
import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)

os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Dict							)    ->   int:







							"""simple docstring"""






							if hor == 128:
														SCREAMING_SNAKE_CASE_	:							int											=("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
														SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=(32, 128, 256)
														SCREAMING_SNAKE_CASE_	:							str											=("UpResnetBlock1D", "UpResnetBlock1D")

							elif hor == 32:
														SCREAMING_SNAKE_CASE_	:							List[Any]											=("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
														SCREAMING_SNAKE_CASE_	:							Optional[Any]											=(32, 64, 128, 256)
														SCREAMING_SNAKE_CASE_	:							Dict											=("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""							)
							SCREAMING_SNAKE_CASE_	:							List[str]											=model.state_dict()
							SCREAMING_SNAKE_CASE_	:							Any											={
							    "down_block_types": down_block_types,
							    "block_out_channels": block_out_channels,
							    "up_block_types": up_block_types,
							    "layers_per_block": 1,
							    "use_timestep_embedding": True,
							    "out_block_type": "OutConv1DBlock",
							    "norm_num_groups": 8,
							    "downsample_each_block": False,
							    "in_channels": 14,
							    "out_channels": 14,
							    "extra_in_channels": 0,
							    "time_embedding_type": "positional",
							    "flip_sin_to_cos": False,
							    "freq_shift": 1,
							    "sample_size": 6_5536,
							    "mid_block_type": "MidResTemporalBlock1D",
							    "act_fn": "mish",
							}
							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=UNetaDModel(**lowerCAmelCase_							)
							print(F"""length of state dict: {len(state_dict.keys()							)}"""							)
							print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()							)}"""							)
							SCREAMING_SNAKE_CASE_	:							Dict											=dict(zip(model.state_dict().keys()       ,hf_value_function.state_dict().keys()							)							)
							for k, v in mapping.items():
														SCREAMING_SNAKE_CASE_	:							Optional[int]											=state_dict.pop(lowerCAmelCase_							)
							hf_value_function.load_state_dict(lowerCAmelCase_							)

							torch.save(hf_value_function.state_dict()       ,F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""							)
							with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""       ,'w'							) as f:
														json.dump(lowerCAmelCase_       ,lowerCAmelCase_							)
def    SCREAMING_SNAKE_CASE__		( )    ->   str:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							Optional[Any]											={
							    "in_channels": 14,
							    "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
							    "up_block_types": (),
							    "out_block_type": "ValueFunction",
							    "mid_block_type": "ValueFunctionMidBlock1D",
							    "block_out_channels": (32, 64, 128, 256),
							    "layers_per_block": 1,
							    "downsample_each_block": True,
							    "sample_size": 6_5536,
							    "out_channels": 14,
							    "extra_in_channels": 0,
							    "time_embedding_type": "positional",
							    "use_timestep_embedding": True,
							    "flip_sin_to_cos": False,
							    "freq_shift": 1,
							    "norm_num_groups": 8,
							    "act_fn": "mish",
							}

							SCREAMING_SNAKE_CASE_	:							Optional[int]											=torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'							)
							SCREAMING_SNAKE_CASE_	:							Dict											=model
							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=UNetaDModel(**lowerCAmelCase_							)
							print(F"""length of state dict: {len(state_dict.keys()							)}"""							)
							print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()							)}"""							)

							SCREAMING_SNAKE_CASE_	:							int											=dict(zip(state_dict.keys()       ,hf_value_function.state_dict().keys()							)							)
							for k, v in mapping.items():
														SCREAMING_SNAKE_CASE_	:							str											=state_dict.pop(lowerCAmelCase_							)

							hf_value_function.load_state_dict(lowerCAmelCase_							)

							torch.save(hf_value_function.state_dict()       ,'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'							)
							with open('hub/hopper-medium-v2/value_function/config.json'       ,'w'							) as f:
														json.dump(lowerCAmelCase_       ,lowerCAmelCase_							)


if __name__ == "__main__":
							unet(32)
							# unet(128)
							value_function()









'''simple docstring'''


import math
import time

from transformers import Trainer, 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 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     def __init__(   self			,     *lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase=None			,     **lowerCamelCase		):


          '''simple docstring'''





          super().__init__(*lowerCamelCase			,     **lowerCamelCase		)
          __A	:       Tuple      =							eval_examples
          __A	:       List[str]      =							post_process_function


     def lowerCAmelCase__  (   self			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase=None			,     lowerCamelCase = "eval"		):


          '''simple docstring'''





          __A	:       Any      =							self.eval_dataset if eval_dataset is None else eval_dataset
          __A	:       Dict      =							self.get_eval_dataloader(lowerCamelCase		)
          __A	:       str      =							self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          __A	:       Any      =							self.compute_metrics
          __A	:       Optional[int]      =							None
          __A	:       Tuple      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __A	:       Dict      =							time.time()
          try:
               __A	:       List[str]      =							eval_loop(
                   lowerCamelCase			,     description="Evaluation"			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=lowerCamelCase			,     metric_key_prefix=lowerCamelCase			,     )
          finally:
               __A	:       List[str]      =							compute_metrics
          __A	:       List[Any]      =							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
               __A	:       List[Any]      =							self.post_process_function(lowerCamelCase			,     lowerCamelCase			,     output.predictions		)
               __A	:       List[str]      =							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}_"		):
                         __A	:       Union[str, Any]      =							metrics.pop(lowerCamelCase		)
               metrics.update(output.metrics		)
          else:
               __A	:       Any      =							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()		)

          __A	:       str      =							self.callback_handler.on_evaluate(self.args			,     self.state			,     self.control			,     lowerCamelCase		)
          return metrics


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase=None			,     lowerCamelCase = "test"		):


          '''simple docstring'''





          __A	:       Tuple      =							self.get_test_dataloader(lowerCamelCase		)

          # Temporarily disable metric computation, we will do it in the loop here.
          __A	:       Union[str, Any]      =							self.compute_metrics
          __A	:       Union[str, Any]      =							None
          __A	:       Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __A	:       Tuple      =							time.time()
          try:
               __A	:       List[str]      =							eval_loop(
                   lowerCamelCase			,     description="Prediction"			,     prediction_loss_only=True if compute_metrics is None else None			,     ignore_keys=lowerCamelCase			,     metric_key_prefix=lowerCamelCase			,     )
          finally:
               __A	:       int      =							compute_metrics
          __A	:       List[Any]      =							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

          __A	:       Dict      =							self.post_process_function(lowerCamelCase			,     lowerCamelCase			,     output.predictions			,     "predict"		)
          __A	:       Tuple      =							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}_"		):
                    __A	:       Any      =							metrics.pop(lowerCamelCase		)
          metrics.update(output.metrics		)
          return PredictionOutput(predictions=predictions.predictions			,     label_ids=predictions.label_ids			,     metrics=lowerCamelCase		)

