




import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


UpperCAmelCase_		:					Optional[Any]          =  datasets.utils.logging.get_logger(__name__)




@dataclass
class 		__A   (      datasets.BuilderConfig      ):
						UpperCamelCase      =       10000
						UpperCamelCase      =       None
						UpperCamelCase      =       None




class 		__A   (      datasets.ArrowBasedBuilder      ):
						UpperCamelCase      =       ParquetConfig

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return datasets.DatasetInfo(features=self.config.features			)

						def      A__    (		self :List[Any]     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										if not self.config.data_files:
														raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"			)
										__magic_name__						:						Any										=dl_manager.download_and_extract(self.config.data_files			)
										if isinstance(__snake_case     ,			(str, list, tuple)			):
														__magic_name__						:						Optional[Any]										=data_files
														if isinstance(__snake_case     ,			__snake_case			):
																		__magic_name__						:						Optional[int]										=[files]
														# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
														__magic_name__						:						List[str]										=[dl_manager.iter_files(__snake_case			) for file in files]
														return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,			gen_kwargs={"""files""": files}			)]
										__magic_name__						:						Optional[int]										=[]
										for split_name, files in data_files.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		__magic_name__						:						Optional[Any]										=[files]
														# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
														__magic_name__						:						Union[str, Any]										=[dl_manager.iter_files(__snake_case			) for file in files]
														# Infer features is they are stoed in the arrow schema
														if self.info.features is None:
																		for file in itertools.chain.from_iterable(__snake_case			):
																						with open(__snake_case     ,			"""rb"""			) as f:
																										__magic_name__						:						List[Any]										=datasets.Features.from_arrow_schema(pq.read_schema(__snake_case			)			)
																						break
														splits.append(datasets.SplitGenerator(name=__snake_case     ,			gen_kwargs={"""files""": files}			)			)
										return splits

						def      A__    (		self :List[Any]     ,			__snake_case :pa.Table			):
										'''simple docstring'''

										if self.info.features is not None:
														# more expensive cast to support nested features with keys in a different order
														# allows str <-> int/float or str to Audio for example
														__magic_name__						:						Union[str, Any]										=table_cast(__snake_case     ,			self.info.features.arrow_schema			)
										return pa_table


						def      A__    (		self :Tuple     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.info.features.arrow_schema if self.info.features is not None else None
										if self.info.features is not None and self.config.columns is not None:
														if sorted(field.name for field in schema			) != sorted(self.config.columns			):
																		raise ValueError(
																		    f"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"			)
										for file_idx, file in enumerate(itertools.chain.from_iterable(__snake_case			)			):
														with open(__snake_case     ,			"""rb"""			) as f:
																		__magic_name__						:						Optional[Any]										=pq.ParquetFile(__snake_case			)
																		try:
																						for batch_idx, record_batch in enumerate(
																						    parquet_file.iter_batches(batch_size=self.config.batch_size     ,			columns=self.config.columns			)			):
																										__magic_name__						:						Dict										=pa.Table.from_batches([record_batch]			)
																										# Uncomment for debugging (will print the Arrow table size and elements)
																										# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																										# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																										yield f"{file_idx}_{batch_idx}", self._cast_table(__snake_case			)
																		except ValueError as e:
																						logger.error(f"Failed to read file '{file}' with error {type(__snake_case			)}: {e}"			)
																						raise










import importlib

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




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Optional[int]										=OmegaConf.load(lowerCamelCase )
				if display:
								print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
				return config




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=None ):
				if conf_path is None:
								__magic_name__						:						List[str]										="""./model_checkpoints/vqgan_only.yaml"""
				__magic_name__						:						Dict										=load_config(lowerCamelCase						,  display=lowerCamelCase )
				__magic_name__						:						Tuple										=VQModel(**config.model.params )
				if ckpt_path is None:
								__magic_name__						:						Optional[Any]										="""./model_checkpoints/vqgan_only.pt"""
				__magic_name__						:						Tuple										=torch.load(lowerCamelCase						,  map_location=lowerCamelCase )
				if ".ckpt" in ckpt_path:
								__magic_name__						:						Any										=sd["""state_dict"""]
				model.load_state_dict(lowerCamelCase						,  strict=lowerCamelCase )
				model.to(lowerCamelCase )
				del sd
				return model




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[Any]										=model.encode(lowerCamelCase )
				print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
				__magic_name__						:						List[Any]										=model.decode(lowerCamelCase )
				return xrec




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__					,			__magic_name__						:						Optional[int]										=string.rsplit("""."""						,  1 )
				if reload:
								__magic_name__						:						Optional[int]										=importlib.import_module(lowerCamelCase )
								importlib.reload(lowerCamelCase )
				return getattr(importlib.import_module(lowerCamelCase						,  package=lowerCamelCase )						,  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 ):
				__magic_name__						:						str										=instantiate_from_config(lowerCamelCase )
				if sd is not None:
								model.load_state_dict(lowerCamelCase )
				if gpu:
								model.cuda()
				if eval_mode:
								model.eval()
				return {"model": model}




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				# load the specified checkpoint
				if ckpt:
								__magic_name__						:						str										=torch.load(lowerCamelCase						,  map_location="""cpu""" )
								__magic_name__						:						Any										=pl_sd["""global_step"""]
								print(F"loaded model from global step {global_step}." )
				else:
								__magic_name__						:						List[Any]										={"""state_dict""": None}
								__magic_name__						:						Optional[Any]										=None
				__magic_name__						:						Tuple										=load_model_from_config(config.model						,  pl_sd["""state_dict"""]						,  gpu=lowerCamelCase						,  eval_mode=lowerCamelCase )["""model"""]
				return model, global_step




