


import importlib

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



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:Tuple=False				)->Optional[int]:
   '''simple docstring'''






   snake_case_            =       OmegaConf.load(lowerCAmelCase_				)
   if display:
      print(yaml.dump(OmegaConf.to_container(lowerCAmelCase_				)				)				)
   return config



def       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, Any]		,    lowerCAmelCase_				:Tuple=None		,    lowerCAmelCase_				:Any=None				)->Optional[Any]:
   '''simple docstring'''






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



def       _lowerCAmelCase    (				lowerCAmelCase_				:Any		,    lowerCAmelCase_				:Optional[int]				)->List[str]:
   '''simple docstring'''






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



def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:List[Any]=False				)->str:
   '''simple docstring'''






   snake_case_     ,     snake_case_            =       string.rsplit("."		,    1				)
   if reload:
      snake_case_            =       importlib.import_module(lowerCAmelCase_				)
      importlib.reload(lowerCAmelCase_				)
   return getattr(importlib.import_module(lowerCAmelCase_		,    package=lowerCAmelCase_				)		,    cls				)



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[int]				)->Dict:
   '''simple docstring'''






   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_				:int		,    lowerCAmelCase_				:List[Any]		,    lowerCAmelCase_				:int=True		,    lowerCAmelCase_				:str=True				)->int:
   '''simple docstring'''






   snake_case_            =       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_				:Union[str, Any]		,    lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:str		,    lowerCAmelCase_				:Dict				)->int:
   '''simple docstring'''






   if ckpt:
      snake_case_            =       torch.load(lowerCAmelCase_		,    map_location="cpu"				)
      snake_case_            =       pl_sd["global_step"]
      print(F'''loaded model from global step {global_step}.'''				)
   else:
      snake_case_            =       {"state_dict": None}
      snake_case_            =       None
   snake_case_            =       load_model_from_config(config.model		,    pl_sd["state_dict"]		,    gpu=lowerCAmelCase_		,    eval_mode=lowerCAmelCase_				)["model"]
   return model, global_step








import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					*_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							List[Any]=None     ,					_lowerCAmelCase							:							str=None     ,					**_lowerCAmelCase							:							str	) ->			Dict:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       eval_examples
     snake_case_            =       post_process_function



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Dataset] = None     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							Optional[List[str]] = None     ,					_lowerCAmelCase							:							str = "eval"     ,					**_lowerCAmelCase							:							Tuple     ,					) ->			Dict[str, float]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()
     snake_case_            =       (
         gen_kwargs["max_length"] if gen_kwargs.get("max_length"	) is not None else self.args.generation_max_length
     )
     snake_case_            =       (
         gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"	) is not None else self.args.generation_num_beams
     )
     snake_case_            =       gen_kwargs

     snake_case_            =       self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_            =       self.get_eval_dataloader(_lowerCAmelCase	)
     snake_case_            =       self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Evaluation"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics
     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)

     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
        # Only the main node write the results by default
        snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
        snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

        # Prefix all keys with metric_key_prefix + '_'
        for key in list(metrics.keys()	):
           if not key.startswith(F'''{metric_key_prefix}_'''	):
              snake_case_            =       metrics.pop(_lowerCAmelCase	)

        metrics.update(output.metrics	)
     else:
        snake_case_            =       output.metrics

     if self.args.should_log:
        # Only the main node log the results by default
        self.log(_lowerCAmelCase	)

     if self.args.tpu_metrics_debug or self.args.debug:
        # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
        xm.master_print(met.metrics_report()	)

     snake_case_            =       self.callback_handler.on_evaluate(self.args     ,					self.state     ,					self.control     ,					_lowerCAmelCase	)
     return metrics



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]=None     ,					_lowerCAmelCase							:							str = "test"     ,					**_lowerCAmelCase							:							Optional[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       gen_kwargs.copy()

     snake_case_            =       self.get_test_dataloader(_lowerCAmelCase	)

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_            =       self.compute_metrics
     snake_case_            =       None
     snake_case_            =       time.time()
     snake_case_            =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
        snake_case_            =       eval_loop(
            _lowerCAmelCase     ,					description="Prediction"     ,					prediction_loss_only=True if compute_metrics is None else None     ,					ignore_keys=_lowerCAmelCase     ,					metric_key_prefix=_lowerCAmelCase     ,					)
     finally:
        snake_case_            =       compute_metrics

     snake_case_            =       self.args.eval_batch_size * self.args.world_size
     if F'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
        start_time += output.metrics[F'''{metric_key_prefix}_jit_compilation_time''']
     output.metrics.update(
         speed_metrics(
             _lowerCAmelCase     ,					_lowerCAmelCase     ,					num_samples=output.num_samples     ,					num_steps=math.ceil(output.num_samples / total_batch_size	)     ,					)	)
     if self.post_process_function is None or self.compute_metrics is None:
        return output

     snake_case_            =       self.post_process_function(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					"predict"	)
     snake_case_            =       self.compute_metrics(_lowerCAmelCase	)

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()	):
        if not key.startswith(F'''{metric_key_prefix}_'''	):
           snake_case_            =       metrics.pop(_lowerCAmelCase	)
     metrics.update(output.metrics	)
     return PredictionOutput(predictions=predictions.predictions     ,					label_ids=predictions.label_ids     ,					metrics=_lowerCAmelCase	)




