
import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


SCREAMING_SNAKE_CASE							:Union[str, Any]										=    [
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]


def 						UpperCAmelCase      (     a_=True						)		->     Union[str, Any]:







				"""simple docstring"""



				if with_config:
								return [
								    {
								        "testcase_name": d["dataset"] + "/" + d["config_name"],
								        "dataset": d["dataset"],
								        "config_name": d["config_name"],
								    }
								    for d in DATASETS_ON_HF_GCP
								]
				else:
								return [
								    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
								]





@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=A_  )  )
class 							UpperCAmelCase				(  A_  ):




						'''simple docstring'''





						snake_case_    =					None
						snake_case_    =					None






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


										with TemporaryDirectory() as tmp_dir:
														__A             =			dataset_module_factory(snake_case__							,cache_dir=snake_case__			)

														__A             =			import_main_class(dataset_module.module_path							,dataset=snake_case__			)

														__A             =			builder_cls(
														    cache_dir=snake_case__							,config_name=snake_case__							,hash=dataset_module.hash							,)

														__A             =			"/".join(
														    [
														        HF_GCP_BASE_URL,
														        builder_instance._relative_data_dir(with_hash=snake_case__			).replace(os.sep							,"/"			),
														        config.DATASET_INFO_FILENAME,
														    ]			)
														__A             =			cached_path(snake_case__							,cache_dir=snake_case__			)
														self.assertTrue(os.path.exists(snake_case__			)			)





@pytest.mark.integration
def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			tmp_path_factory.mktemp("test_hf_gcp"						) / "test_wikipedia_simple"
				__A             =			dataset_module_factory("wikipedia"		,       cache_dir=__lowerCamelCase						)
				__A             =			import_main_class(dataset_module.module_path						)
				__A             =			builder_cls(
				    cache_dir=__lowerCamelCase		,       config_name="20220301.frr"		,       hash=dataset_module.hash		,       )
				# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
				__A             =			None
				builder_instance.download_and_prepare()
				__A             =			builder_instance.as_dataset()
				assert ds


@pytest.mark.integration
def 						UpperCAmelCase      (     a_						)		->     str:







				"""simple docstring"""



				__A             =			dataset_module_factory("wikipedia"		,       cache_dir=__lowerCamelCase						)
				__A             =			import_main_class(dataset_module.module_path		,       dataset=__lowerCamelCase						)
				__A             =			builder_cls(
				    cache_dir=__lowerCamelCase		,       config_name="20220301.frr"		,       hash=dataset_module.hash		,       )
				__A             =			builder_instance.as_streaming_dataset()
				assert ds
				assert isinstance(__lowerCamelCase		,       __lowerCamelCase						)
				assert "train" in ds
				assert isinstance(ds["train"]		,       __lowerCamelCase						)
				assert next(iter(ds["train"]						)						)


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			)
