


'''simple docstring'''



import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json




def 			_a			(		_lowercase      :			Union[str, Any]						):





					'''simple docstring'''






					__UpperCAmelCase			:				Optional[Any]													=    filter(lambda _lowercase						: p.requires_grad    ,      model.parameters()						)
					__UpperCAmelCase			:				int													=    sum([np.prod(p.size()						) for p in model_parameters]						)
					return params


__UpperCAmelCase    :int				       =     logging.getLogger(__name__)




def 			_a			(		_lowercase      :			Optional[int]    ,      _lowercase      :			List[str]						):





					'''simple docstring'''






					if metric == "rouge2":
										__UpperCAmelCase			:				int													=    '''{val_avg_rouge2:.4f}-{step_count}'''
					elif metric == "bleu":
										__UpperCAmelCase			:				Dict													=    '''{val_avg_bleu:.4f}-{step_count}'''
					elif metric == "em":
										__UpperCAmelCase			:				str													=    '''{val_avg_em:.4f}-{step_count}'''
					elif metric == "loss":
										__UpperCAmelCase			:				str													=    '''{val_avg_loss:.4f}-{step_count}'''
					else:
										raise NotImplementedError(
										    F'seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'
										    ''' function.'''						)

					__UpperCAmelCase			:				Tuple													=    ModelCheckpoint(
					    dirpath=lowercase__    ,      filename=lowercase__    ,      monitor=F'val_{metric}'    ,      mode='''max'''    ,      save_top_k=1    ,      every_n_epochs=1    ,      )

					return checkpoint_callback




def 			_a			(		_lowercase      :			List[str]    ,      _lowercase      :			Dict						):





					'''simple docstring'''






					return EarlyStopping(
					    monitor=F'val_{metric}'    ,      mode='''min''' if '''loss''' in metric else '''max'''    ,      patience=lowercase__    ,      verbose=lowercase__    ,      )



class 			a       (	pl.Callback ):


					"""simple docstring"""






					def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	List[Any]							,				snake_case  :	Tuple    )		->							Optional[Any]:
										__UpperCAmelCase			:				Any													=    {f'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
										pl_module.logger.log_metrics(__UpperCamelCase    )

					@rank_zero_only
					def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[Any]							,				snake_case  :	int							,				snake_case  :	List[str]=True    )		->							Optional[Any]:
										logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'    )
										__UpperCAmelCase			:				Optional[Any]													=    trainer.callback_metrics
										trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}    )
										# Log results
										__UpperCAmelCase			:				List[str]													=    Path(pl_module.hparams.output_dir    )
										if type_path == "test":
															__UpperCAmelCase			:				List[Any]													=    od / '''test_results.txt'''
															__UpperCAmelCase			:				List[Any]													=    od / '''test_generations.txt'''
										else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__UpperCAmelCase			:				Tuple													=    od / f'{type_path}_results/{trainer.global_step:05d}.txt'
															__UpperCAmelCase			:				str													=    od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
															results_file.parent.mkdir(exist_ok=__UpperCamelCase    )
															generations_file.parent.mkdir(exist_ok=__UpperCamelCase    )
										with open(__UpperCamelCase							,				'''a+'''    ) as writer:
															for key in sorted(__UpperCamelCase    ):
																				if key in ["log", "progress_bar", "preds"]:
																									continue
																				__UpperCAmelCase			:				Any													=    metrics[key]
																				if isinstance(__UpperCamelCase							,				torch.Tensor    ):
																									__UpperCAmelCase			:				List[Any]													=    val.item()
																				__UpperCAmelCase			:				List[str]													=    f'{key}: {val:.6f}\n'
																				writer.write(__UpperCamelCase    )

										if not save_generations:
															return

										if "preds" in metrics:
															__UpperCAmelCase			:				List[str]													=    '''\n'''.join(metrics['''preds''']    )
															generations_file.open('''w+'''    ).write(__UpperCamelCase    )

					@rank_zero_only
					def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Dict    )		->							Optional[Any]:


										try:
															__UpperCAmelCase			:				List[str]													=    pl_module.model.model.num_parameters()
										except AttributeError:
															__UpperCAmelCase			:				Any													=    pl_module.model.num_parameters()

										__UpperCAmelCase			:				int													=    count_trainable_parameters(__UpperCamelCase    )
										# mp stands for million parameters
										trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6}    )

					@rank_zero_only
					def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	str							,				snake_case  :	str    )		->							Optional[int]:
										save_json(pl_module.metrics							,				pl_module.metrics_save_path    )
										return self._write_logs(__UpperCamelCase							,				__UpperCamelCase							,				'''test'''    )






					@rank_zero_only
					def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	List[str]    )		->							Optional[Any]:
										save_json(pl_module.metrics							,				pl_module.metrics_save_path    )
										# Uncommenting this will save val generations
										# return self._write_logs(trainer, pl_module, "valid")



'''simple docstring'''



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			(		_lowercase      :			Any						):





     '''simple docstring'''






     if hor == 128:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Union[str, Any]													=    (32, 128, 256)
          __UpperCAmelCase			:				str													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

     elif hor == 32:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Any													=    (32, 64, 128, 256)
          __UpperCAmelCase			:				Optional[int]													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
     __UpperCAmelCase			:				Any													=    torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'						)
     __UpperCAmelCase			:				str													=    model.state_dict()
     __UpperCAmelCase			:				int													=    {
         '''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''': 65536,
         '''mid_block_type''': '''MidResTemporalBlock1D''',
         '''act_fn''': '''mish''',
     }
     __UpperCAmelCase			:				Optional[Any]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)
     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(model.state_dict().keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				List[Any]													=    state_dict.pop(_lowercase						)
     hf_value_function.load_state_dict(_lowercase						)

     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(_lowercase    ,      _lowercase						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    {
         '''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''': 65536,
         '''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''',
     }

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

     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(state_dict.keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				Dict													=    state_dict.pop(_lowercase						)

     hf_value_function.load_state_dict(_lowercase						)

     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(_lowercase    ,      _lowercase						)


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