import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


lowerCamelCase_				:		Optional[int]       						=       1_000


class 						a__							:



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=1.2      ,						UpperCAmelCase="g"      ,						UpperCAmelCase=0.5      ,						UpperCAmelCase="-"      ,						UpperCAmelCase="test_out.jpg"      ,						UpperCAmelCase=True      ,						UpperCAmelCase=False      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase=0.7      ,						) ->    List[str]:
								if isinstance(UpperCAmelCase      ,						torch.Tensor    ):
													__a							=   img.numpy().astype('np.uint8'    )
								if isinstance(UpperCAmelCase      ,						UpperCAmelCase    ):
													__a							=   img_tensorize(UpperCAmelCase    )
								assert isinstance(UpperCAmelCase      ,						np.ndarray    )

								__a					,      __a							=   img.shape[1], img.shape[0]
								__a							=   mplfigure.Figure(frameon=UpperCAmelCase    )
								__a							=   fig.get_dpi()
								__a							=   (width * scale + 1e-2) / dpi
								__a							=   (height * scale + 1e-2) / dpi
								fig.set_size_inches(UpperCAmelCase      ,						UpperCAmelCase    )
								__a							=   fig.add_axes([0.0, 0.0, 1.0, 1.0]    )
								ax.axis('off'    )
								ax.set_xlim(0.0      ,						UpperCAmelCase    )
								ax.set_ylim(UpperCAmelCase    )

								__a							=   saveas
								__a							=   rgb
								__a							=   pynb
								__a							=   img
								__a							=   edgecolor
								__a							=   0.5
								__a							=   linestyle
								__a							=   int(np.sqrt(min(UpperCAmelCase      ,						UpperCAmelCase    )    ) * scale // 3    )
								__a							=   width
								__a							=   height
								__a							=   scale
								__a							=   fig
								__a							=   ax
								__a							=   pad
								__a							=   idaobj
								__a							=   idaattr
								__a							=   FigureCanvasAgg(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=None    ) ->    Tuple:
								if color is None:
													__a							=   self.edgecolor
								((__a)					,      (__a)					,      (__a)					,      (__a))							=   box
								__a							=   xa - xa
								__a							=   ya - ya
								self.ax.add_patch(
								    mpl.patches.Rectangle(
								        (xa, ya)      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						fill=UpperCAmelCase      ,						edgecolor=UpperCAmelCase      ,						linewidth=self.font_size // 3      ,						alpha=self.alpha      ,						linestyle=self.linestyle      ,						)    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None      ,						UpperCAmelCase=None    ) ->    List[str]:
								if len(boxes.shape    ) > 2:
													__a							=   boxes[0]
								if len(obj_ids.shape    ) > 1:
													__a							=   obj_ids[0]
								if len(obj_scores.shape    ) > 1:
													__a							=   obj_scores[0]
								if len(attr_ids.shape    ) > 1:
													__a							=   attr_ids[0]
								if len(attr_scores.shape    ) > 1:
													__a							=   attr_scores[0]
								if isinstance(UpperCAmelCase      ,						torch.Tensor    ):
													__a							=   boxes.numpy()
								if isinstance(UpperCAmelCase      ,						UpperCAmelCase    ):
													__a							=   np.array(UpperCAmelCase    )
								assert isinstance(UpperCAmelCase      ,						np.ndarray    )
								__a							=   np.prod(boxes[:, 2:] - boxes[:, :2]      ,						axis=1    )
								__a							=   np.argsort(-areas    ).tolist()
								__a							=   boxes[sorted_idxs] if boxes is not None else None
								__a							=   obj_ids[sorted_idxs] if obj_ids is not None else None
								__a							=   obj_scores[sorted_idxs] if obj_scores is not None else None
								__a							=   attr_ids[sorted_idxs] if attr_ids is not None else None
								__a							=   attr_scores[sorted_idxs] if attr_scores is not None else None

								__a							=   [self._random_color(maximum=1    ) for _ in range(len(UpperCAmelCase    )    )]
								__a							=   [assigned_colors[idx] for idx in sorted_idxs]
								if obj_ids is not None:
													__a							=   self._create_text_labels_attr(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													for i in range(len(UpperCAmelCase    )    ):
																		__a							=   assigned_colors[i]
																		self.add_box(boxes[i]      ,						UpperCAmelCase    )
																		self.draw_labels(labels[i]      ,						boxes[i]      ,						UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Dict:
								__a					,      __a					,      __a					,      __a							=   box
								__a							=   (xa, ya)
								__a							=   (ya - ya) * (xa - xa)
								__a							=   _SMALL_OBJ * self.scale
								if instance_area < small or ya - ya < 4_0 * self.scale:
													if ya >= self.height - 5:
																		__a							=   (xa, ya)
													else:
																		__a							=   (xa, ya)

								__a							=   (ya - ya) / np.sqrt(self.height * self.width    )
								__a							=   self._change_color_brightness(UpperCAmelCase      ,						brightness_factor=0.7    )
								__a							=   np.clip((height_ratio - 0.02) / 0.08 + 1      ,						1.2      ,						2    )
								font_size *= 0.75 * self.font_size

								self.draw_text(
								    text=UpperCAmelCase      ,						position=UpperCAmelCase      ,						color=UpperCAmelCase      ,						)



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase="g"      ,						UpperCAmelCase="left"      ,						) ->    List[str]:
								__a							=   0
								__a							=   self.font_size
								__a							=   np.maximum(list(mplc.to_rgb(UpperCAmelCase    )    )      ,						0.2    )
								__a							=   max(0.8      ,						np.max(UpperCAmelCase    )    )
								__a							=   {
								    'facecolor': 'black',
								    'alpha': self.alpha,
								    'pad': self.pad,
								    'edgecolor': 'none',
								}
								__a					,      __a							=   position
								self.ax.text(
								    UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						size=font_size * self.scale      ,						family='sans-serif'      ,						bbox=UpperCAmelCase      ,						verticalalignment='top'      ,						horizontalalignment=UpperCAmelCase      ,						color=UpperCAmelCase      ,						zorder=1_0      ,						rotation=UpperCAmelCase      ,						)



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase=None    ) ->    List[Any]:
								if saveas is None:
													__a							=   self.saveas
								if saveas.lower().endswith('.jpg'    ) or saveas.lower().endswith('.png'    ):
													cva.imwrite(
													    UpperCAmelCase      ,						self._get_buffer()[:, :, ::-1]      ,						)
								else:
													self.fig.savefig(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Tuple:
								__a							=   [self.idaobj[i] for i in classes]
								__a							=   [self.idaattr[i] for i in attr_classes]
								__a							=   [
								    f'''{label} {score:.2f} {attr} {attr_score:.2f}'''
								    for label, score, attr, attr_score in zip(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								]
								return labels



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    List[Any]:
								__a							=   [self.idaobj[i] for i in classes]
								if scores is not None:
													if labels is None:
																		__a							=   ['{:.0f}%'.format(s * 1_0_0    ) for s in scores]
													else:
																		__a							=   ['{} {:.0f}%'.format(UpperCAmelCase      ,						s * 1_0_0    ) for li, s in zip(UpperCAmelCase      ,						UpperCAmelCase    )]
								return labels



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase=2_5_5    ) ->    int:
								__a							=   np.random.randint(0      ,						len(_COLORS    )    )
								__a							=   _COLORS[idx] * maximum
								if not self.rgb:
													__a							=   ret[::-1]
								return ret



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								if not self.pynb:
													__a					,      __a							=   self.canvas.print_to_buffer()
													if (width, height) != (self.width, self.height):
																		__a							=   cva.resize(self.img      ,						(width, height)    )
													else:
																		__a							=   self.img
								else:
													__a							=   io.BytesIO()  # works for cairo backend
													self.canvas.print_rgba(UpperCAmelCase    )
													__a					,      __a							=   self.width, self.height
													__a							=   buf.getvalue()
													__a							=   self.img

								__a							=   np.frombuffer(UpperCAmelCase      ,						dtype='uint8'    )
								__a							=   buffer.reshape(UpperCAmelCase      ,						UpperCAmelCase      ,						4    )
								__a					,      __a							=   np.split(UpperCAmelCase      ,						[3]      ,						axis=2    )

								try:
													import numexpr as ne  # fuse them with numexpr

													__a							=   ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'    )
								except ImportError:
													__a							=   alpha.astype('float32'    ) / 255.0
													__a							=   img * (1 - alpha) + rgb * alpha

								return visualized_image.astype('uint8'    )







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Dict:
								assert brightness_factor >= -1.0 and brightness_factor <= 1.0
								__a							=   mplc.to_rgb(UpperCAmelCase    )
								__a							=   colorsys.rgb_to_hls(*mplc.to_rgb(UpperCAmelCase    )    )
								__a							=   polygon_color[1] + (brightness_factor * polygon_color[1])
								__a							=   0.0 if modified_lightness < 0.0 else modified_lightness
								__a							=   1.0 if modified_lightness > 1.0 else modified_lightness
								__a							=   colorsys.hls_to_rgb(polygon_color[0]      ,						UpperCAmelCase      ,						polygon_color[2]    )
								return modified_color






lowerCamelCase_				:		Optional[int]       						=       (
    np.array(
        [
            0.0_0_0,
            0.4_4_7,
            0.7_4_1,
            0.8_5_0,
            0.3_2_5,
            0.0_9_8,
            0.9_2_9,
            0.6_9_4,
            0.1_2_5,
            0.4_9_4,
            0.1_8_4,
            0.5_5_6,
            0.4_6_6,
            0.6_7_4,
            0.1_8_8,
            0.3_0_1,
            0.7_4_5,
            0.9_3_3,
            0.6_3_5,
            0.0_7_8,
            0.1_8_4,
            0.3_0_0,
            0.3_0_0,
            0.3_0_0,
            0.6_0_0,
            0.6_0_0,
            0.6_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.7_4_9,
            0.7_4_9,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            0.0_0_0,
            0.3_3_3,
            0.6_6_7,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.3_3_3,
            0.0_0_0,
            0.6_6_7,
            0.6_6_7,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.5_0_0,
            0.0_0_0,
            0.6_6_7,
            0.5_0_0,
            0.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.0_0_0,
            0.5_0_0,
            0.3_3_3,
            0.3_3_3,
            0.5_0_0,
            0.3_3_3,
            0.6_6_7,
            0.5_0_0,
            0.3_3_3,
            1.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.0_0_0,
            0.5_0_0,
            0.6_6_7,
            0.3_3_3,
            0.5_0_0,
            0.6_6_7,
            0.6_6_7,
            0.5_0_0,
            0.6_6_7,
            1.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.0_0_0,
            0.5_0_0,
            1.0_0_0,
            0.3_3_3,
            0.5_0_0,
            1.0_0_0,
            0.6_6_7,
            0.5_0_0,
            1.0_0_0,
            1.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.3_3_3,
            1.0_0_0,
            0.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            1.0_0_0,
            0.3_3_3,
            0.3_3_3,
            1.0_0_0,
            0.3_3_3,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.0_0_0,
            1.0_0_0,
            0.6_6_7,
            0.3_3_3,
            1.0_0_0,
            0.6_6_7,
            0.6_6_7,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.0_0_0,
            1.0_0_0,
            1.0_0_0,
            0.3_3_3,
            1.0_0_0,
            1.0_0_0,
            0.6_6_7,
            1.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_6_7,
            0.0_0_0,
            0.0_0_0,
            0.3_3_3,
            0.0_0_0,
            0.0_0_0,
            0.5_0_0,
            0.0_0_0,
            0.0_0_0,
            0.6_6_7,
            0.0_0_0,
            0.0_0_0,
            0.8_3_3,
            0.0_0_0,
            0.0_0_0,
            1.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.0_0_0,
            0.1_4_3,
            0.1_4_3,
            0.1_4_3,
            0.8_5_7,
            0.8_5_7,
            0.8_5_7,
            1.0_0_0,
            1.0_0_0,
            1.0_0_0,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)









'''simple docstring'''

import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


UpperCAmelCase			=    logging.getLogger(__name__)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Tuple							)			->						Dict:







	"""simple docstring"""
	random.seed(_SCREAMING_SNAKE_CASE							)
	np.random.seed(_SCREAMING_SNAKE_CASE							)
	torch.manual_seed(_SCREAMING_SNAKE_CASE							)
	torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						List[Any]							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						Optional[int]							)			->						int:







	"""simple docstring"""
	model.eval()
	lowerCAmelCase    =	next(model.parameters()							).device
	lowerCAmelCase    =	1
	lowerCAmelCase    =	torch.zeros((eval_batch_size, context_len)							,		dtype=torch.long							,		device=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	DataLoader(_SCREAMING_SNAKE_CASE							,		shuffle=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	torch.zeros(1							,		device=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	0
	for batch in eval_dataloader:
		batch.to(_SCREAMING_SNAKE_CASE							)
		# pad
		context.zero_()
		for i in range(_SCREAMING_SNAKE_CASE							):
			lowerCAmelCase    =	batch[i]
		lowerCAmelCase    =	model(_SCREAMING_SNAKE_CASE							,		labels=_SCREAMING_SNAKE_CASE							)
		eval_loss += outputs[0].sum().item()
		nb_eval_examples += batch.size(0							)
	lowerCAmelCase    =	eval_loss / nb_eval_examples
	lowerCAmelCase    =	torch.exp(_SCREAMING_SNAKE_CASE							)
	model.train()
	return perplexity






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int="gpt2"							)			->						List[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	GPTaLMHeadModel.from_pretrained(_SCREAMING_SNAKE_CASE							,		output_hidden_states=_SCREAMING_SNAKE_CASE							)
	torch.save(model.state_dict()							,		model_name + """local.pt"""							)
	return model






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						Tuple							)			->						Union[str, Any]:







	"""simple docstring"""
	lowerCAmelCase    =	copy.deepcopy(_SCREAMING_SNAKE_CASE							)
	model.to(_SCREAMING_SNAKE_CASE							)

	lowerCAmelCase    =	["""bias""", """LayerNorm.weight"""]
	lowerCAmelCase    =	[
	    {
	        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)],
	        """weight_decay""": 0.0,
	    },
	    {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)], """weight_decay""": 0.0},
	]
	lowerCAmelCase    =	AdamW(_SCREAMING_SNAKE_CASE							,		lr=5E-5							,		eps=1E-8							)
	lowerCAmelCase    =	get_linear_schedule_with_warmup(_SCREAMING_SNAKE_CASE							,		0							,		_SCREAMING_SNAKE_CASE							)
	torch.cuda.empty_cache()
	return model, lm_optimizer, lm_scheduler






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Any							)			->						List[str]:







	"""simple docstring"""
	lowerCAmelCase    =	np.array(_SCREAMING_SNAKE_CASE							).mean()
	lowerCAmelCase    =	np.array(_SCREAMING_SNAKE_CASE							).std()
	lowerCAmelCase    =	(real_perps - avg) / std
	lowerCAmelCase    =	list(zip(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							)
	joblib.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						str							,		_SCREAMING_SNAKE_CASE     :						Dict							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						str							,		_SCREAMING_SNAKE_CASE     :						int							,		_SCREAMING_SNAKE_CASE     :						Tuple="dev.jbl"							,		_SCREAMING_SNAKE_CASE     :						List[Any]=recopy_gpta							,		)			->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	[]
	lowerCAmelCase    =	[]
	lowerCAmelCase    =	[]

	# Initialize the transformer model
	lowerCAmelCase    =	copy.deepcopy(_SCREAMING_SNAKE_CASE							)
	orig_model.to(device="""cpu"""							)
	torch.cuda.empty_cache()

	# Compute perplexity of initial transformer model for comparison
	model.train()
	lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	recopy_model(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	for step in tqdm(range(_SCREAMING_SNAKE_CASE							)							):
		lowerCAmelCase    =	torch.zeros((1, context_len)							,		dtype=torch.long							,		device=_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	random.choice(_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	random.randint(0							,		len(story[0]							) - context_len - 1							)
		lowerCAmelCase    =	story[0][start : start + context_len]
		lm_optimizer.zero_grad()
		lowerCAmelCase    =	model(_SCREAMING_SNAKE_CASE							,		labels=_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase    =	outputs[0]
		lowerCAmelCase    =	compute_perplexity(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		model.train()
		lm_loss.backward()
		# Do LM backprop
		torch.nn.utils.clip_grad_norm_(model.parameters()							,		3.0							)
		lm_optimizer.step()
		lm_scheduler.step()  # Update learning rate schedule

		# Compute perplexity after back-propagating on the selected context
		lowerCAmelCase    =	compute_perplexity(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

		# Periodically save the stored (X, IG(X)) pairs
		if step % 1_000 == 0 and step > 1:
			intermittent_save(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

		# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
		lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	recopy_model(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

		past_perps.append(past_perp.item()							)
		real_perps.append(orig_perp - real_perp.item()							)
		contexts.append(np.array(context.cpu()							)							)

	intermittent_save(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Union[str, Any]							,		_SCREAMING_SNAKE_CASE     :						int="data/tokenized_stories_train_wikitext103.jbl"							,		_SCREAMING_SNAKE_CASE     :						Any=100							,		_SCREAMING_SNAKE_CASE     :						Optional[int]=1_026							,		_SCREAMING_SNAKE_CASE     :						List[str]=True							)			->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	joblib.load(_SCREAMING_SNAKE_CASE							)
	print("""data loaded"""							)
	lowerCAmelCase    =	[]
	if trim:
		for i, example in enumerate(_SCREAMING_SNAKE_CASE							):
			if len(example[0]							) > min_len:
				lowerCAmelCase    =	random.randint(0							,		len(example[0]							) - context_len - 1							)
				objective_set.append(example[0, start : start + context_len]							)
			if len(_SCREAMING_SNAKE_CASE							) >= number:
				break
		lowerCAmelCase    =	[]
		for j in range(i + 1							,		len(_SCREAMING_SNAKE_CASE							)							):
			if len(data[j][0]							) > min_len:
				train_data.append(data[j]							)
	else:
		lowerCAmelCase    =	data[0:number]
		lowerCAmelCase    =	data[number:]

	joblib.dump(_SCREAMING_SNAKE_CASE							,		"""objective_set.jbl"""							)
	print("""objective set saved"""							)
	return train_data, objective_set






def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						Any							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						Optional[Any]							,		_SCREAMING_SNAKE_CASE     :						List[str]							,		_SCREAMING_SNAKE_CASE     :						str=50							,		_SCREAMING_SNAKE_CASE     :						Tuple="secondary_learner.pt"							)			->						List[Any]:







	"""simple docstring"""
	lowerCAmelCase    =	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""							)
	# We will use the first 512 pairs from our dataset as a test set for
	# our secondary learner and the rest to train
	lowerCAmelCase    =	train_dataset[:512]
	lowerCAmelCase    =	train_dataset[512:]
	lowerCAmelCase    =	DataLoader(_SCREAMING_SNAKE_CASE							,		shuffle=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	DataLoader(_SCREAMING_SNAKE_CASE							,		shuffle=_SCREAMING_SNAKE_CASE							,		batch_size=_SCREAMING_SNAKE_CASE							)

	# secondary learner model set up
	lowerCAmelCase    =	nn.MSELoss()
	lowerCAmelCase    =	nn.MSELoss(reduction="""sum"""							)
	secondary_learner.to(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase    =	torch.optim.Adam(secondary_learner.parameters()							,		lr=0.0_0001							)
	secondary_learner.train()

	# TODO in original code this is written as number of actual batches seen
	# not number of items seen but other places it is number of items instead.
	# improve consistency! changed this to epochs for clarity
	lowerCAmelCase    =	float("""inf"""							)
	# Iterate through batches until we've used max_steps batches
	for epoch in range(int(_SCREAMING_SNAKE_CASE							)							):
		lowerCAmelCase    =	0.0
		secondary_learner.train()
		for step, batch in enumerate(_SCREAMING_SNAKE_CASE							):
			lowerCAmelCase    =	batch[0].to(_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase    =	batch[1].to(_SCREAMING_SNAKE_CASE							)
			lowerCAmelCase    =	secondary_learner(_SCREAMING_SNAKE_CASE							)
			q_optimizer.zero_grad()
			lowerCAmelCase    =	loss(_SCREAMING_SNAKE_CASE							,		real_q.float()							)
			q_loss.backward()
			q_optimizer.step()
			tr_q_loss += q_loss.item()

			# model trains fairly quickly so we won't wait for a full epoch
			# eval is triggered at eval_freq and end of epochs
			if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_SCREAMING_SNAKE_CASE							)):
				lowerCAmelCase    =	tr_q_loss / (step + 1)

				secondary_learner.eval()
				lowerCAmelCase    =	0.0
				lowerCAmelCase    =	0.0
				lowerCAmelCase    =	[]
				lowerCAmelCase    =	[]
				# Compute performance of the secondary learner after this batch
				for stepa, batcha in enumerate(_SCREAMING_SNAKE_CASE							):
					lowerCAmelCase    =	batcha[0].to(_SCREAMING_SNAKE_CASE							)
					lowerCAmelCase    =	batcha[1].to(_SCREAMING_SNAKE_CASE							)
					lowerCAmelCase    =	secondary_learner(_SCREAMING_SNAKE_CASE							)
					q_lossa += test_loss(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							).item()
					sum_qa += torch.sum(_SCREAMING_SNAKE_CASE							).item()
					for ei, i in enumerate(predicted_qa.cpu().detach().numpy()							):
						predicted.append(i.item()							)
					for ei, i in enumerate(real_qa.cpu().detach().numpy()							):
						actual.append(i.item()							)

				q_lossa /= len(_SCREAMING_SNAKE_CASE							)
				print(
				    """Epoch: """							,		_SCREAMING_SNAKE_CASE							,		"""step: """							,		_SCREAMING_SNAKE_CASE							,		"""Avg. q:"""							,		sum_qa / len(_SCREAMING_SNAKE_CASE							)							,		"""Train Loss: """							,		_SCREAMING_SNAKE_CASE							,		"""Test Loss: """							,		_SCREAMING_SNAKE_CASE							,		)
				if q_lossa < best_test_loss:
					joblib.dump((predicted, actual)							,		"""pred_vs_actual.jbl"""							)
					torch.save(secondary_learner.state_dict()							,		_SCREAMING_SNAKE_CASE							)
					lowerCAmelCase    =	q_lossa

			secondary_learner.train()
	return secondary_learner





class __snake_case(      nn.Module					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	Dict:
							super(A_	,       self      ).__init__()
							lowerCAmelCase    =	model.transformer.wte
							lowerCAmelCase    =	copy.deepcopy(model.transformer.wte.weight      )
							lowerCAmelCase    =	nn.Convad(self.embeddings.weight.size(1      )	,       256	,       3	,       padding=1      )
							lowerCAmelCase    =	nn.Sequential(nn.Linear(256	,       32      )	,       nn.Dropout(p=0.1      )	,       nn.Linear(32	,       32      )	,       nn.Linear(32	,       1      )      )


						def 				__snake_case    (					self	,       A_      )	->	Optional[int]:
							lowerCAmelCase    =	torch.max(self.conv(self.embeddings(A_      ).squeeze(1      ).transpose(1	,       2      )      )	,       2      )[0]
							lowerCAmelCase    =	self.fc(A_      )
							return qs.squeeze(1      )




						@classmethod
						def 				__snake_case    (					cls	,       A_	,       A_      )	->	str:
							lowerCAmelCase    =	cls(A_      )  # this calls __init__
							lowerCAmelCase    =	torch.load(A_      )
							secondary_learner.load_state_dict(A_      )
							lowerCAmelCase    =	model.transformer.wte
							lowerCAmelCase    =	copy.deepcopy(model.transformer.wte.weight      )
							return secondary_learner