




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      a__       (   snake_case__ ):






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A=False     ):




											"""simple docstring"""



											__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			(
											    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()
											__lowerCAmelCase							  =			pa.Table.from_pydict({"filename": [Path(_A     ).name] * 1_0_0}     )
											if take == -1:
																__lowerCAmelCase							  =			len(_A     ) - skip
											if skip is not None and take is not None:
																__lowerCAmelCase							  =			pa_table.slice(_A						,     _A     )
											return pa_table






class      a__       (   snake_case__ ):






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			"my_name"
											__lowerCAmelCase							  =			SplitInfo(name="train"						,     num_examples=1_0_0     )
											__lowerCAmelCase							  =			SplitInfo(name="test"						,     num_examples=1_0_0     )
											__lowerCAmelCase							  =			[train_info, test_info]
											__lowerCAmelCase							  =			SplitDict()
											split_dict.add(_A     )
											split_dict.add(_A     )
											__lowerCAmelCase							  =			DatasetInfo(splits=_A     )

											with tempfile.TemporaryDirectory() as tmp_dir:
																__lowerCAmelCase							  =			ReaderTest(_A						,     _A     )

																__lowerCAmelCase							  =			"test[:33%]"
																__lowerCAmelCase							  =			Dataset(**reader.read(_A						,     _A						,     _A     )     )
																self.assertEqual(dset["filename"][0]						,     f"""{name}-test"""     )
																self.assertEqual(dset.num_rows						,     3_3     )
																self.assertEqual(dset.num_columns						,     1     )

																__lowerCAmelCase							  =			["train", "test[:33%]"]
																__lowerCAmelCase							  =			[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"     )]
																for instructions in [instructionsa, instructionsa]:
																					__lowerCAmelCase							  =			[reader.read(_A						,     _A						,     _A     ) for instr in instructions]
																					__lowerCAmelCase  ,				__lowerCAmelCase							  =			(Dataset(**_A     ) for dataset_kwargs in datasets_kwargs)
																					self.assertEqual(train_dset["filename"][0]						,     f"""{name}-train"""     )
																					self.assertEqual(train_dset.num_rows						,     1_0_0     )
																					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						,     3_3     )
																					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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			"my_name"
											__lowerCAmelCase							  =			SplitInfo(name="train"						,     num_examples=1_0_0_0						,     shard_lengths=[1_0_0] * 1_0     )
											__lowerCAmelCase							  =			[train_info]
											__lowerCAmelCase							  =			SplitDict()
											split_dict.add(_A     )
											__lowerCAmelCase							  =			DatasetInfo(splits=_A     )

											with tempfile.TemporaryDirectory() as tmp_dir:
																__lowerCAmelCase							  =			ReaderTest(_A						,     _A     )

																__lowerCAmelCase							  =			"train[:33%]"
																__lowerCAmelCase							  =			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						,     3_3_0     )
																self.assertEqual(dset.num_columns						,     1     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			SplitInfo(name="train"						,     num_examples=1_0_0     )
											__lowerCAmelCase							  =			SplitInfo(name="test"						,     num_examples=1_0_0     )
											__lowerCAmelCase							  =			SplitDict()
											split_dict.add(_A     )
											split_dict.add(_A     )
											__lowerCAmelCase							  =			DatasetInfo(splits=_A     )

											with tempfile.TemporaryDirectory() as tmp_dir:
																__lowerCAmelCase							  =			ReaderTest(_A						,     _A     )

																__lowerCAmelCase							  =			[
																    {"filename": os.path.join(_A						,     "train"     )},
																    {"filename": os.path.join(_A						,     "test"     ), "skip": 1_0, "take": 1_0},
																]
																__lowerCAmelCase							  =			Dataset(**reader.read_files(_A						,     original_instructions="train+test[10:20]"     )     )
																self.assertEqual(dset.num_rows						,     1_1_0     )
																self.assertEqual(dset.num_columns						,     1     )
																del dset







@pytest.mark.parametrize("in_memory" , [False, True]	)
def       _a (		SCREAMING_SNAKE_CASE_				:							Tuple , SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							List[str]	):
					__lowerCAmelCase							  =			arrow_file
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase							  =			ArrowReader.read_table(SCREAMING_SNAKE_CASE_ , in_memory=SCREAMING_SNAKE_CASE_	)
					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 (		SCREAMING_SNAKE_CASE_				:							List[Any] , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Any	):
					__lowerCAmelCase							  =			arrow_file
					__lowerCAmelCase							  =			ArrowReader("" , SCREAMING_SNAKE_CASE_	)
					with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase							  =			reader.read_files([{"filename": filename}] , in_memory=SCREAMING_SNAKE_CASE_	)
					assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
					__lowerCAmelCase							  =			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 (		):
					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							  =			"train+test"
					assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE_	).to_spec() == spec_train_test

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

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

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

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

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







def       _a (		):
					__lowerCAmelCase							  =			"dummy"
					__lowerCAmelCase							  =			[SplitInfo(name="train" , num_examples=1_00	)]
					__lowerCAmelCase							  =			"train[:33%]"
					__lowerCAmelCase							  =			"arrow"
					__lowerCAmelCase							  =			"prefix"

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

					__lowerCAmelCase							  =			[SplitInfo(name="train" , num_examples=1_00 , shard_lengths=[10] * 10	)]
					__lowerCAmelCase							  =			make_file_instructions(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					assert file_instructions.num_examples == 33
					assert file_instructions.file_instructions == [
					    {"filename": os.path.join(SCREAMING_SNAKE_CASE_ , F"""{name}-train-00000-of-00010.arrow"""	), "skip": 0, "take": -1},
					    {"filename": os.path.join(SCREAMING_SNAKE_CASE_ , F"""{name}-train-00001-of-00010.arrow"""	), "skip": 0, "take": -1},
					    {"filename": os.path.join(SCREAMING_SNAKE_CASE_ , F"""{name}-train-00002-of-00010.arrow"""	), "skip": 0, "take": -1},
					    {"filename": os.path.join(SCREAMING_SNAKE_CASE_ , F"""{name}-train-00003-of-00010.arrow"""	), "skip": 0, "take": 3},
					]







@pytest.mark.parametrize("name, expected_exception" , [(None, TypeError), ("", ValueError)]	)
def       _a (		SCREAMING_SNAKE_CASE_				:							Tuple , SCREAMING_SNAKE_CASE_				:							Tuple	):
					__lowerCAmelCase							  =			[SplitInfo(name="train" , num_examples=1_00	)]
					__lowerCAmelCase							  =			"train"
					__lowerCAmelCase							  =			"arrow"
					__lowerCAmelCase							  =			"prefix_path"
					with pytest.raises(SCREAMING_SNAKE_CASE_	):
										__lowerCAmelCase							  =			make_file_instructions(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)










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

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCamelCase__            =     logging.get_logger(__name__)  # pylint: disable=invalid-name
class      a__       (   snake_case__ ):
						_a				:       Tuple   =       ["""safety_checker""", """feature_extractor"""]






						def __init__(  self						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A = True						,     ):




											"""simple docstring"""



											super().__init__()
											if hasattr(scheduler.config						,     "steps_offset"     ) and scheduler.config.steps_offset != 1:
																__lowerCAmelCase							  =			(
																    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
																    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
																    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
																    " file"
																)
																deprecate("steps_offset!=1"						,     "1.0.0"						,     _A						,     standard_warn=_A     )
																__lowerCAmelCase							  =			dict(scheduler.config     )
																__lowerCAmelCase							  =			1
																__lowerCAmelCase							  =			FrozenDict(_A     )

											self.register_modules(
											    vae=_A						,     text_encoder=_A						,     tokenizer=_A						,     unet=_A						,     scheduler=_A						,     safety_checker=_A						,     feature_extractor=_A						,     )
											self.register_to_config(requires_safety_checker=_A     )






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return {k: getattr(self						,     _A     ) for k in self.config.keys() if not k.startswith("_"     )}






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A = "auto"     ):




											"""simple docstring"""



											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowerCAmelCase							  =			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											self.enable_attention_slicing(_A     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionInpaintPipelineLegacy(**self.components     )(
											    prompt=_A						,     image=_A						,     mask_image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     **_A						,     ):




											"""simple docstring"""



											return StableDiffusionImgaImgPipeline(**self.components     )(
											    prompt=_A						,     image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = 5_1_2						,     _A = 5_1_2						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionPipeline(**self.components     )(
											    prompt=_A						,     height=_A						,     width=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     latents=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )




