "skip": 0, "take": 33}
	]

	lowerCamelCase_			:							List[Any]      =       [SplitInfo(name="train"      ,					num_examples=100      ,					shard_lengths=[10] * 10)]
	lowerCamelCase_			:							Union[str, Any]      =       make_file_instructions(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase)
	assert isinstance(_lowerCamelCase      ,					_lowerCamelCase)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(_lowerCamelCase      ,					F"""{name}-train-00000-of-00010.arrow"""), "skip": 0, "take": -1},
	    {"filename": os.path.join(_lowerCamelCase      ,					F"""{name}-train-00001-of-00010.arrow"""), "skip": 0, "take": -1},
	    {"filename": os.path.join(_lowerCamelCase      ,					F"""{name}-train-00002-of-00010.arrow"""), "skip": 0, "take": -1},
	    {"filename": os.path.join(_lowerCamelCase      ,					F"""{name}-train-00003-of-00010.arrow"""), "skip": 0, "take": 3},
	]







@pytest.mark.parametrize("name, expected_exception"      ,					[(None, TypeError), ("", ValueError)])
def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Optional[int]      =       [SplitInfo(name="train"      ,					num_examples=100)]
	lowerCamelCase_			:							List[str]      =       "train"
	lowerCamelCase_			:							int      =       "arrow"
	lowerCamelCase_			:							str      =       "prefix_path"
	with pytest.raises(_lowerCamelCase):
		lowerCamelCase_			:							int      =       make_file_instructions(_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase)





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__magic_name__       					=      logging.getLogger(__name__)





class    lowerCAmelCase__				:






						"""simple docstring"""




						def __init__(   self     ):
							lowerCamelCase_			:							List[Any]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							if not self.initialized:
								lowerCamelCase_			:							Optional[int]      =       RagRetriever(
								    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)
								lowerCamelCase_			:							Any      =       True




						def 					_UpperCamelCase			(   self     ):
							self.retriever.index.init_index()




						def 					_UpperCamelCase			(   self     ):
							# delete the old self.retriever object before assigning the new index
							del self.retriever
							lowerCamelCase_			:							Optional[int]      =       False




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							lowerCamelCase_   ,lowerCamelCase_			:							List[Any]      =       self.retriever._main_retrieve(a_				,							a_     )
							lowerCamelCase_			:							str      =       self.retriever.index.get_doc_dicts(a_     )
							return doc_ids, retrieved_doc_embeds, doc_dicts





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_				,							a_				,							a_				,							a_=None     ):
							if index is not None and index.is_initialized() and len(a_     ) > 0:
								raise ValueError(
								    "When using Ray for distributed fine-tuning, "
								    "you'll need to provide the paths instead, "
								    "as the dataset and the index are loaded "
								    "separately. More info in examples/rag/use_own_knowledge_dataset.py "     )

							super().__init__(
							    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							index=a_				,							init_retrieval=a_				,							)

							lowerCamelCase_			:							List[str]      =       retrieval_workers
							lowerCamelCase_			:							str      =       question_encoder_tokenizer
							lowerCamelCase_			:							Optional[int]      =       generator_tokenizer
							if len(self.retrieval_workers     ) > 0:
								ray.get(
								    [
								        worker.create_rag_retriever.remote(a_				,							a_				,							a_				,							a_     )
								        for worker in self.retrieval_workers
								    ]     )




						def 					_UpperCamelCase			(   self     ):
							logger.info("initializing retrieval"     )

							if len(self.retrieval_workers     ) > 0:
								ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]     )
							else:
								# Non-distributed training. Load index into this same process.
								self.index.init_index()




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if len(self.retrieval_workers     ) > 0:
								# Select a random retrieval actor.
								lowerCamelCase_			:							List[Any]      =       self.retrieval_workers[random.randint(0				,							len(self.retrieval_workers     ) - 1     )]
								lowerCamelCase_   ,lowerCamelCase_   ,lowerCamelCase_			:							str      =       ray.get(
								    random_worker.retrieve.remote(a_				,							a_     )     )
							else:
								lowerCamelCase_   ,lowerCamelCase_			:							Optional[int]      =       self._main_retrieve(a_				,							a_     )
								lowerCamelCase_			:							Any      =       self.index.get_doc_dicts(a_     )
							return retrieved_doc_embeds, doc_ids, doc_dicts




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_=None				,							**a_     ):
							return super(a_				,							cls     ).get_tokenizers(a_				,							a_				,							**a_     )




						@classmethod
						def 					_UpperCamelCase			(   cls				,							a_				,							a_				,							a_=None				,							**a_     ):
							lowerCamelCase_			:							str      =       kwargs.pop("config"				,							a_     ) or RagConfig.from_pretrained(a_				,							**a_     )
							lowerCamelCase_			:							Tuple      =       RagTokenizer.from_pretrained(a_				,							config=a_     )
							lowerCamelCase_			:							str      =       rag_tokenizer.question_encoder
							lowerCamelCase_			:							int      =       rag_tokenizer.generator

							if indexed_dataset is not None:
								lowerCamelCase_			:							int      =       "custom"
								lowerCamelCase_			:							List[str]      =       CustomHFIndex(config.retrieval_vector_size				,							a_     )
							else:
								lowerCamelCase_			:							str      =       cls._build_index(a_     )

							return cls(
							    a_				,							question_encoder_tokenizer=a_				,							generator_tokenizer=a_				,							retrieval_workers=a_				,							index=a_				,							)




						def 					_UpperCamelCase			(   self     ):
							logger.info("re-loading the new dataset with embeddings"     )
							# access from the training loop

							ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]     )

							# build the index object again
							lowerCamelCase_			:							Optional[int]      =       self._build_index(self.config     )

							ray.get(
							    [
							        worker.create_rag_retriever.remote(
							            self.config				,							self.question_encoder_tokenizer				,							self.generator_tokenizer				,							a_     )
							        for worker in self.retrieval_workers
							    ]     )

