




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 			a__    (					snake_case__      ):






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




											"""simple docstring"""



											super().__init__(*_A		,			**_A			)
											__lowerCAmelCase				   =       eval_examples
											__lowerCAmelCase				   =       post_process_function






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A = None		,			_A=None		,			_A = None		,			_A = "eval"		,			**_A		,			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       self.eval_dataset if eval_dataset is None else eval_dataset
											__lowerCAmelCase				   =       self.get_eval_dataloader(_A			)
											__lowerCAmelCase				   =       self.eval_examples if eval_examples is None else eval_examples

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

																# Prefix all keys with metric_key_prefix + '_'
																for key in list(metrics.keys()			):
																					if not key.startswith(f"""{metric_key_prefix}_"""			):
																										__lowerCAmelCase				   =       metrics.pop(_A			)

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

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

											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()			)

											__lowerCAmelCase				   =       self.callback_handler.on_evaluate(self.args		,			self.state		,			self.control		,			_A			)
											return metrics






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A=None		,			_A = "test"		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       gen_kwargs.copy()

											__lowerCAmelCase				   =       self.get_test_dataloader(_A			)

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

											__lowerCAmelCase				   =       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(
											        _A		,			_A		,			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

											__lowerCAmelCase				   =       self.post_process_function(_A		,			_A		,			_A		,			"predict"			)
											__lowerCAmelCase				   =       self.compute_metrics(_A			)

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










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 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					if hor == 1_28:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D")

					elif hor == 32:
										__lowerCAmelCase				   =       ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
										__lowerCAmelCase				   =       (32, 64, 1_28, 2_56)
										__lowerCAmelCase				   =       ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
					__lowerCAmelCase				   =       torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""     )
					__lowerCAmelCase				   =       model.state_dict()
					__lowerCAmelCase				   =       {
					    "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_55_36,
					    "mid_block_type": "MidResTemporalBlock1D",
					    "act_fn": "mish",
					}
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_SNAKE_CASE_     )
					print(F"""length of state dict: {len(state_dict.keys()     )}"""     )
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()     )}"""     )
					__lowerCAmelCase				   =       dict(zip(model.state_dict().keys()      ,      hf_value_function.state_dict().keys()     )     )
					for k, v in mapping.items():
										__lowerCAmelCase				   =       state_dict.pop(SCREAMING_SNAKE_CASE_     )
					hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_     )

					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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )







def 	_a      (						):
					__lowerCAmelCase				   =       {
					    "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, 1_28, 2_56),
					    "layers_per_block": 1,
					    "downsample_each_block": True,
					    "sample_size": 6_55_36,
					    "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",
					}

					__lowerCAmelCase				   =       torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"     )
					__lowerCAmelCase				   =       model
					__lowerCAmelCase				   =       UNetaDModel(**SCREAMING_SNAKE_CASE_     )
					print(F"""length of state dict: {len(state_dict.keys()     )}"""     )
					print(F"""length of value function dict: {len(hf_value_function.state_dict().keys()     )}"""     )

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

					hf_value_function.load_state_dict(SCREAMING_SNAKE_CASE_     )

					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(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )


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




