


"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						str:
						SCREAMING_SNAKE_CASE													=					OmegaConf.load(SCREAMING_SNAKE_CASE_  )
						if display:
												print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_  )  )  )
						return config


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=None  )   ->						Any:
						if conf_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.yaml'
						SCREAMING_SNAKE_CASE													=					load_config(SCREAMING_SNAKE_CASE_ ,			display=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					VQModel(**config.model.params  )
						if ckpt_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.pt'
						SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location=SCREAMING_SNAKE_CASE_  )
						if ".ckpt" in ckpt_path:
												SCREAMING_SNAKE_CASE													=					sd['state_dict']
						model.load_state_dict(SCREAMING_SNAKE_CASE_ ,			strict=SCREAMING_SNAKE_CASE_  )
						model.to(SCREAMING_SNAKE_CASE_  )
						del sd
						return model


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					model.encode(SCREAMING_SNAKE_CASE_  )
						print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'  )
						SCREAMING_SNAKE_CASE													=					model.decode(SCREAMING_SNAKE_CASE_  )
						return xrec


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					string.rsplit('.' ,			1  )
						if reload:
												SCREAMING_SNAKE_CASE													=					importlib.import_module(SCREAMING_SNAKE_CASE_  )
												importlib.reload(SCREAMING_SNAKE_CASE_  )
						return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_ ,			package=SCREAMING_SNAKE_CASE_  ) ,			cls  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any  )   ->						Dict:
						if "target" not in config:
												raise KeyError('Expected key `target` to instantiate.'  )
						return get_obj_from_str(config['target']  )(**config.get('params' ,			{}  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				List[Any]=True ,			SCREAMING_SNAKE_CASE_		:				int=True  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					instantiate_from_config(SCREAMING_SNAKE_CASE_  )
						if sd is not None:
												model.load_state_dict(SCREAMING_SNAKE_CASE_  )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						Any:
						# load the specified checkpoint
						if ckpt:
												SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )
												SCREAMING_SNAKE_CASE													=					pl_sd['global_step']
												print(F'loaded model from global step {global_step}.'  )
						else:
												SCREAMING_SNAKE_CASE													=					{'state_dict': None}
												SCREAMING_SNAKE_CASE													=					None
						SCREAMING_SNAKE_CASE													=					load_model_from_config(config.model ,			pl_sd['state_dict'] ,			gpu=SCREAMING_SNAKE_CASE_ ,			eval_mode=SCREAMING_SNAKE_CASE_  )['model']
						return model, global_step






"""simple docstring"""




import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam
class 					lowerCAmelCase	(					datasets.BeamBasedBuilder    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							Dict:
								return datasets.DatasetInfo(
								    features=datasets.Features({'content': datasets.Value('string'				)}				)						,       supervised_keys=lowerCAmelCase__						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							str:
								return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,       gen_kwargs={'examples': get_test_dummy_examples()}				)]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[int]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__				)





class 					lowerCAmelCase	(					datasets.BeamBasedBuilder    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							int:
								return datasets.DatasetInfo(
								    features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)						,       supervised_keys=lowerCAmelCase__						,       )







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Dict:
								return [
								    datasets.SplitGenerator(name=datasets.Split.TRAIN						,       gen_kwargs={'examples': get_test_nested_examples()}				)
								]







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[Any]:
								import apache_beam as beam

								return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase__				)





def     lowercase			()   ->						str:
						return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar']  )]


def     lowercase			()   ->						Optional[Any]:
						return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar']  )]


class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		@require_beam
		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					len(get_test_dummy_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train.arrow'				)				)				)
														self.assertDictEqual(builder.info.features						,       datasets.Features({'content': datasets.Value('string'				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														self.assertDictEqual(dset['train'][0]						,       get_test_dummy_examples()[0][1]				)
														self.assertDictEqual(
														    dset['train'][expected_num_examples - 1]						,       get_test_dummy_examples()[expected_num_examples - 1][1]				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset







		@require_beam
		def 							__A						(	self				)     ->							int:
								import apache_beam as beam

								SCREAMING_SNAKE_CASE													=					beam.io.parquetio.WriteToParquet

								SCREAMING_SNAKE_CASE													=					len(get_test_dummy_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														with patch('apache_beam.io.parquetio.WriteToParquet'				) as write_parquet_mock:
																				SCREAMING_SNAKE_CASE													=					partial(lowerCAmelCase__						,       num_shards=2				)
																				builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train-00000-of-00002.arrow'				)				)				)
														self.assertTrue(
														    os.path.exists(
														        os.path.join(
														            lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train-00000-of-00002.arrow'				)				)				)
														self.assertDictEqual(builder.info.features						,       datasets.Features({'content': datasets.Value('string'				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														# Order is not preserved when sharding, so we just check that all the elements are there
														self.assertListEqual(sorted(dset['train']['content']				)						,       sorted(['foo', 'bar', 'foobar']				)				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset







		@require_beam
		def 							__A						(	self				)     ->							Optional[Any]:
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					DummyBeamDataset(cache_dir=lowerCAmelCase__				)
														self.assertRaises(datasets.builder.MissingBeamOptions						,       builder.download_and_prepare				)







		@require_beam
		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					len(get_test_nested_examples()				)
								with tempfile.TemporaryDirectory() as tmp_cache_dir:
														SCREAMING_SNAKE_CASE													=					NestedBeamDataset(cache_dir=lowerCAmelCase__						,       beam_runner='DirectRunner'				)
														builder.download_and_prepare()
														self.assertTrue(
														    os.path.exists(
														        os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       F'{builder.name}-train.arrow'				)				)				)
														self.assertDictEqual(
														    builder.info.features						,       datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string'				)}				)}				)				)
														SCREAMING_SNAKE_CASE													=					builder.as_dataset()
														self.assertEqual(dset['train'].num_rows						,       lowerCAmelCase__				)
														self.assertEqual(dset['train'].info.splits['train'].num_examples						,       lowerCAmelCase__				)
														self.assertDictEqual(dset['train'][0]						,       get_test_nested_examples()[0][1]				)
														self.assertDictEqual(
														    dset['train'][expected_num_examples - 1]						,       get_test_nested_examples()[expected_num_examples - 1][1]				)
														self.assertTrue(
														    os.path.exists(os.path.join(lowerCAmelCase__						,       builder.name						,       'default'						,       '0.0.0'						,       'dataset_info.json'				)				)				)
														del dset


