



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 	__snake_case (				lowerCamelCase_):







				'''simple docstring'''






				def _a (							self					,					a_					,					a_=False						):
								a__			 =						(
								    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(a_						)					,					"""wb"""						).close()
								a__			 =						pa.Table.from_pydict({"""filename""": [Path(a_						).name] * 100}						)
								if take == -1:
												a__			 =						len(a_						) - skip
								if skip is not None and take is not None:
												a__			 =						pa_table.slice(a_					,					a_						)
								return pa_table




class 	__snake_case (				lowerCamelCase_):







				'''simple docstring'''






				def _a (							self						):
								a__			 =						"my_name"
								a__			 =						SplitInfo(name="""train"""					,					num_examples=100						)
								a__			 =						SplitInfo(name="""test"""					,					num_examples=100						)
								a__			 =						[train_info, test_info]
								a__			 =						SplitDict()
								split_dict.add(a_						)
								split_dict.add(a_						)
								a__			 =						DatasetInfo(splits=a_						)

								with tempfile.TemporaryDirectory() as tmp_dir:
												a__			 =						ReaderTest(a_					,					a_						)

												a__			 =						"test[:33%]"
												a__			 =						Dataset(**reader.read(a_					,					a_					,					a_						)						)
												self.assertEqual(dset["""filename"""][0]					,					F'''{name}-test'''						)
												self.assertEqual(dset.num_rows					,					33						)
												self.assertEqual(dset.num_columns					,					1						)

												a__			 =						["train", "test[:33%]"]
												a__			 =						[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""						)]
												for instructions in [instructionsa, instructionsa]:
																a__			 =						[reader.read(a_					,					a_					,					a_						) for instr in instructions]
																a__			 =						(Dataset(**a_						) 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					,					a_						)
																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					,					a_						)
																self.assertEqual(str(test_dset.split						)					,					"""test[:33%]"""						)
																del train_dset, test_dset


				def _a (							self						):
								a__			 =						"my_name"
								a__			 =						SplitInfo(name="""train"""					,					num_examples=1_000					,					shard_lengths=[100] * 10						)
								a__			 =						[train_info]
								a__			 =						SplitDict()
								split_dict.add(a_						)
								a__			 =						DatasetInfo(splits=a_						)

								with tempfile.TemporaryDirectory() as tmp_dir:
												a__			 =						ReaderTest(a_					,					a_						)

												a__			 =						"train[:33%]"
												a__			 =						Dataset(**reader.read(a_					,					a_					,					a_						)						)
												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 _a (							self						):
								a__			 =						SplitInfo(name="""train"""					,					num_examples=100						)
								a__			 =						SplitInfo(name="""test"""					,					num_examples=100						)
								a__			 =						SplitDict()
								split_dict.add(a_						)
								split_dict.add(a_						)
								a__			 =						DatasetInfo(splits=a_						)

								with tempfile.TemporaryDirectory() as tmp_dir:
												a__			 =						ReaderTest(a_					,					a_						)

												a__			 =						[
												    {"filename": os.path.join(a_					,					"""train"""						)},
												    {"filename": os.path.join(a_					,					"""test"""						), "skip": 10, "take": 10},
												]
												a__			 =						Dataset(**reader.read_files(a_					,					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      A_ (		__a       :			List[Any]	,						__a       :			List[str]	,						__a       :			Optional[int]						):

				"""simple docstring"""
				a__			 =						arrow_file
				with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
								a__			 =						ArrowReader.read_table(lowercase__	,						in_memory=lowercase__						)
				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      A_ (		__a       :			Dict	,						__a       :			Any	,						__a       :			Any						):

				"""simple docstring"""
				a__			 =						arrow_file
				a__			 =						ArrowReader(""""""	,						lowercase__						)
				with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
								a__			 =						reader.read_files([{"""filename""": filename}]	,						in_memory=lowercase__						)
				assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
				a__			 =						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      A_ (		):

				"""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%]"

				a__			 =						"train+test"
				assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_test

				a__			 =						"train[2:10]"
				assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_abs

				a__			 =						"train[15%:-20%]"
				assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_pct

				a__			 =						"train[:10%](closest)"
				assert ReadInstruction.from_spec(lowercase__						).to_spec() == "train[:10%]"

				a__			 =						"train[:10%](pct1_dropremainder)"
				assert ReadInstruction.from_spec(lowercase__						).to_spec() == spec_train_pct_rounding

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

def      A_ (		):

				"""simple docstring"""
				a__			 =						"dummy"
				a__			 =						[SplitInfo(name="""train"""	,						num_examples=100						)]
				a__			 =						"train[:33%]"
				a__			 =						"arrow"
				a__			 =						"prefix"

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

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




@pytest.mark.parametrize("""name, expected_exception"""	,						[(None, TypeError), ("""""", ValueError)]						)
def      A_ (		__a       :			int	,						__a       :			int						):

				"""simple docstring"""
				a__			 =						[SplitInfo(name="""train"""	,						num_examples=100						)]
				a__			 =						"train"
				a__			 =						"arrow"
				a__			 =						"prefix_path"
				with pytest.raises(lowercase__						):
								a__			 =						make_file_instructions(lowercase__	,						lowercase__	,						lowercase__	,						lowercase__	,						lowercase__						)






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							def _a (							self						):
											a__			 =						tempfile.mkdtemp()

											a__			 =						CLIPImageProcessor()
											a__			 =						BertTokenizer.from_pretrained(
											    """hf-internal-testing/tiny-random-BertModel"""					,					model_input_names=["""input_ids""", """attention_mask"""]						)

											a__			 =						GitProcessor(a_					,					a_						)

											processor.save_pretrained(self.tmpdirname						)


							def _a (							self					,					**a_						):
											return AutoProcessor.from_pretrained(self.tmpdirname					,					**a_						).tokenizer


							def _a (							self					,					**a_						):
											return AutoProcessor.from_pretrained(self.tmpdirname					,					**a_						).image_processor


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


							def _a (							self						):
											a__			 =						[np.random.randint(255					,					size=(3, 30, 400)					,					dtype=np.uinta						)]

											a__			 =						[Image.fromarray(np.moveaxis(a_					,					0					,					-1						)						) for x in image_inputs]

											return image_inputs


							def _a (							self						):
											a__			 =						GitProcessor(tokenizer=self.get_tokenizer()					,					image_processor=self.get_image_processor()						)
											processor.save_pretrained(self.tmpdirname						)

											a__			 =						self.get_tokenizer(bos_token="""(BOS)"""					,					eos_token="""(EOS)"""						)
											a__			 =						self.get_image_processor(do_normalize=a_					,					padding_value=1.0						)

											a__			 =						GitProcessor.from_pretrained(
											    self.tmpdirname					,					bos_token="""(BOS)"""					,					eos_token="""(EOS)"""					,					do_normalize=a_					,					padding_value=1.0						)

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

											self.assertEqual(processor.image_processor.to_json_string()					,					image_processor_add_kwargs.to_json_string()						)
											self.assertIsInstance(processor.image_processor					,					a_						)


							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						self.prepare_image_inputs()

											a__			 =						image_processor(a_					,					return_tensors="""np"""						)
											a__			 =						processor(images=a_					,					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 _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						"""lower newer"""

											a__			 =						processor(text=a_						)

											a__			 =						tokenizer(a_					,					return_token_type_ids=a_						)

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


							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						"""lower newer"""
											a__			 =						self.prepare_image_inputs()

											a__			 =						processor(text=a_					,					images=a_						)

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

											# test if it raises when no input is passed
											with pytest.raises(a_						):
															processor()


							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											a__			 =						processor.batch_decode(a_						)
											a__			 =						tokenizer.batch_decode(a_						)

											self.assertListEqual(a_					,					a_						)







							def _a (							self						):
											a__			 =						self.get_image_processor()
											a__			 =						self.get_tokenizer()

											a__			 =						GitProcessor(tokenizer=a_					,					image_processor=a_						)

											a__			 =						"""lower newer"""
											a__			 =						self.prepare_image_inputs()

											a__			 =						processor(text=a_					,					images=a_						)

											# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
											self.assertListEqual(list(inputs.keys()						)					,					["""input_ids""", """attention_mask""", """pixel_values"""]						)

