



'''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      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Any     =					filter(lambda lowerCamelCase__   : p.requires_grad						,				model.parameters()   )
			A_     :						str     =					sum([np.prod(p.size()   ) for p in model_parameters]   )
			return params


lowerCamelCase							:Optional[Any]       = logging.getLogger(__name__)



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			if metric == "rouge2":
						A_     :						int     =					"""{val_avg_rouge2:.4f}-{step_count}"""
			elif metric == "bleu":
						A_     :						Optional[Any]     =					"""{val_avg_bleu:.4f}-{step_count}"""
			elif metric == "em":
						A_     :						Any     =					"""{val_avg_em:.4f}-{step_count}"""
			elif metric == "loss":
						A_     :						Tuple     =					"""{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_     :						Any     =					ModelCheckpoint(
			    dirpath=lowerCamelCase__						,				filename=lowerCamelCase__						,				monitor=f'val_{metric}'						,				mode="""max"""						,				save_top_k=1						,				every_n_epochs=1						,				)

			return checkpoint_callback



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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







class      _lowerCAmelCase							(      pl.Callback			):


	def    _a     (self	,				lowercase	,				lowercase							):
				A_     :						Optional[Any]     =					{F'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
				pl_module.logger.log_metrics(lowercase							)


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

				if not save_generations:
							return

				if "preds" in metrics:
							A_     :						Optional[int]     =					"""\n""".join(metrics["""preds"""]							)
							generations_file.open("""w+"""							).write(lowercase							)


	@rank_zero_only
	def    _a     (self	,				lowercase	,				lowercase							):
				try:
							A_     :						List[Any]     =					pl_module.model.model.num_parameters()
				except AttributeError:
							A_     :						Optional[int]     =					pl_module.model.num_parameters()

				A_     :						Any     =					count_trainable_parameters(lowercase							)
				# 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    _a     (self	,				lowercase	,				lowercase							):
				save_json(pl_module.metrics	,				pl_module.metrics_save_path							)
				return self._write_logs(lowercase	,				lowercase	,				"""test"""							)


	@rank_zero_only
	def    _a     (self	,				lowercase	,				lowercase							):
				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 unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      _lowerCAmelCase							:


	@property
	def    _a     (self							):
				return self.get_dummy_input()


	@property
	def    _a     (self							):
				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'							)


	def    _a     (self	,				lowercase=True	,				lowercase=False	,				lowercase=False	,				lowercase=False	,				):
				A_     :						List[str]     =					4
				A_     :						int     =					32
				A_     :						Optional[int]     =					(32, 32)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)
				A_     :						int     =					torch.device(lowercase							)
				A_     :						int     =					(batch_size, num_channels) + sizes
				A_     :						Optional[int]     =					randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							)
				A_     :						Union[str, Any]     =					{"""hidden_states""": hidden_states}

				if include_temb:
							A_     :						str     =					128
							A_     :						List[Any]     =					randn_tensor((batch_size, temb_channels)	,				generator=lowercase	,				device=lowercase							)

				if include_res_hidden_states_tuple:
							A_     :						List[str]     =					torch.manual_seed(1							)
							A_     :						int     =					(randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							),)

				if include_encoder_hidden_states:
							A_     :						List[str]     =					floats_tensor((batch_size, 32, 32)							).to(lowercase							)

				if include_skip_sample:
							A_     :						Dict     =					randn_tensor(((batch_size, 3) + sizes)	,				generator=lowercase	,				device=lowercase							)

				return dummy_input


	def    _a     (self							):
				A_     :						Tuple     =					{
				    """in_channels""": 32,
				    """out_channels""": 32,
				    """temb_channels""": 128,
				}
				if self.block_type == "up":
							A_     :						Any     =					32

				if self.block_type == "mid":
							init_dict.pop("""out_channels"""							)

				A_     :						Optional[int]     =					self.dummy_input
				return init_dict, inputs_dict


	def    _a     (self	,				lowercase							):
				A_,   A_     :						Optional[Any]     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						int     =					self.block_class(**lowercase							)
				unet_block.to(lowercase							)
				unet_block.eval()

				with torch.no_grad():
							A_     :						List[str]     =					unet_block(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						Union[str, Any]     =					output[0]

				self.assertEqual(output.shape	,				self.output_shape							)

				A_     :						int     =					output[0, -1, -3:, -3:]
				A_     :						List[Any]     =					torch.tensor(lowercase							).to(lowercase							)
				assert torch_all_close(output_slice.flatten()	,				lowercase	,				atol=5E-3							)


	@unittest.skipIf(torch_device == """mps"""	,				"""Training is not supported in mps"""							)
	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						List[str]     =					self.block_class(**lowercase							)
				model.to(lowercase							)
				model.train()
				A_     :						Any     =					model(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						str     =					output[0]

				A_     :						Union[str, Any]     =					torch.device(lowercase							)
				A_     :						Tuple     =					randn_tensor(output.shape	,				device=lowercase							)
				A_     :						List[str]     =					torch.nn.functional.mse_loss(lowercase	,				lowercase							)
				loss.backward()