





"""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   __SCREAMING_SNAKE_CASE     (    UpperCAmelCase_ ):







  '''simple docstring'''


  def __init__(		self :							Dict       , *__a :							List[str]       , __a :							Any=None       , __a :							Any=None       , **__a :							int						)		->					Tuple:
        super().__init__(*_snake_case       , **_snake_case						)
        _UpperCamelCase   :						Optional[Any]    =							eval_examples
        _UpperCamelCase   :						Dict    =							post_process_function

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , __a :							Optional[Dataset] = None       , __a :							int=None       , __a :							Optional[List[str]] = None       , __a :							str = "eval"       , **__a :							Dict       , )		->					int:
        _UpperCamelCase   :						int    =							gen_kwargs.copy()
        _UpperCamelCase   :						Dict    =							(
            gen_kwargs["max_length"] if gen_kwargs.get("max_length"						) is not None else self.args.generation_max_length
        )
        _UpperCamelCase   :						int    =							(
            gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"						) is not None else self.args.generation_num_beams
        )
        _UpperCamelCase   :						Dict    =							gen_kwargs

        _UpperCamelCase   :						Dict    =							self.eval_dataset if eval_dataset is None else eval_dataset
        _UpperCamelCase   :						Union[str, Any]    =							self.get_eval_dataloader(_snake_case						)
        _UpperCamelCase   :						Optional[Any]    =							self.eval_examples if eval_examples is None else eval_examples

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

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

              metrics.update(output.metrics						)
        else:
              _UpperCamelCase   :						int    =							output.metrics

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

        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   :						Optional[Any]    =							self.callback_handler.on_evaluate(self.args       , self.state       , self.control       , _snake_case						)
        return metrics


  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							Union[str, Any]       , __a :							Optional[int]       , __a :							str=None       , __a :							str = "test"       , **__a :							List[str]						)		->					Dict:
        _UpperCamelCase   :						Tuple    =							gen_kwargs.copy()

        _UpperCamelCase   :						Tuple    =							self.get_test_dataloader(_snake_case						)

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

        _UpperCamelCase   :						List[str]    =							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(
                _snake_case       , _snake_case       , 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   :						Any    =							self.post_process_function(_snake_case       , _snake_case       , _snake_case       , "predict"						)
        _UpperCamelCase   :						Any    =							self.compute_metrics(_snake_case						)

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





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    (							__UpperCamelCase       )    ->   Union[str, Any]:
			if hor == 128:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D')

			elif hor == 32:
						A__	  =     ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
						A__	  =     (32, 64, 128, 256)
						A__	  =     ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
			A__	  =     torch.load(f'''/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'''       )
			A__	  =     model.state_dict()
			A__	  =     {
			    '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': 65_536,
			    'mid_block_type': 'MidResTemporalBlock1D',
			    'act_fn': 'mish',
			}
			A__	  =     UNetaDModel(**__UpperCamelCase       )
			print(f'''length of state dict: {len(state_dict.keys()       )}'''       )
			print(f'''length of value function dict: {len(hf_value_function.state_dict().keys()       )}'''       )
			A__	  =     dict(zip(model.state_dict().keys()			,						hf_value_function.state_dict().keys()       )       )
			for k, v in mapping.items():
						A__	  =     state_dict.pop(__UpperCamelCase       )
			hf_value_function.load_state_dict(__UpperCamelCase       )

			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(__UpperCamelCase			,						__UpperCamelCase       )


def A    (							)    ->   List[str]:
			A__	  =     {
			    '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': 65_536,
			    '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',
			}

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

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

			hf_value_function.load_state_dict(__UpperCamelCase       )

			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(__UpperCamelCase			,						__UpperCamelCase       )


if __name__ == "__main__":
							unet(3_2)
							# unet(128)
							value_function()




