





'''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  _lowerCamelCase				( lowercase						:      Dict						)	->	Any:
				_a								= filter(lambda lowercase						: p.requires_grad    ,							model.parameters()						)
				_a								= sum([np.prod(p.size()						) for p in model_parameters]						)
				return params


lowerCAmelCase_					:    int      	=			logging.getLogger(__name__)






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      Any						)	->	Any:
				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}"
				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=lowercase    ,							filename=lowercase    ,							monitor=F'val_{metric}'    ,							mode="max"    ,							save_top_k=3    ,							every_n_epochs=1    ,							)
				return checkpoint_callback






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				return EarlyStopping(
				    monitor=F'val_{metric}'    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase    ,							verbose=lowercase    ,							)



class 				__SCREAMING_SNAKE_CASE  (pl.Callback      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								_a								= {f'lr_group_{i}': param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
								pl_module.logger.log_metrics(__a					)




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					,				__a     :					str					,				__a     :					Tuple=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=__a					)
												generations_file.parent.mkdir(exist_ok=__a					)
								with open(__a					,				"a+"					) as writer:
												for key in sorted(__a					):
																if key in ["log", "progress_bar", "preds"]:
																				continue
																_a								= metrics[key]
																if isinstance(__a					,				torch.Tensor					):
																				_a								= val.item()
																_a								= f'{key}: {val:.6f}\n'
																writer.write(__a					)

								if not save_generations:
												return

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




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					int					,				__a     :					List[Any]					,				__a     :					Union[str, Any]					):
								try:
												_a								= pl_module.model.model.num_parameters()
								except AttributeError:
												_a								= pl_module.model.num_parameters()

								_a								= count_trainable_parameters(__a					)
								# 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   UpperCamelCase__   (  self     :					Union[str, Any]					,				__a     :					pl.Trainer					,				__a     :					pl.LightningModule					):
								save_json(pl_module.metrics					,				pl_module.metrics_save_path					)
								return self._write_logs(__a					,				__a					,				"test"					)




				@rank_zero_only
				def   UpperCamelCase__   (  self     :					Any					,				__a     :					pl.Trainer					,				__a     :					int					):
								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 torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
