
import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def 						UpperCAmelCase      (     a_		,       a_						)		->     str:







				"""simple docstring"""



				assert isinstance(lowerCAmelCase__		,       lowerCAmelCase__						)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"		,       [False, True]						)
def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     List[str]:







				"""simple docstring"""



				__A             =			tmp_path / "cache"
				__A             =			{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__A             =			JsonDatasetReader(lowerCAmelCase__		,       cache_dir=lowerCAmelCase__		,       keep_in_memory=lowerCAmelCase__						).read()
				_check_json_dataset(lowerCAmelCase__		,       lowerCAmelCase__						)


@pytest.mark.parametrize(
    "features"		,       [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]		,       )
def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Any:







				"""simple docstring"""



				__A             =			tmp_path / "cache"
				__A             =			{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				__A             =			features.copy() if features else default_expected_features
				__A             =			(
				    Features({feature: Value(lowerCAmelCase__						) for feature, dtype in features.items()}						) if features is not None else None
				)
				__A             =			JsonDatasetReader(lowerCAmelCase__		,       features=lowerCAmelCase__		,       cache_dir=lowerCAmelCase__						).read()
				_check_json_dataset(lowerCAmelCase__		,       lowerCAmelCase__						)


@pytest.mark.parametrize(
    "features"		,       [
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]		,       )
def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Optional[Any]:







				"""simple docstring"""



				__A             =			tmp_path / "cache"
				__A             =			{"col_3": "float64", "col_1": "string", "col_2": "int64"}
				__A             =			features.copy() if features else default_expected_features
				__A             =			(
				    Features({feature: Value(lowerCAmelCase__						) for feature, dtype in features.items()}						) if features is not None else None
				)
				__A             =			JsonDatasetReader(lowerCAmelCase__		,       features=lowerCAmelCase__		,       cache_dir=lowerCAmelCase__						).read()
				assert isinstance(lowerCAmelCase__		,       lowerCAmelCase__						)
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_3", "col_1", "col_2"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype


def 						UpperCAmelCase      (     a_		,       a_						)		->     str:







				"""simple docstring"""



				__A             =			{"col_2": "int64", "col_3": "float64", "col_1": "string"}
				__A             =			features.copy()
				__A             =			(
				    Features({feature: Value(lowerCAmelCase__						) for feature, dtype in features.items()}						) if features is not None else None
				)
				__A             =			tmp_path / "cache"
				__A             =			JsonDatasetReader(lowerCAmelCase__		,       features=lowerCAmelCase__		,       cache_dir=lowerCAmelCase__						).read()
				assert isinstance(lowerCAmelCase__		,       lowerCAmelCase__						)
				assert dataset.num_rows == 2
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_2", "col_3", "col_1"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("split"		,       [None, NamedSplit("train"						), "train", "test"]						)
def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Any:







				"""simple docstring"""



				__A             =			tmp_path / "cache"
				__A             =			{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				__A             =			JsonDatasetReader(lowerCAmelCase__		,       cache_dir=lowerCAmelCase__		,       split=lowerCAmelCase__						).read()
				_check_json_dataset(lowerCAmelCase__		,       lowerCAmelCase__						)
				assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"		,       [str, list]						)
def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     List[Any]:







				"""simple docstring"""



				if issubclass(lowerCAmelCase__		,       lowerCAmelCase__						):
								__A             =			jsonl_path
				elif issubclass(lowerCAmelCase__		,       lowerCAmelCase__						):
								__A             =			[jsonl_path]
				__A             =			tmp_path / "cache"
				__A             =			{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				__A             =			JsonDatasetReader(lowerCAmelCase__		,       cache_dir=lowerCAmelCase__						).read()
				_check_json_dataset(lowerCAmelCase__		,       lowerCAmelCase__						)


def 						UpperCAmelCase      (     a_		,       a_		,       a_=("train",)						)		->     Tuple:







				"""simple docstring"""



				assert isinstance(lowerCAmelCase__		,       lowerCAmelCase__						)
				for split in splits:
								__A             =			dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"		,       [False, True]						)
def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     str:







				"""simple docstring"""



				__A             =			tmp_path / "cache"
				__A             =			{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								__A             =			JsonDatasetReader({"train": jsonl_path}		,       cache_dir=lowerCAmelCase__		,       keep_in_memory=lowerCAmelCase__						).read()
				_check_json_datasetdict(lowerCAmelCase__		,       lowerCAmelCase__						)


@pytest.mark.parametrize(
    "features"		,       [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]		,       )
def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     List[str]:







				"""simple docstring"""



				__A             =			tmp_path / "cache"
				__A             =			{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				__A             =			features.copy() if features else default_expected_features
				__A             =			(
				    Features({feature: Value(lowerCAmelCase__						) for feature, dtype in features.items()}						) if features is not None else None
				)
				__A             =			JsonDatasetReader({"train": jsonl_path}		,       features=lowerCAmelCase__		,       cache_dir=lowerCAmelCase__						).read()
				_check_json_datasetdict(lowerCAmelCase__		,       lowerCAmelCase__						)


@pytest.mark.parametrize("split"		,       [None, NamedSplit("train"						), "train", "test"]						)
def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Optional[Any]:







				"""simple docstring"""



				if split:
								__A             =			{split: jsonl_path}
				else:
								__A             =			"train"
								__A             =			{"train": jsonl_path, "test": jsonl_path}
				__A             =			tmp_path / "cache"
				__A             =			{"col_1": "string", "col_2": "int64", "col_3": "float64"}
				__A             =			JsonDatasetReader(lowerCAmelCase__		,       cache_dir=lowerCAmelCase__						).read()
				_check_json_datasetdict(lowerCAmelCase__		,       lowerCAmelCase__		,       splits=list(path.keys()						)						)
				assert all(dataset[split].split == split for split in path.keys()						)


def 						UpperCAmelCase      (     a_						)		->     Optional[Any]:







				"""simple docstring"""



				return json.load(lowerCAmelCase__						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				return [json.loads(lowerCAmelCase__						) for line in buffer]





class 							UpperCAmelCase				:




						'''simple docstring'''





						@pytest.mark.parametrize("lines, load_json_function"							,[(True, load_json_lines), (False, load_json)]			)
						def     UpperCamelCase_  (	self :			Tuple							,A :			str							,A :			List[Any]							,A :			Optional[Any]			):



										with io.BytesIO() as buffer:
														JsonDatasetWriter(__lowerCAmelCase							,__lowerCAmelCase							,lines=__lowerCAmelCase			).write()
														buffer.seek(0			)
														__A             =			load_json_function(__lowerCAmelCase			)
										assert isinstance(__lowerCAmelCase							,__lowerCAmelCase			)
										assert isinstance(exported_content[0]							,__lowerCAmelCase			)
										assert len(__lowerCAmelCase			) == 10






						@pytest.mark.parametrize(
						    "orient, container, keys, len_at"							,[
						        ("records", list, {"tokens", "labels", "answers", "id"}, None),
						        ("split", dict, {"columns", "data"}, "data"),
						        ("index", dict, set("0123456789"			), None),
						        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
						        ("values", list, None, None),
						        ("table", dict, {"schema", "data"}, "data"),
						    ]							,)
						def     UpperCamelCase_  (	self :			Optional[int]							,A :			List[str]							,A :			List[Any]							,A :			Union[str, Any]							,A :			Union[str, Any]							,A :			Any			):



										with io.BytesIO() as buffer:
														JsonDatasetWriter(__lowerCAmelCase							,__lowerCAmelCase							,lines=__lowerCAmelCase							,orient=__lowerCAmelCase			).write()
														buffer.seek(0			)
														__A             =			load_json(__lowerCAmelCase			)
										assert isinstance(__lowerCAmelCase							,__lowerCAmelCase			)
										if keys:
														if container is dict:
																		assert exported_content.keys() == keys
														else:
																		assert exported_content[0].keys() == keys
										else:
														assert not hasattr(__lowerCAmelCase							,"keys"			) and not hasattr(exported_content[0]							,"keys"			)
										if len_at:
														assert len(exported_content[len_at]			) == 10
										else:
														assert len(__lowerCAmelCase			) == 10






						@pytest.mark.parametrize("lines, load_json_function"							,[(True, load_json_lines), (False, load_json)]			)
						def     UpperCamelCase_  (	self :			List[Any]							,A :			List[str]							,A :			Optional[Any]							,A :			Optional[int]			):



										with io.BytesIO() as buffer:
														JsonDatasetWriter(__lowerCAmelCase							,__lowerCAmelCase							,lines=__lowerCAmelCase							,num_proc=2			).write()
														buffer.seek(0			)
														__A             =			load_json_function(__lowerCAmelCase			)
										assert isinstance(__lowerCAmelCase							,__lowerCAmelCase			)
										assert isinstance(exported_content[0]							,__lowerCAmelCase			)
										assert len(__lowerCAmelCase			) == 10






						@pytest.mark.parametrize(
						    "orient, container, keys, len_at"							,[
						        ("records", list, {"tokens", "labels", "answers", "id"}, None),
						        ("split", dict, {"columns", "data"}, "data"),
						        ("index", dict, set("0123456789"			), None),
						        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
						        ("values", list, None, None),
						        ("table", dict, {"schema", "data"}, "data"),
						    ]							,)
						def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[str]							,A :			int							,A :			int							,A :			Dict							,A :			str			):



										with io.BytesIO() as buffer:
														JsonDatasetWriter(__lowerCAmelCase							,__lowerCAmelCase							,lines=__lowerCAmelCase							,orient=__lowerCAmelCase							,num_proc=2			).write()
														buffer.seek(0			)
														__A             =			load_json(__lowerCAmelCase			)
										assert isinstance(__lowerCAmelCase							,__lowerCAmelCase			)
										if keys:
														if container is dict:
																		assert exported_content.keys() == keys
														else:
																		assert exported_content[0].keys() == keys
										else:
														assert not hasattr(__lowerCAmelCase							,"keys"			) and not hasattr(exported_content[0]							,"keys"			)
										if len_at:
														assert len(exported_content[len_at]			) == 10
										else:
														assert len(__lowerCAmelCase			) == 10






						def     UpperCamelCase_  (	self :			List[Any]							,A :			Dict			):



										with pytest.raises(__lowerCAmelCase			):
														with io.BytesIO() as buffer:
																		JsonDatasetWriter(__lowerCAmelCase							,__lowerCAmelCase							,num_proc=0			)







						@pytest.mark.parametrize("compression, extension"							,[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]			)
						def     UpperCamelCase_  (	self :			int							,A :			Dict							,A :			List[str]							,A :			int							,A :			int							,A :			Optional[int]			):
										__A             =			tmp_path_factory.mktemp("data"			) / f'''test.json.{extension}'''
										__A             =			str(shared_datadir / f'''test_file.json.{extension}'''			)
										JsonDatasetWriter(__lowerCAmelCase							,__lowerCAmelCase							,compression=__lowerCAmelCase			).write()

										with fsspec.open(__lowerCAmelCase							,"rb"							,compression="infer"			) as f:
														__A             =			f.read()
										with fsspec.open(__lowerCAmelCase							,"rb"							,compression="infer"			) as f:
														__A             =			f.read()
										assert exported_content == original_content


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

					# set step values
					self.scheduler.set_timesteps(A			)

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
