




'''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   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





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

								# create a imagenet -> id dictionary for easier use
								A_									= {}
								if idalabel is not None:
											for key, value in idalabel.items():
														for label in value.split(','):
																	A_									= int(_lowercase)
											A_									= dict(sorted(self.labels.items()))



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, List[str]])    ->							List[int]:

								if not isinstance(_lowercase      ,       _lowercase):
											A_									= list(_lowercase)

								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     :						Union[str, Any]      ,       _lowercase     :						List[int]      ,       _lowercase     :						float = 4.0      ,       _lowercase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,       _lowercase     :						int = 50      ,       _lowercase     :						Optional[str] = "pil"      ,       _lowercase     :						bool = True      ,       )    ->							Union[ImagePipelineOutput, Tuple]:
								A_									= len(_lowercase)
								A_									= self.transformer.config.sample_size
								A_									= self.transformer.config.in_channels

								A_									= randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)      ,       generator=_lowercase      ,       device=self.device      ,       dtype=self.transformer.dtype      ,       )
								A_									= torch.cat([latents] * 2) if guidance_scale > 1 else latents

								A_									= torch.tensor(_lowercase      ,       device=self.device).reshape(-1)
								A_									= torch.tensor([1_000] * batch_size      ,       device=self.device)
								A_									= torch.cat([class_labels, class_null]      ,       0) if guidance_scale > 1 else class_labels

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

								for t in self.progress_bar(self.scheduler.timesteps):
											if guidance_scale > 1:
														A_									= latent_model_input[: len(_lowercase) // 2]
														A_									= torch.cat([half, half]      ,       dim=0)
											A_									= self.scheduler.scale_model_input(_lowercase      ,       _lowercase)

											A_									= t
											if not torch.is_tensor(_lowercase):
														# 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+)
														A_									= latent_model_input.device.type == 'mps'
														if isinstance(_lowercase      ,       _lowercase):
																	A_									= torch.floataa if is_mps else torch.floataa
														else:
																	A_									= torch.intaa if is_mps else torch.intaa
														A_									= torch.tensor([timesteps]      ,       dtype=_lowercase      ,       device=latent_model_input.device)
											elif len(timesteps.shape) == 0:
														A_									= timesteps[None].to(latent_model_input.device)
											# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
											A_									= timesteps.expand(latent_model_input.shape[0])
											# predict noise model_output
											A_									= self.transformer(
											    _lowercase      ,       timestep=_lowercase      ,       class_labels=_lowercase).sample

											# perform guidance
											if guidance_scale > 1:
														A_		, A_									= noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
														A_		, A_									= torch.split(_lowercase      ,       len(_lowercase) // 2      ,       dim=0)

														A_									= uncond_eps + guidance_scale * (cond_eps - uncond_eps)
														A_									= torch.cat([half_eps, half_eps]      ,       dim=0)

														A_									= torch.cat([eps, rest]      ,       dim=1)

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

											# compute previous image: x_t -> x_t-1
											A_									= self.scheduler.step(_lowercase      ,       _lowercase      ,       _lowercase).prev_sample

								if guidance_scale > 1:
											A_		, A_									= latent_model_input.chunk(2      ,       dim=0)
								else:
											A_									= latent_model_input

								A_									= 1 / self.vae.config.scaling_factor * latents
								A_									= self.vae.decode(_lowercase).sample

								A_									= (samples / 2 + 0.5).clamp(0      ,       1)

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

								if output_type == "pil":
											A_									= self.numpy_to_pil(_lowercase)

								if not return_dict:
											return (samples,)

								return ImagePipelineOutput(images=_lowercase)










'''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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		str:
			A_									= int(SCREAMING_SNAKE_CASE_     )
			A_		, A_		, A_									= t // 3600, (t // 60) % 60, t % 60
			return F'{h}:{m:02d}:{s:02d}' if h != 0 else F'{m:02d}:{s:02d}'





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_=300     )		->		Dict:
			# 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(							SCREAMING_SNAKE_CASE_     )		->		List[str]:
			A_									= '<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:
									A_									= F'{elt:.6f}' if isinstance(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     ) else str(SCREAMING_SNAKE_CASE_     )
									html_code += F'      <td>{elt}</td>\n'
						html_code += "    </tr>\n"
			html_code += "  </tbody>\n</table><p>"
			return html_code
class   __UpperCAmelCase				:



					'''simple docstring'''





					_UpperCamelCase							=		5
					_UpperCamelCase							=		0.2



					def __init__(	self     :						Tuple      ,       _lowercase     :						int      ,       _lowercase     :						Optional[str] = None      ,       _lowercase     :						bool = True      ,       _lowercase     :						Optional["NotebookTrainingTracker"] = None      ,       _lowercase     :						int = 300      ,       )    ->							Dict:
								A_									= total
								A_									= '' if prefix is None else prefix
								A_									= leave
								A_									= parent
								A_									= width
								A_									= None
								A_									= None
								A_									= None



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



					def     __snake_case						(	self     :						str      ,       _lowercase     :						List[Any]      ,       _lowercase     :						Optional[int]=None)    ->							str:
								A_									= ' ' * (len(str(self.total)) - len(str(_lowercase))) + str(_lowercase)
								if self.elapsed_time is None:
											A_									= F'[{spaced_value}/{self.total} : < :'
								elif self.predicted_remaining is None:
											A_									= F'[{spaced_value}/{self.total} {format_time(self.elapsed_time)}'
								else:
											A_									= (
											    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     :						Optional[int])    ->							Dict:
								A_									= 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:
											A_									= disp.display(disp.HTML(self.html_code)      ,       display_id=_lowercase)
								else:
											self.output.update(disp.HTML(self.html_code))



					def     __snake_case						(	self     :						List[Any])    ->							Optional[Any]:
								if self.parent is None and self.output is not None:
											self.output.update(disp.HTML(''))





class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						int      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						str=None)    ->							str:
								super().__init__(_lowercase)
								A_									= None if column_names is None else [column_names]
								A_									= None



					def     __snake_case						(	self     :						Dict)    ->							List[Any]:
								A_									= 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:
											A_									= disp.display(disp.HTML(self.html_code)      ,       display_id=_lowercase)
								else:
											self.output.update(disp.HTML(self.html_code))



					def     __snake_case						(	self     :						str      ,       _lowercase     :						int)    ->							Optional[int]:
								if self.inner_table is None:
											A_									= [list(values.keys()), list(values.values())]
								else:
											A_									= 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(_lowercase)
														A_									= columns
											self.inner_table.append([values[c] for c in columns])



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						str=None      ,       _lowercase     :						Optional[int]=300)    ->							Dict:
								A_									= NotebookProgressBar(_lowercase      ,       prefix=_lowercase      ,       parent=self      ,       width=_lowercase)
								return self.child_bar



					def     __snake_case						(	self     :						Any)    ->							int:
								A_									= None
								self.display()





class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					def __init__(	self     :						Dict)    ->							Tuple:
								A_									= None
								A_									= None
								A_									= False



					def     __snake_case						(	self     :						int      ,       _lowercase     :						str      ,       _lowercase     :						str      ,       _lowercase     :						str      ,       **_lowercase     :						List[Any])    ->							List[Any]:
								A_									= 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
								A_									= 0
								A_									= 0
								A_									= [self.first_column] + ['Training Loss']
								if args.evaluation_strategy != IntervalStrategy.NO:
											column_names.append('Validation Loss')
								A_									= NotebookTrainingTracker(state.max_steps      ,       _lowercase)



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						Union[str, Any]      ,       _lowercase     :						List[Any]      ,       _lowercase     :						List[str]      ,       **_lowercase     :						Union[str, Any])    ->							str:
								A_									= 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      ,       )
								A_									= False



					def     __snake_case						(	self     :						Union[str, Any]      ,       _lowercase     :						Any      ,       _lowercase     :						int      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						List[Any]=None      ,       **_lowercase     :						Any)    ->							Union[str, Any]:
								if not has_length(_lowercase):
											return
								if self.prediction_bar is None:
											if self.training_tracker is not None:
														A_									= self.training_tracker.add_child(len(_lowercase))
											else:
														A_									= NotebookProgressBar(len(_lowercase))
											self.prediction_bar.update(1)
								else:
											self.prediction_bar.update(self.prediction_bar.value + 1)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						List[str]      ,       _lowercase     :						Any      ,       _lowercase     :						Union[str, Any]      ,       **_lowercase     :						Optional[int])    ->							Optional[Any]:
								if self.prediction_bar is not None:
											self.prediction_bar.close()
								A_									= None



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



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Any      ,       _lowercase     :						Optional[int]      ,       _lowercase     :						str      ,       _lowercase     :						Optional[Any]=None      ,       **_lowercase     :						Union[str, Any])    ->							Union[str, Any]:
								if self.training_tracker is not None:
											A_									= {'Training Loss': 'No log', 'Validation Loss': 'No log'}
											for log in reversed(state.log_history):
														if "loss" in log:
																	A_									= log['loss']
																	break

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



					def     __snake_case						(	self     :						Tuple      ,       _lowercase     :						Dict      ,       _lowercase     :						str      ,       _lowercase     :						List[str]      ,       **_lowercase     :						str)    ->							Optional[int]:
								self.training_tracker.update(
								    state.global_step      ,       comment=F'Epoch {int(state.epoch)}/{state.num_train_epochs}'      ,       force_update=_lowercase)
								A_									= None




