"""simple docstring"""





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    lowercase_   (   _lowerCamelCase:	int      ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Tuple		   =     int(_lowerCamelCase      )
							__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     t // 3600, (t // 60) % 60, t % 60
							return F"""{h}:{m:02d}:{s:02d}""" if h != 0 else F"""{m:02d}:{s:02d}"""


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	int=300      ) ->      List[str]:



							'''simple docstring'''

							return F"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """


def    lowercase_   (   _lowerCamelCase:	str      ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     "<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:
																					__lowerCamelCase					:		Any		   =     F"""{elt:.6f}""" if isinstance(_lowerCamelCase						,   _lowerCamelCase      ) else str(_lowerCamelCase      )
																					html_code += F"""      <td>{elt}</td>\n"""
														html_code += "    </tr>\n"
							html_code += "  </tbody>\n</table><p>"
							return html_code
class  _snake_case     :
						snake_case__			  =			5
						snake_case__			  =			0.2






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[str] = None							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional["NotebookTrainingTracker"] = None							,					UpperCAmelCase      :			int = 300							,					):
													__lowerCamelCase					:		List[Any]		   =     total
													__lowerCamelCase					:		Tuple		   =     "" if prefix is None else prefix
													__lowerCamelCase					:		Union[str, Any]		   =     leave
													__lowerCamelCase					:		Optional[Any]		   =     parent
													__lowerCamelCase					:		Any		   =     width
													__lowerCamelCase					:		Any		   =     None
													__lowerCamelCase					:		List[Any]		   =     None
													__lowerCamelCase					:		Optional[int]		   =     None






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			str = None    ):
													__lowerCamelCase					:		Any		   =     value
													if comment is not None:
																				__lowerCamelCase					:		List[Any]		   =     comment
													if self.last_value is None:
																				__lowerCamelCase					:		Optional[Any]		   =     time.time()
																				__lowerCamelCase					:		Optional[Any]		   =     value
																				__lowerCamelCase					:		Tuple		   =     None
																				__lowerCamelCase					:		List[Any]		   =     self.warmup
																				__lowerCamelCase					:		List[Any]		   =     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
																				__lowerCamelCase					:		Tuple		   =     time.time()
																				__lowerCamelCase					:		Any		   =     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:
																											__lowerCamelCase					:		Tuple		   =     self.elapsed_time / (value - self.start_value)
																				else:
																											__lowerCamelCase					:		Dict		   =     None
																				if value >= self.total:
																											__lowerCamelCase					:		List[Any]		   =     self.total
																											__lowerCamelCase					:		Optional[int]		   =     None
																											if not self.leave:
																																		self.close()
																				elif self.average_time_per_item is not None:
																											__lowerCamelCase					:		int		   =     self.average_time_per_item * (self.total - value)
																				self.update_bar(UpperCAmelCase    )
																				__lowerCamelCase					:		Optional[Any]		   =     value
																				__lowerCamelCase					:		List[str]		   =     current_time
																				if self.average_time_per_item is None:
																											__lowerCamelCase					:		int		   =     1
																				else:
																											__lowerCamelCase					:		List[str]		   =     max(int(self.update_every / self.average_time_per_item    )							,					1    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			str=None    ):
													__lowerCamelCase					:		Dict		   =     " " * (len(str(self.total    )    ) - len(str(UpperCAmelCase    )    )) + str(UpperCAmelCase    )
													if self.elapsed_time is None:
																				__lowerCamelCase					:		List[str]		   =     F"""[{spaced_value}/{self.total} : < :"""
													elif self.predicted_remaining is None:
																				__lowerCamelCase					:		Tuple		   =     F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time    )}"""
													else:
																				__lowerCamelCase					:		int		   =     (
																				    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       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Any		   =     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:
																				__lowerCamelCase					:		Union[str, Any]		   =     disp.display(disp.HTML(self.html_code    )							,					display_id=UpperCAmelCase    )
													else:
																				self.output.update(disp.HTML(self.html_code    )    )







						def       lowerCamelCase__     (       self      :			List[Any]    ):
													if self.parent is None and self.output is not None:
																				self.output.update(disp.HTML(""    )    )

class  _snake_case     (					a__		):






						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]=None    ):
													super().__init__(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     None if column_names is None else [column_names]
													__lowerCamelCase					:		Dict		   =     None






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     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:
																				__lowerCamelCase					:		List[Any]		   =     disp.display(disp.HTML(self.html_code    )							,					display_id=UpperCAmelCase    )
													else:
																				self.output.update(disp.HTML(self.html_code    )    )






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			Union[str, Any]    ):
													if self.inner_table is None:
																				__lowerCamelCase					:		str		   =     [list(values.keys()    ), list(values.values()    )]
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     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    )
																											__lowerCamelCase					:		int		   =     columns
																				self.inner_table.append([values[c] for c in columns]    )






						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			str=300    ):
													__lowerCamelCase					:		Union[str, Any]		   =     NotebookProgressBar(UpperCAmelCase							,					prefix=UpperCAmelCase							,					parent=self							,					width=UpperCAmelCase    )
													return self.child_bar







						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		int		   =     None
													self.display()

class  _snake_case     (					a__		):






						def __init__(       self      :			List[Any]    ):
													__lowerCamelCase					:		Optional[Any]		   =     None
													__lowerCamelCase					:		int		   =     None
													__lowerCamelCase					:		Union[str, Any]		   =     False






						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Union[str, Any]							,					**UpperCAmelCase      :			Optional[Any]    ):
													__lowerCamelCase					:		List[str]		   =     "Epoch" if args.evaluation_strategy == IntervalStrategy.EPOCH else "Step"
													__lowerCamelCase					:		Dict		   =     0
													__lowerCamelCase					:		Tuple		   =     0
													__lowerCamelCase					:		Tuple		   =     [self.first_column] + ["Training Loss"]
													if args.evaluation_strategy != IntervalStrategy.NO:
																				column_names.append("Validation Loss"    )
													__lowerCamelCase					:		List[str]		   =     NotebookTrainingTracker(state.max_steps							,					UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Tuple							,					**UpperCAmelCase      :			Tuple    ):
													__lowerCamelCase					:		Optional[Any]		   =     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							,					)
													__lowerCamelCase					:		Union[str, Any]		   =     False






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Dict=None							,					**UpperCAmelCase      :			Union[str, Any]    ):
													if not has_length(UpperCAmelCase    ):
																				return
													if self.prediction_bar is None:
																				if self.training_tracker is not None:
																											__lowerCamelCase					:		Optional[Any]		   =     self.training_tracker.add_child(len(UpperCAmelCase    )    )
																				else:
																											__lowerCamelCase					:		Optional[int]		   =     NotebookProgressBar(len(UpperCAmelCase    )    )
																				self.prediction_bar.update(1    )
													else:
																				self.prediction_bar.update(self.prediction_bar.value + 1    )






						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			int							,					**UpperCAmelCase      :			List[str]    ):
													if self.prediction_bar is not None:
																				self.prediction_bar.close()
													__lowerCamelCase					:		List[Any]		   =     None






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[int]=None							,					**UpperCAmelCase      :			List[Any]    ):
													# Only for when there is no evaluation
													if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
																				__lowerCamelCase					:		str		   =     {"Training Loss": logs["loss"]}
																				# First column is necessarily Step sine we're not in epoch eval strategy
																				__lowerCamelCase					:		str		   =     state.global_step
																				self.training_tracker.write_line(UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Tuple=None							,					**UpperCAmelCase      :			Dict    ):
													if self.training_tracker is not None:
																				__lowerCamelCase					:		Any		   =     {"Training Loss": "No log", "Validation Loss": "No log"}
																				for log in reversed(state.log_history    ):
																											if "loss" in log:
																																		__lowerCamelCase					:		Dict		   =     log["loss"]
																																		break

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







						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Any							,					**UpperCAmelCase      :			Dict    ):
													self.training_tracker.update(
													    state.global_step							,					comment=F"""Epoch {int(state.epoch    )}/{state.num_train_epochs}"""							,					force_update=UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     None
"""simple docstring"""





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  _snake_case     (					a__		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			TransformeraDModel							,					UpperCAmelCase      :			AutoencoderKL							,					UpperCAmelCase      :			KarrasDiffusionSchedulers							,					UpperCAmelCase      :			Optional[Dict[int, str]] = None							,					):
													super().__init__()
													self.register_modules(transformer=UpperCAmelCase							,					vae=UpperCAmelCase							,					scheduler=UpperCAmelCase    )

													# create a imagenet -> id dictionary for easier use
													__lowerCamelCase					:		Optional[Any]		   =     {}
													if idalabel is not None:
																				for key, value in idalabel.items():
																											for label in value.split(","    ):
																																		__lowerCamelCase					:		List[str]		   =     int(UpperCAmelCase    )
																				__lowerCamelCase					:		Union[str, Any]		   =     dict(sorted(self.labels.items()    )    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Union[str, List[str]]    ):

													if not isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																				__lowerCamelCase					:		List[str]		   =     list(UpperCAmelCase    )

													for l in label:
																				if l not in self.labels:
																											raise ValueError(
																											    F"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""    )

													return [self.labels[l] for l in label]







						@torch.no_grad()
						def __call__(       self      :			int							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			float = 4.0							,					UpperCAmelCase      :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					UpperCAmelCase      :			int = 50							,					UpperCAmelCase      :			Optional[str] = "pil"							,					UpperCAmelCase      :			bool = True							,					):
													__lowerCamelCase					:		int		   =     len(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     self.transformer.config.sample_size
													__lowerCamelCase					:		Dict		   =     self.transformer.config.in_channels

													__lowerCamelCase					:		Any		   =     randn_tensor(
													    shape=(batch_size, latent_channels, latent_size, latent_size)							,					generator=UpperCAmelCase							,					device=self.device							,					dtype=self.transformer.dtype							,					)
													__lowerCamelCase					:		Optional[Any]		   =     torch.cat([latents] * 2    ) if guidance_scale > 1 else latents

													__lowerCamelCase					:		Optional[int]		   =     torch.tensor(UpperCAmelCase							,					device=self.device    ).reshape(-1    )
													__lowerCamelCase					:		Optional[int]		   =     torch.tensor([1000] * batch_size							,					device=self.device    )
													__lowerCamelCase					:		Any		   =     torch.cat([class_labels, class_null]							,					0    ) if guidance_scale > 1 else class_labels

													# set step values
													self.scheduler.set_timesteps(UpperCAmelCase    )

													for t in self.progress_bar(self.scheduler.timesteps    ):
																				if guidance_scale > 1:
																											__lowerCamelCase					:		str		   =     latent_model_input[: len(UpperCAmelCase    ) // 2]
																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([half, half]							,					dim=0    )
																				__lowerCamelCase					:		Dict		   =     self.scheduler.scale_model_input(UpperCAmelCase							,					UpperCAmelCase    )

																				__lowerCamelCase					:		Dict		   =     t
																				if not torch.is_tensor(UpperCAmelCase    ):
																											# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																											# This would be a good case for the `match` statement (Python 3.10+)
																											__lowerCamelCase					:		List[str]		   =     latent_model_input.device.type == "mps"
																											if isinstance(UpperCAmelCase							,					UpperCAmelCase    ):
																																		__lowerCamelCase					:		List[Any]		   =     torch.floataa if is_mps else torch.floataa
																											else:
																																		__lowerCamelCase					:		Optional[Any]		   =     torch.intaa if is_mps else torch.intaa
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([timesteps]							,					dtype=UpperCAmelCase							,					device=latent_model_input.device    )
																				elif len(timesteps.shape    ) == 0:
																											__lowerCamelCase					:		Union[str, Any]		   =     timesteps[None].to(latent_model_input.device    )
																				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																				__lowerCamelCase					:		List[Any]		   =     timesteps.expand(latent_model_input.shape[0]    )
																				# predict noise model_output
																				__lowerCamelCase					:		Union[str, Any]		   =     self.transformer(
																				    UpperCAmelCase							,					timestep=UpperCAmelCase							,					class_labels=UpperCAmelCase    ).sample

																				# perform guidance
																				if guidance_scale > 1:
																											__lowerCamelCase   ,  __lowerCamelCase					:		str		   =     noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																											__lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     torch.split(UpperCAmelCase							,					len(UpperCAmelCase    ) // 2							,					dim=0    )

																											__lowerCamelCase					:		List[str]		   =     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																											__lowerCamelCase					:		Union[str, Any]		   =     torch.cat([half_eps, half_eps]							,					dim=0    )

																											__lowerCamelCase					:		Optional[Any]		   =     torch.cat([eps, rest]							,					dim=1    )

																				# learned sigma
																				if self.transformer.config.out_channels // 2 == latent_channels:
																											__lowerCamelCase   ,  __lowerCamelCase					:		int		   =     torch.split(UpperCAmelCase							,					UpperCAmelCase							,					dim=1    )
																				else:
																											__lowerCamelCase					:		int		   =     noise_pred

																				# compute previous image: x_t -> x_t-1
																				__lowerCamelCase					:		Optional[Any]		   =     self.scheduler.step(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    ).prev_sample

													if guidance_scale > 1:
																				__lowerCamelCase   ,  __lowerCamelCase					:		List[str]		   =     latent_model_input.chunk(2							,					dim=0    )
													else:
																				__lowerCamelCase					:		Optional[Any]		   =     latent_model_input

													__lowerCamelCase					:		Tuple		   =     1 / self.vae.config.scaling_factor * latents
													__lowerCamelCase					:		Any		   =     self.vae.decode(UpperCAmelCase    ).sample

													__lowerCamelCase					:		Tuple		   =     (samples / 2 + 0.5).clamp(0							,					1    )

													# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
													__lowerCamelCase					:		Optional[int]		   =     samples.cpu().permute(0							,					2							,					3							,					1    ).float().numpy()

													if output_type == "pil":
																				__lowerCamelCase					:		Dict		   =     self.numpy_to_pil(UpperCAmelCase    )

													if not return_dict:
																				return (samples,)

													return ImagePipelineOutput(images=UpperCAmelCase    )