






"""simple docstring"""


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__SCREAMING_SNAKE_CASE   =   50_00_00

__SCREAMING_SNAKE_CASE   =   os.path.split(__file__)
__SCREAMING_SNAKE_CASE   =   os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def        UpperCAmelCase      (					a__     ,      **a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			dataset.map(**a__ )


@get_duration
def        UpperCAmelCase      (					a__     ,      **a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			dataset.filter(**a__ )


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			{"""num examples""": SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												lowerCAmelCase   :str						=			datasets.Features({'text': datasets.Value('string' ), 'numbers': datasets.Value('float32' )} )
												lowerCAmelCase   :str						=			generate_example_dataset(
												    os.path.join(a__     ,      'dataset.arrow' )     ,      a__     ,      num_examples=a__ )

												lowerCAmelCase   :Dict						=			transformers.AutoTokenizer.from_pretrained('bert-base-cased'     ,      use_fast=a__ )

												def tokenize(a__ ):
																		return tokenizer(examples['text'] )

												lowerCAmelCase   :Union[str, Any]						=			map(a__ )

												lowerCAmelCase   :List[Any]						=			map(a__     ,      batched=a__ )

												lowerCAmelCase   :Optional[int]						=			map(a__     ,      function=lambda a__ : None     ,      batched=a__ )

												with dataset.formatted_as(type='numpy' ):
																		lowerCAmelCase   :Optional[Any]						=			map(a__     ,      function=lambda a__ : None     ,      batched=a__ )

												with dataset.formatted_as(type='pandas' ):
																		lowerCAmelCase   :int						=			map(a__     ,      function=lambda a__ : None     ,      batched=a__ )

												with dataset.formatted_as(type='torch'     ,      columns='numbers' ):
																		lowerCAmelCase   :List[Any]						=			map(a__     ,      function=lambda a__ : None     ,      batched=a__ )

												with dataset.formatted_as(type='tensorflow'     ,      columns='numbers' ):
																		lowerCAmelCase   :Optional[Any]						=			map(a__     ,      function=lambda a__ : None     ,      batched=a__ )

												lowerCAmelCase   :Optional[Any]						=			map(a__     ,      function=a__     ,      batched=a__ )

												lowerCAmelCase   :Optional[int]						=			filter(a__ )

												# Activate later when tokenizer support batched inputs
												# with dataset.formatted_as(type='numpy'):
												#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

						with open(a__     ,      'wb' ) as f:
												f.write(json.dumps(a__ ).encode('utf-8' ) )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_map_filter()





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




