
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class __snake_case						( __snake_case	):
    _a						:		Optional[Any]=							42






class __snake_case						( __snake_case   ,      __snake_case	):
    @register_to_config
    def __init__(    self						,snake_case = 65536						,snake_case = None						,snake_case = 2						,snake_case = 2						,snake_case = 0						,snake_case = "fourier"						,snake_case = True						,snake_case = False						,snake_case = 0.0						,snake_case = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")						,snake_case = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")						,snake_case = "UNetMidBlock1D"						,snake_case = None						,snake_case = (32, 32, 64)						,snake_case = None						,snake_case = 8						,snake_case = 1						,snake_case = False						,):



           '''simple docstring'''







           super().__init__()
           lowercase				:	List[str]			      = sample_size

           # time
           if time_embedding_type == "fourier":
                  lowercase				:	Optional[int]			      = GaussianFourierProjection(
                      embedding_size=8						,set_W_to_weight=snake_case_						,log=snake_case_						,flip_sin_to_cos=snake_case_ )
                  lowercase				:	List[str]			      = 2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                  lowercase				:	Union[str, Any]			      = Timesteps(
                      block_out_channels[0]						,flip_sin_to_cos=snake_case_						,downscale_freq_shift=snake_case_ )
                  lowercase				:	Tuple			      = block_out_channels[0]

           if use_timestep_embedding:
                  lowercase				:	List[Any]			      = block_out_channels[0] * 4
                  lowercase				:	Optional[Any]			      = TimestepEmbedding(
                      in_channels=snake_case_						,time_embed_dim=snake_case_						,act_fn=snake_case_						,out_dim=block_out_channels[0]						,)

           lowercase				:	int			      = nn.ModuleList([] )
           lowercase				:	Union[str, Any]			      = None
           lowercase				:	str			      = nn.ModuleList([] )
           lowercase				:	Tuple			      = None

           # down
           lowercase				:	Dict			      = in_channels
           for i, down_block_type in enumerate(snake_case_ ):
                  lowercase				:	Dict			      = output_channel
                  lowercase				:	Optional[int]			      = block_out_channels[i]

                  if i == 0:
                         input_channel += extra_in_channels

                  lowercase				:	int			      = i == len(snake_case_ ) - 1

                  lowercase				:	int			      = get_down_block(
                      snake_case_						,num_layers=snake_case_						,in_channels=snake_case_						,out_channels=snake_case_						,temb_channels=block_out_channels[0]						,add_downsample=not is_final_block or downsample_each_block						,)
                  self.down_blocks.append(snake_case_ )

           # mid
           lowercase				:	Union[str, Any]			      = get_mid_block(
               snake_case_						,in_channels=block_out_channels[-1]						,mid_channels=block_out_channels[-1]						,out_channels=block_out_channels[-1]						,embed_dim=block_out_channels[0]						,num_layers=snake_case_						,add_downsample=snake_case_						,)

           # up
           lowercase				:	str			      = list(reversed(snake_case_ ) )
           lowercase				:	Any			      = reversed_block_out_channels[0]
           if out_block_type is None:
                  lowercase				:	Optional[int]			      = out_channels
           else:
                  lowercase				:	Any			      = block_out_channels[0]

           for i, up_block_type in enumerate(snake_case_ ):
                  lowercase				:	List[Any]			      = output_channel
                  lowercase				:	Tuple			      = (
                      reversed_block_out_channels[i + 1] if i < len(snake_case_ ) - 1 else final_upsample_channels
                  )

                  lowercase				:	Dict			      = i == len(snake_case_ ) - 1

                  lowercase				:	Any			      = get_up_block(
                      snake_case_						,num_layers=snake_case_						,in_channels=snake_case_						,out_channels=snake_case_						,temb_channels=block_out_channels[0]						,add_upsample=not is_final_block						,)
                  self.up_blocks.append(snake_case_ )
                  lowercase				:	List[str]			      = output_channel

           # out
           lowercase				:	Optional[int]			      = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,32 )
           lowercase				:	Optional[int]			      = get_out_block(
               out_block_type=snake_case_						,num_groups_out=snake_case_						,embed_dim=block_out_channels[0]						,out_channels=snake_case_						,act_fn=snake_case_						,fc_dim=block_out_channels[-1] // 4						,)
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case						,snake_case = True						,):



           '''simple docstring'''







           lowercase				:	str			      = timestep
           if not torch.is_tensor(snake_case_ ):
                  lowercase				:	List[Any]			      = torch.tensor([timesteps]						,dtype=torch.long						,device=sample.device )
           elif torch.is_tensor(snake_case_ ) and len(timesteps.shape ) == 0:
                  lowercase				:	Optional[int]			      = timesteps[None].to(sample.device )

           lowercase				:	List[str]			      = self.time_proj(snake_case_ )
           if self.config.use_timestep_embedding:
                  lowercase				:	Optional[int]			      = self.time_mlp(snake_case_ )
           else:
                  lowercase				:	Optional[int]			      = timestep_embed[..., None]
                  lowercase				:	str			      = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
                  lowercase				:	str			      = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

           # 2. down
           lowercase				:	Optional[Any]			      = ()
           for downsample_block in self.down_blocks:
                  lowercase      ,     lowercase				:	Union[str, Any]			      = downsample_block(hidden_states=snake_case_						,temb=snake_case_ )
                  down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                  lowercase				:	List[str]			      = self.mid_block(snake_case_						,snake_case_ )

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks ):
                  lowercase				:	Tuple			      = down_block_res_samples[-1:]
                  lowercase				:	List[str]			      = down_block_res_samples[:-1]
                  lowercase				:	Dict			      = upsample_block(snake_case_						,res_hidden_states_tuple=snake_case_						,temb=snake_case_ )

           # 5. post-process
           if self.out_block:
                  lowercase				:	str			      = self.out_block(snake_case_						,snake_case_ )

           if not return_dict:
                  return (sample,)

           return UNetaDOutput(sample=snake_case_ )







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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							filter(lambda _SCREAMING_SNAKE_CASE				: p.requires_grad							,     model.parameters()				)
    _A     =							sum([np.prod(p.size()				) for p in model_parameters]				)
    return params


__A							:			Union[str, Any]         =			logging.getLogger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    if metric == "rouge2":
        _A     =							'{val_avg_rouge2:.4f}-{step_count}'
    elif metric == "bleu":
        _A     =							'{val_avg_bleu:.4f}-{step_count}'
    elif metric == "em":
        _A     =							'{val_avg_em:.4f}-{step_count}'
    elif metric == "loss":
        _A     =							'{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.'				)

    _A     =							ModelCheckpoint(
        dirpath=_SCREAMING_SNAKE_CASE							,     filename=_SCREAMING_SNAKE_CASE							,     monitor=F"val_{metric}"							,     mode='max'							,     save_top_k=1							,     every_n_epochs=1							,     )

    return checkpoint_callback





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




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







class      lowerCamelCase(						pl.Callback	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							{F"lr_group_{i}": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
         pl_module.logger.log_metrics(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_=True							):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"							)
         _A     =							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
         _A     =							Path(pl_module.hparams.output_dir							)
         if type_path == "test":
             _A     =							od / 'test_results.txt'
             _A     =							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.
             _A     =							od / F"{type_path}_results/{trainer.global_step:05d}.txt"
             _A     =							od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
             results_file.parent.mkdir(exist_ok=snake_case_							)
             generations_file.parent.mkdir(exist_ok=snake_case_							)
         with open(snake_case_				,     'a+'							) as writer:
             for key in sorted(snake_case_							):
                 if key in ["log", "progress_bar", "preds"]:
                     continue
                 _A     =							metrics[key]
                 if isinstance(snake_case_				,     torch.Tensor							):
                     _A     =							val.item()
                 _A     =							F"{key}: {val:.6f}\n"
                 writer.write(snake_case_							)

         if not save_generations:
             return

         if "preds" in metrics:
             _A     =							'\n'.join(metrics['preds']							)
             generations_file.open('w+'							).write(snake_case_							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         try:
             _A     =							pl_module.model.model.num_parameters()
         except AttributeError:
             _A     =							pl_module.model.num_parameters()

         _A     =							count_trainable_parameters(snake_case_							)
         # 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				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         return self._write_logs(snake_case_				,     snake_case_				,     'test'							)




     @rank_zero_only
     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         save_json(pl_module.metrics				,     pl_module.metrics_save_path							)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")





