
import importlib

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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__=False    ):
				lowerCamelCase_						=			OmegaConf.load(SCREAMING_SNAKE_CASE_    )
				if display:
								print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_    )    )    )
				return config





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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			model.encode(SCREAMING_SNAKE_CASE_    )
				print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'    )
				lowerCamelCase_						=			model.decode(SCREAMING_SNAKE_CASE_    )
				return xrec





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__=False    ):
				lowerCamelCase_	,    lowerCamelCase_						=			string.rsplit("."	, 1    )
				if reload:
								lowerCamelCase_						=			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 			lowerCamelCase_ (				lowerCamelCase__    ):
				if "target" not in config:
								raise KeyError("Expected key `target` to instantiate."    )
				return get_obj_from_str(config["target"]    )(**config.get("params"	, {}    )    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=True	, lowerCamelCase__=True    ):
				lowerCamelCase_						=			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 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				# load the specified checkpoint
				if ckpt:
								lowerCamelCase_						=			torch.load(SCREAMING_SNAKE_CASE_	, map_location="cpu"    )
								lowerCamelCase_						=			pl_sd["global_step"]
								print(F'loaded model from global step {global_step}.'    )
				else:
								lowerCamelCase_						=			{"state_dict": None}
								lowerCamelCase_						=			None
				lowerCamelCase_						=			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


