






"""simple docstring"""







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 	UpperCamelCase_			(							a__	):



  def __init__(   self			,		*snake_case__			,		snake_case__=None			,		snake_case__=None			,		**snake_case__					)      ->      List[Any]:



         """simple docstring"""





         super().__init__(*lowerCamelCase_			,		**lowerCamelCase_					)
         UpperCAmelCase						     =      eval_examples
         UpperCAmelCase						     =      post_process_function



  def 		UpperCamelCase_					(   self			,		snake_case__ = None			,		snake_case__=None			,		snake_case__ = None			,		snake_case__ = "eval"			,		**snake_case__			,		)      ->      Dict[str, float]:



         """simple docstring"""





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

         UpperCAmelCase						     =      self.eval_dataset if eval_dataset is None else eval_dataset
         UpperCAmelCase						     =      self.get_eval_dataloader(lowerCamelCase_					)
         UpperCAmelCase						     =      self.eval_examples if eval_examples is None else eval_examples

         # Temporarily disable metric computation, we will do it in the loop here.
         UpperCAmelCase						     =      self.compute_metrics
         UpperCAmelCase						     =      None
         UpperCAmelCase						     =      time.time()
         UpperCAmelCase						     =      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         try:
                UpperCAmelCase						     =      eval_loop(
                    lowerCamelCase_			,		description="""Evaluation"""			,		prediction_loss_only=True if compute_metrics is None else None			,		ignore_keys=lowerCamelCase_			,		metric_key_prefix=lowerCamelCase_			,		)
         finally:
                UpperCAmelCase						     =      compute_metrics
         UpperCAmelCase						     =      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
                UpperCAmelCase						     =      self.post_process_function(lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_					)
                UpperCAmelCase						     =      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}_'''					):
                              UpperCAmelCase						     =      metrics.pop(lowerCamelCase_					)

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

         UpperCAmelCase						     =      self.callback_handler.on_evaluate(self.args			,		self.state			,		self.control			,		lowerCamelCase_					)
         return metrics



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__			,		snake_case__=None			,		snake_case__ = "test"			,		**snake_case__					)      ->      int:



         """simple docstring"""





         UpperCAmelCase						     =      gen_kwargs.copy()

         UpperCAmelCase						     =      self.get_test_dataloader(lowerCamelCase_					)

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

         UpperCAmelCase						     =      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

         UpperCAmelCase						     =      self.post_process_function(lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		"""predict"""					)
         UpperCAmelCase						     =      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}_'''					):
                       UpperCAmelCase						     =      metrics.pop(lowerCamelCase_					)
         metrics.update(output.metrics					)
         return PredictionOutput(predictions=predictions.predictions			,		label_ids=predictions.label_ids			,		metrics=lowerCamelCase_					)




'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							lowerCAmelCase__			  =      list(state_dict.keys()			)

							# extract state_dict for VQVAE
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''first_stage_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

    # extract state_dict for UNetLDM
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''model.diffusion_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							lowerCAmelCase__			  =      DDIMScheduler(
							    timesteps=config.model.params.timesteps					,       beta_schedule='''scaled_linear'''					,       beta_start=config.model.params.linear_start					,       beta_end=config.model.params.linear_end					,       clip_sample=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				argparse.ArgumentParser()
		parser.add_argument('''--checkpoint_path''', type=str, required=True)
		parser.add_argument('''--config_path''', type=str, required=True)
		parser.add_argument('''--output_path''', type=str, required=True)
		__UpperCAmelCase       							=				parser.parse_args()

		convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)