


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						(							snake_case	:    List[str] )       ->							Tuple:
			__lowerCamelCase:				Union[str, Any]     =	filter(lambda snake_case : p.requires_grad						,      model.parameters() )
			__lowerCamelCase:				List[str]     =	sum([np.prod(p.size() ) for p in model_parameters] )
			return params


_A		:				Optional[int]									=       logging.getLogger(__name__)



def     __lowerCAmelCase						(							snake_case	:    Optional[Any]						,      snake_case	:    Any )       ->							List[Any]:



			if metric == "rouge2":
						__lowerCamelCase:				Union[str, Any]     =	"""{val_avg_rouge2:.4f}-{step_count}"""
			elif metric == "bleu":
						__lowerCamelCase:				Optional[Any]     =	"""{val_avg_bleu:.4f}-{step_count}"""
			elif metric == "em":
						__lowerCamelCase:				List[Any]     =	"""{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.""" )

			__lowerCamelCase:				str     =	ModelCheckpoint(
			    dirpath=lowerCamelCase_						,      filename=lowerCamelCase_						,      monitor=f'val_{metric}'						,      mode="""max"""						,      save_top_k=3						,      every_n_epochs=1						,      )
			return checkpoint_callback



def     __lowerCAmelCase						(							snake_case	:    List[Any]						,      snake_case	:    Union[str, Any] )       ->							Tuple:
			return EarlyStopping(
			    monitor=f'val_{metric}'						,      mode="""min""" if """loss""" in metric else """max"""						,      patience=lowerCamelCase_						,      verbose=lowerCamelCase_						,      )







class        a					(			pl.Callback   ):





					def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
								__lowerCamelCase:				Tuple     =	{F'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
								pl_module.logger.log_metrics(lowerCamelCase_							)





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

								if not save_generations:
											return

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





					@rank_zero_only
					def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any							):



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

								__lowerCamelCase:				str     =	count_trainable_parameters(lowerCamelCase_							)
								# 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  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				pl.Trainer    ,	SCREAMING_SNAKE_CASE_					:				pl.LightningModule							):
								save_json(pl_module.metrics    ,	pl_module.metrics_save_path							)
								return self._write_logs(lowerCamelCase_    ,	lowerCamelCase_    ,	"""test"""							)







					@rank_zero_only
					def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				pl.Trainer    ,	SCREAMING_SNAKE_CASE_					:				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")






from manim import *



class        a					(			_UpperCAmelCase   ):





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				int     =	Rectangle(height=0.5    ,	width=0.5							)
        __lowerCamelCase:				List[str]     =	Rectangle(height=0.25    ,	width=0.25							)
        __lowerCamelCase:				Optional[int]     =	Rectangle(height=0.46    ,	width=0.46							).set_stroke(width=0							)

        __lowerCamelCase:				str     =	[mem.copy() for i in range(6							)]
        __lowerCamelCase:				Dict     =	[mem.copy() for i in range(6							)]
        __lowerCamelCase:				Union[str, Any]     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
        __lowerCamelCase:				Tuple     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
        __lowerCamelCase:				Optional[int]     =	VGroup(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
        __lowerCamelCase:				Dict     =	Text("""CPU"""    ,	font_size=24							)
        __lowerCamelCase:				Any     =	Group(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0.5    ,	aligned_edge=SCREAMING_SNAKE_CASE_							)
        cpu.move_to([-2.5, -0.5, 0]							)
        self.add(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	[mem.copy() for i in range(4							)]
        __lowerCamelCase:				Optional[int]     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
        __lowerCamelCase:				Dict     =	Text("""GPU"""    ,	font_size=24							)
        __lowerCamelCase:				Dict     =	Group(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0.5    ,	aligned_edge=SCREAMING_SNAKE_CASE_							)
        gpu.move_to([-1, -1, 0]							)
        self.add(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	[mem.copy() for i in range(6							)]
        __lowerCamelCase:				Dict     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)

        __lowerCamelCase:				List[Any]     =	Text("""Model"""    ,	font_size=24							)
        __lowerCamelCase:				Any     =	Group(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0.5    ,	aligned_edge=SCREAMING_SNAKE_CASE_							)
        model.move_to([3, -1.0, 0]							)
        self.add(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	[]
        __lowerCamelCase:				Any     =	[]
        __lowerCamelCase:				int     =	[]

        for i, rect in enumerate(SCREAMING_SNAKE_CASE_							):
           rect.set_stroke(SCREAMING_SNAKE_CASE_							)

           __lowerCamelCase:				int     =	Rectangle(height=0.46 / 4    ,	width=0.46 / 3							).set_stroke(width=0.0							).set_fill(SCREAMING_SNAKE_CASE_    ,	opacity=0.7							)

           if i == 0:
              cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							)    ,	buff=0.02    ,	direction=SCREAMING_SNAKE_CASE_							)
              cpu_target.set_x(cpu_target.get_x() + 0.1							)
           elif i == 3:
              cpu_target.next_to(model_cpu_arr[0]    ,	direction=SCREAMING_SNAKE_CASE_    ,	buff=0.0							)
           else:
              cpu_target.next_to(model_cpu_arr[i - 1]    ,	direction=SCREAMING_SNAKE_CASE_    ,	buff=0.0							)
           self.add(SCREAMING_SNAKE_CASE_							)
           model_cpu_arr.append(SCREAMING_SNAKE_CASE_							)

        self.add(*SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	[mem.copy() for i in range(6							)]
        __lowerCamelCase:				Any     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)

        __lowerCamelCase:				Tuple     =	Text("""Loaded Checkpoint"""    ,	font_size=24							)
        __lowerCamelCase:				Tuple     =	Group(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0.5    ,	aligned_edge=SCREAMING_SNAKE_CASE_							)
        checkpoint.move_to([3, 0.5, 0]							)

        self.add(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	[]
        __lowerCamelCase:				Optional[int]     =	[]

        for i, rect in enumerate(SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				Optional[int]     =	fill.copy().set_fill(SCREAMING_SNAKE_CASE_    ,	opacity=0.7							)
           target.move_to(SCREAMING_SNAKE_CASE_							)
           ckpt_arr.append(SCREAMING_SNAKE_CASE_							)

           __lowerCamelCase:				List[Any]     =	target.copy()
           if i < 5:
              cpu_target.move_to(cpu_left_col_base[i + 1]							)
           else:
              cpu_target.move_to(cpu_right_col_base[i - 5]							)
           ckpt_cpu_arr.append(SCREAMING_SNAKE_CASE_							)
        self.add(*SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[Any]     =	Square(side_length=2.2							)
        key.move_to([-5, 2, 0]							)

        __lowerCamelCase:				Dict     =	MarkupText(
            F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'    ,	font_size=18    ,	)

        key_text.move_to([-5, 2.4, 0]							)

        self.add(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	MarkupText(
            F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'    ,	font_size=18    ,	)

        blue_text.next_to(SCREAMING_SNAKE_CASE_    ,	DOWN * 2.4    ,	aligned_edge=key_text.get_left()							)
        self.add(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	MarkupText(
            F'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'    ,	font_size=24    ,	)
        step_a.move_to([2, 2, 0]							)

        __lowerCamelCase:				List[Any]     =	[meta_mem.copy() for i in range(6							)]
        __lowerCamelCase:				Optional[int]     =	[meta_mem.copy() for i in range(6							)]
        __lowerCamelCase:				str     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
        __lowerCamelCase:				Optional[int]     =	VGroup(*SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
        __lowerCamelCase:				List[str]     =	VGroup(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0							)
        __lowerCamelCase:				Dict     =	Text("""Disk"""    ,	font_size=24							)
        __lowerCamelCase:				Any     =	Group(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).arrange(SCREAMING_SNAKE_CASE_    ,	buff=0.5    ,	aligned_edge=SCREAMING_SNAKE_CASE_							)
        disk.move_to([-4.0, -1.25, 0]							)
        self.play(Write(SCREAMING_SNAKE_CASE_    ,	run_time=3							)    ,	Write(SCREAMING_SNAKE_CASE_    ,	run_time=1							)    ,	Create(SCREAMING_SNAKE_CASE_    ,	run_time=1							)							)

        __lowerCamelCase:				int     =	[]
        for i, rect in enumerate(SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				Optional[int]     =	rect.copy()
           target.generate_target()
           target.target.move_to(disk_left_col_base[i]							).scale(0.5							)
           animations.append(MoveToTarget(SCREAMING_SNAKE_CASE_    ,	run_time=1.5							)							)
        self.play(*SCREAMING_SNAKE_CASE_							)

        self.play(FadeOut(SCREAMING_SNAKE_CASE_							)							)

        __lowerCamelCase:				List[Any]     =	MarkupText(F'Then, the checkpoint is removed from memory\nthrough garbage collection.'    ,	font_size=24							)
        step_a.move_to([2, 2, 0]							)

        self.play(Write(SCREAMING_SNAKE_CASE_    ,	run_time=3							)							)

        self.play(
            FadeOut(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_							)    ,	)

        self.wait()


