import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple					):
					SCREAMING_SNAKE_CASE           =    int(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
					return F"{h}:{m:02d}:{s:02d}" if h != 0 else F"{m:02d}:{s:02d}"





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							str=3_0_0					):
					# docstyle-ignore
					return F"\n    <div>\n      {prefix}\n      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>\n      {label}\n    </div>\n    "





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str]					):
					SCREAMING_SNAKE_CASE           =    "<table border=\"1\" class=\"dataframe\">\n"
					html_code += """  <thead>\n <tr style="text-align: left;">\n"""
					for i in items[0]:
										html_code += F"      <th>{i}</th>\n"
					html_code += "    </tr>\n  </thead>\n  <tbody>\n"
					for line in items[1:]:
										html_code += "    <tr>\n"
										for elt in line:
															SCREAMING_SNAKE_CASE           =    F"{elt:.6f}" if isinstance(UpperCAmelCase__ ,	UpperCAmelCase__					) else str(UpperCAmelCase__					)
															html_code += F"      <td>{elt}</td>\n"
										html_code += "    </tr>\n"
					html_code += "  </tbody>\n</table><p>"
					return html_code


class   lowercase      :
							lowercase__  :    int									=							5
							lowercase__  :    Any									=							0.2




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional["NotebookTrainingTracker"] = None		,							_UpperCamelCase				:   int = 300		,							)   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    total
												SCREAMING_SNAKE_CASE           =    "" if prefix is None else prefix
												SCREAMING_SNAKE_CASE           =    leave
												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    width
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   bool = False		,							_UpperCamelCase				:   str = None )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    value
												if comment is not None:
																	SCREAMING_SNAKE_CASE           =    comment
												if self.last_value is None:
																	SCREAMING_SNAKE_CASE           =    SCREAMING_SNAKE_CASE           =    time.time()
																	SCREAMING_SNAKE_CASE           =    SCREAMING_SNAKE_CASE           =    value
																	SCREAMING_SNAKE_CASE           =    SCREAMING_SNAKE_CASE           =    None
																	SCREAMING_SNAKE_CASE           =    self.warmup
																	SCREAMING_SNAKE_CASE           =    1
																	self.update_bar(_UpperCamelCase )
												elif value <= self.last_value and not force_update:
																	return
												elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for		,							self.total ):
																	if self.first_calls > 0:
																						self.first_calls -= 1
																	SCREAMING_SNAKE_CASE           =    time.time()
																	SCREAMING_SNAKE_CASE           =    current_time - self.start_time
																	# We could have value = self.start_value if the update is called twixe with the same start value.
																	if value > self.start_value:
																						SCREAMING_SNAKE_CASE           =    self.elapsed_time / (value - self.start_value)
																	else:
																						SCREAMING_SNAKE_CASE           =    None
																	if value >= self.total:
																						SCREAMING_SNAKE_CASE           =    self.total
																						SCREAMING_SNAKE_CASE           =    None
																						if not self.leave:
																											self.close()
																	elif self.average_time_per_item is not None:
																						SCREAMING_SNAKE_CASE           =    self.average_time_per_item * (self.total - value)
																	self.update_bar(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    value
																	SCREAMING_SNAKE_CASE           =    current_time
																	if self.average_time_per_item is None:
																						SCREAMING_SNAKE_CASE           =    1
																	else:
																						SCREAMING_SNAKE_CASE           =    max(int(self.update_every / self.average_time_per_item )		,							1 )




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Optional[Any]=None )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    " " * (len(str(self.total ) ) - len(str(_UpperCamelCase ) )) + str(_UpperCamelCase )
												if self.elapsed_time is None:
																	SCREAMING_SNAKE_CASE           =    F"[{spaced_value}/{self.total} : < :"
												elif self.predicted_remaining is None:
																	SCREAMING_SNAKE_CASE           =    F"[{spaced_value}/{self.total} {format_time(self.elapsed_time )}"
												else:
																	SCREAMING_SNAKE_CASE           =    (
																	    F"[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <"
																	    F" {format_time(self.predicted_remaining )}"
																	)
																	self.label += F", {1/self.average_time_per_item:.2f} it/s"
												self.label += "]" if self.comment is None or len(self.comment ) == 0 else F", {self.comment}]"
												self.display()




							def 		__snake_case(  self				:   int )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    html_progress_bar(self.value		,							self.total		,							self.prefix		,							self.label		,							self.width )
												if self.parent is not None:
																	# If this is a child bar, the parent will take care of the display.
																	self.parent.display()
																	return
												if self.output is None:
																	SCREAMING_SNAKE_CASE           =    disp.display(disp.HTML(self.html_code )		,							display_id=_UpperCamelCase )
												else:
																	self.output.update(disp.HTML(self.html_code ) )





							def 		__snake_case(  self				:   str )   ->			str:



												'''simple docstring'''

												if self.parent is None and self.output is not None:
																	self.output.update(disp.HTML("" ) )



class   lowercase      (       a       ):




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Any=None )   ->			Optional[int]:



												'''simple docstring'''

												super().__init__(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    None if column_names is None else [column_names]
												SCREAMING_SNAKE_CASE           =    None




							def 		__snake_case(  self				:   Optional[int] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    html_progress_bar(self.value		,							self.total		,							self.prefix		,							self.label		,							self.width )
												if self.inner_table is not None:
																	self.html_code += text_to_html_table(self.inner_table )
												if self.child_bar is not None:
																	self.html_code += self.child_bar.html_code
												if self.output is None:
																	SCREAMING_SNAKE_CASE           =    disp.display(disp.HTML(self.html_code )		,							display_id=_UpperCamelCase )
												else:
																	self.output.update(disp.HTML(self.html_code ) )




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   List[str] )   ->			Optional[int]:



												'''simple docstring'''

												if self.inner_table is None:
																	SCREAMING_SNAKE_CASE           =    [list(values.keys() ), list(values.values() )]
												else:
																	SCREAMING_SNAKE_CASE           =    self.inner_table[0]
																	if len(self.inner_table ) == 1:
																						# We give a chance to update the column names at the first iteration
																						for key in values.keys():
																											if key not in columns:
																																columns.append(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    columns
																	self.inner_table.append([values[c] for c in columns] )




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   int=None		,							_UpperCamelCase				:   str=300 )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    NotebookProgressBar(_UpperCamelCase		,							prefix=_UpperCamelCase		,							parent=self		,							width=_UpperCamelCase )
												return self.child_bar





							def 		__snake_case(  self				:   Dict )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    None
												self.display()



class   lowercase      (       a       ):




							def __init__(  self				:   List[str] )   ->			str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    False




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   List[Any] )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    "Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step"
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    [self.first_column] + ["Training Loss"]
												if args.evaluation_strategy != IntervalStrategy.NO:
																	column_names.append("Validation Loss" )
												SCREAMING_SNAKE_CASE           =    NotebookTrainingTracker(state.max_steps		,							_UpperCamelCase )




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   Optional[int] )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    int(state.epoch ) if int(state.epoch ) == state.epoch else F"{state.epoch:.2f}"
												self.training_tracker.update(
												    state.global_step + 1		,							comment=F"Epoch {epoch}/{state.num_train_epochs}"		,							force_update=self._force_next_update		,							)
												SCREAMING_SNAKE_CASE           =    False




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Optional[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   str=None		,							**_UpperCamelCase				:   Tuple )   ->			Union[str, Any]:



												'''simple docstring'''

												if not has_length(_UpperCamelCase ):
																	return
												if self.prediction_bar is None:
																	if self.training_tracker is not None:
																						SCREAMING_SNAKE_CASE           =    self.training_tracker.add_child(len(_UpperCamelCase ) )
																	else:
																						SCREAMING_SNAKE_CASE           =    NotebookProgressBar(len(_UpperCamelCase ) )
																	self.prediction_bar.update(1 )
												else:
																	self.prediction_bar.update(self.prediction_bar.value + 1 )




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   List[Any]		,							**_UpperCamelCase				:   int )   ->			Any:



												'''simple docstring'''

												if self.prediction_bar is not None:
																	self.prediction_bar.close()
												SCREAMING_SNAKE_CASE           =    None




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Optional[Any]=None		,							**_UpperCamelCase				:   List[str] )   ->			Optional[int]:



												'''simple docstring'''

												if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
																	SCREAMING_SNAKE_CASE           =    {"Training Loss": logs["loss"]}
																	# First column is necessarily Step sine we're not in epoch eval strategy
																	SCREAMING_SNAKE_CASE           =    state.global_step
																	self.training_tracker.write_line(_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[int]=None		,							**_UpperCamelCase				:   Dict )   ->			Optional[Any]:



												'''simple docstring'''

												if self.training_tracker is not None:
																	SCREAMING_SNAKE_CASE           =    {"Training Loss": "No log", "Validation Loss": "No log"}
																	for log in reversed(state.log_history ):
																						if "loss" in log:
																											SCREAMING_SNAKE_CASE           =    log["loss"]
																											break

																	if self.first_column == "Epoch":
																						SCREAMING_SNAKE_CASE           =    int(state.epoch )
																	else:
																						SCREAMING_SNAKE_CASE           =    state.global_step
																	SCREAMING_SNAKE_CASE           =    "eval"
																	for k in metrics:
																						if k.endswith("_loss" ):
																											SCREAMING_SNAKE_CASE           =    re.sub(R"\_loss$"		,							""		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    metrics.pop("total_flos"		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    metrics.pop("epoch"		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    metrics.pop(F"{metric_key_prefix}_runtime"		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    metrics.pop(F"{metric_key_prefix}_samples_per_second"		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    metrics.pop(F"{metric_key_prefix}_steps_per_second"		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    metrics.pop(F"{metric_key_prefix}_jit_compilation_time"		,							_UpperCamelCase )
																	for k, v in metrics.items():
																						if k == F"{metric_key_prefix}_loss":
																											SCREAMING_SNAKE_CASE           =    v
																						else:
																											SCREAMING_SNAKE_CASE           =    k.split("_" )
																											SCREAMING_SNAKE_CASE           =    " ".join([part.capitalize() for part in splits[1:]] )
																											SCREAMING_SNAKE_CASE           =    v
																	self.training_tracker.write_line(_UpperCamelCase )
																	self.training_tracker.remove_child()
																	SCREAMING_SNAKE_CASE           =    None
																	# Evaluation takes a long time so we should force the next update.
																	SCREAMING_SNAKE_CASE           =    True





							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Any		,							**_UpperCamelCase				:   Optional[Any] )   ->			List[str]:



												'''simple docstring'''

												self.training_tracker.update(
												    state.global_step		,							comment=F"Epoch {int(state.epoch )}/{state.num_train_epochs}"		,							force_update=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    None





from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch


@dataclass
class   lowercase      :
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    torch.Tensor  # [batch_size x 3]
							lowercase__  :    int
							lowercase__  :    int
							lowercase__  :    float
							lowercase__  :    float
							lowercase__  :    Tuple[int]




							def 		__snake_case(  self				:   str )   ->			Dict:



												'''simple docstring'''

												assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
												assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
												assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2




							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												return torch.from_numpy(np.array([self.width, self.height]		,							dtype=np.floataa ) )




							def 		__snake_case(  self				:   Tuple )   ->			List[str]:



												'''simple docstring'''

												return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,							dtype=np.floataa ) )




							def 		__snake_case(  self				:   Any )   ->			torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    torch.arange(self.height * self.width )
												SCREAMING_SNAKE_CASE           =    torch.stack(
												    [
												        pixel_indices % self.width,
												        torch.div(_UpperCamelCase		,							self.width		,							rounding_mode="trunc" ),
												    ]		,							axis=1		,							)
												return coords




							@property
							def 		__snake_case(  self				:   Any )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE           =    self.shape
												SCREAMING_SNAKE_CASE           =    int(np.prod(_UpperCamelCase ) )

												SCREAMING_SNAKE_CASE           =    self.get_image_coords()
												SCREAMING_SNAKE_CASE           =    torch.broadcast_to(coords.unsqueeze(0 )		,							[batch_size * inner_batch_size, *coords.shape] )
												SCREAMING_SNAKE_CASE           =    self.get_camera_rays(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    rays.view(_UpperCamelCase		,							inner_batch_size * self.height * self.width		,							2		,							3 )

												return rays




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   torch.Tensor )   ->			torch.Tensor:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE     ,				*SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    coords.shape
												assert n_coords == 2
												assert batch_size == self.origin.shape[0]

												SCREAMING_SNAKE_CASE           =    coords.view(_UpperCamelCase		,							-1		,							2 )

												SCREAMING_SNAKE_CASE           =    self.resolution()
												SCREAMING_SNAKE_CASE           =    self.fov()

												SCREAMING_SNAKE_CASE           =    (flat.float() / (res - 1)) * 2 - 1
												SCREAMING_SNAKE_CASE           =    fracs * torch.tan(fov / 2 )

												SCREAMING_SNAKE_CASE           =    fracs.view(_UpperCamelCase		,							-1		,							2 )
												SCREAMING_SNAKE_CASE           =    (
												    self.z.view(_UpperCamelCase		,							1		,							3 )
												    + self.x.view(_UpperCamelCase		,							1		,							3 ) * fracs[:, :, :1]
												    + self.y.view(_UpperCamelCase		,							1		,							3 ) * fracs[:, :, 1:]
												)
												SCREAMING_SNAKE_CASE           =    directions / directions.norm(dim=-1		,							keepdim=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    torch.stack(
												    [
												        torch.broadcast_to(self.origin.view(_UpperCamelCase		,							1		,							3 )		,							[batch_size, directions.shape[1], 3] ),
												        directions,
												    ]		,							dim=2		,							)
												return rays.view(_UpperCamelCase		,							*_UpperCamelCase		,							2		,							3 )





							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int )   ->			"DifferentiableProjectiveCamera":



												'''simple docstring'''

												assert width * self.height == height * self.width, "The aspect ratio should not change."
												return DifferentiableProjectiveCamera(
												    origin=self.origin		,							x=self.x		,							y=self.y		,							z=self.z		,							width=_UpperCamelCase		,							height=_UpperCamelCase		,							x_fov=self.x_fov		,							y_fov=self.y_fov		,							)





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    []
					for theta in np.linspace(0 ,	2 * np.pi ,	num=2_0					):
										SCREAMING_SNAKE_CASE           =    np.array([np.sin(UpperCAmelCase__					), np.cos(UpperCAmelCase__					), -0.5]					)
										z /= np.sqrt(np.sum(z**2					)					)
										SCREAMING_SNAKE_CASE           =    -z * 4
										SCREAMING_SNAKE_CASE           =    np.array([np.cos(UpperCAmelCase__					), -np.sin(UpperCAmelCase__					), 0.0]					)
										SCREAMING_SNAKE_CASE           =    np.cross(UpperCAmelCase__ ,	UpperCAmelCase__					)
										origins.append(UpperCAmelCase__					)
										xs.append(UpperCAmelCase__					)
										ys.append(UpperCAmelCase__					)
										zs.append(UpperCAmelCase__					)
					return DifferentiableProjectiveCamera(
					    origin=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	x=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	y=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	z=torch.from_numpy(np.stack(UpperCAmelCase__ ,	axis=0					)					).float() ,	width=UpperCAmelCase__ ,	height=UpperCAmelCase__ ,	x_fov=0.7 ,	y_fov=0.7 ,	shape=(1, len(UpperCAmelCase__					)) ,	)




