"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  _lowerCamelCase			(__lowerCAmelCase						,							unittest.TestCase     ):
				_snake_case     =  KandinskyVaaControlnetImgaImgPipeline
				_snake_case     =  ["image_embeds", "negative_image_embeds", "image", "hint"]
				_snake_case     =  ["image_embeds", "negative_image_embeds", "image", "hint"]
				_snake_case     =  [
				    "generator",
				    "height",
				    "width",
				    "strength",
				    "guidance_scale",
				    "num_inference_steps",
				    "return_dict",
				    "guidance_scale",
				    "num_images_per_prompt",
				    "output_type",
				    "return_dict",
				]
				_snake_case     =  False







				@property
				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					return 3_2







				@property
				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					return 3_2







				@property
				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					return self.time_input_dim







				@property
				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					return self.time_input_dim * 4







				@property
				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					return 1_0_0







				@property
				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					torch.manual_seed(0						)

					_lowercase :    Optional[Any]							=							{
					    """in_channels""": 8,
					    # Out channels is double in channels because predicts mean and variance
					    """out_channels""": 8,
					    """addition_embed_type""": """image_hint""",
					    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
					    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
					    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
					    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
					    """layers_per_block""": 1,
					    """encoder_hid_dim""": self.text_embedder_hidden_size,
					    """encoder_hid_dim_type""": """image_proj""",
					    """cross_attention_dim""": self.cross_attention_dim,
					    """attention_head_dim""": 4,
					    """resnet_time_scale_shift""": """scale_shift""",
					    """class_embed_type""": None,
					}

					_lowercase :    List[Any]							=							UNetaDConditionModel(**_UpperCamelCase						)
					return model







				@property
				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					return {
					    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
					    "down_block_types": [
					        "DownEncoderBlock2D",
					        "DownEncoderBlock2D",
					        "DownEncoderBlock2D",
					        "AttnDownEncoderBlock2D",
					    ],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 1_2,
					    "out_channels": 3,
					    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
					    "vq_embed_dim": 4,
					}







				@property
				def 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					torch.manual_seed(0						)
					_lowercase :    List[Any]							=							VQModel(**self.dummy_movq_kwargs						)
					return model







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							self.dummy_unet
					_lowercase :    Dict							=							self.dummy_movq

					_lowercase :    List[str]							=							{
					    """num_train_timesteps""": 1_0_0_0,
					    """beta_schedule""": """linear""",
					    """beta_start""": 0.0_0085,
					    """beta_end""": 0.012,
					    """clip_sample""": False,
					    """set_alpha_to_one""": False,
					    """steps_offset""": 0,
					    """prediction_type""": """epsilon""",
					    """thresholding""": False,
					}

					_lowercase :    Dict							=							DDIMScheduler(**_UpperCamelCase						)

					_lowercase :    Optional[Any]							=							{
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Any=0						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							floats_tensor((1, self.text_embedder_hidden_size)			,					rng=random.Random(_UpperCamelCase						)						).to(_UpperCamelCase						)
					_lowercase :    str							=							floats_tensor((1, self.text_embedder_hidden_size)			,					rng=random.Random(seed + 1						)						).to(
					    _UpperCamelCase						)
					# create init_image
					_lowercase :    int							=							floats_tensor((1, 3, 6_4, 6_4)			,					rng=random.Random(_UpperCamelCase						)						).to(_UpperCamelCase						)
					_lowercase :    Optional[Any]							=							image.cpu().permute(0			,					2			,					3			,					1						)[0]
					_lowercase :    int							=							Image.fromarray(np.uinta(_UpperCamelCase						)						).convert('RGB'						).resize((2_5_6, 2_5_6)						)
					# create hint
					_lowercase :    List[Any]							=							floats_tensor((1, 3, 6_4, 6_4)			,					rng=random.Random(_UpperCamelCase						)						).to(_UpperCamelCase						)

					if str(_UpperCamelCase						).startswith('mps'						):
						_lowercase :    Any							=							torch.manual_seed(_UpperCamelCase						)
					else:
						_lowercase :    Any							=							torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
					_lowercase :    List[Any]							=							{
					    """image""": init_image,
					    """image_embeds""": image_embeds,
					    """negative_image_embeds""": negative_image_embeds,
					    """hint""": hint,
					    """generator""": generator,
					    """height""": 6_4,
					    """width""": 6_4,
					    """num_inference_steps""": 1_0,
					    """guidance_scale""": 7.0,
					    """strength""": 0.2,
					    """output_type""": """np""",
					}
					return inputs







				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    str							=							"""cpu"""

					_lowercase :    Tuple							=							self.get_dummy_components()

					_lowercase :    Union[str, Any]							=							self.pipeline_class(**_UpperCamelCase						)
					_lowercase :    str							=							pipe.to(_UpperCamelCase						)

					pipe.set_progress_bar_config(disable=_UpperCamelCase						)

					_lowercase :    List[str]							=							pipe(**self.get_dummy_inputs(_UpperCamelCase						)						)
					_lowercase :    Any							=							output.images

					_lowercase :    Dict							=							pipe(
					    **self.get_dummy_inputs(_UpperCamelCase						)			,					return_dict=_UpperCamelCase			,					)[0]

					_lowercase :    Optional[int]							=							image[0, -3:, -3:, -1]
					_lowercase :    int							=							image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 6_4, 6_4, 3)

					_lowercase :    str							=							np.array(
					    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]						)
					assert (
					    np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
					), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
					), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



@slow
@require_torch_gpu
class  _lowerCamelCase			(unittest.TestCase     ):







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    Union[str, Any]							=							load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'						)

					_lowercase :    Any							=							load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
					_lowercase :    Optional[int]							=							init_image.resize((5_1_2, 5_1_2)						)

					_lowercase :    Dict							=							load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/kandinskyv22/hint_image_cat.png'						)
					_lowercase :    int							=							torch.from_numpy(np.array(_UpperCamelCase						)						).float() / 2_5_5.0
					_lowercase :    Dict							=							hint.permute(2			,					0			,					1						).unsqueeze(0						)

					_lowercase :    Tuple							=							"""A robot, 4k photo"""

					_lowercase :    Optional[int]							=							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-2-prior'			,					torch_dtype=torch.floataa						)
					pipe_prior.to(_UpperCamelCase						)

					_lowercase :    str							=							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
					    'kandinsky-community/kandinsky-2-2-controlnet-depth'			,					torch_dtype=torch.floataa						)
					_lowercase :    List[str]							=							pipeline.to(_UpperCamelCase						)

					pipeline.set_progress_bar_config(disable=_UpperCamelCase						)

					_lowercase :    str							=							torch.Generator(device='cpu'						).manual_seed(0						)

					_lowercase :    Union[str, Any]							=							pipe_prior(
					    _UpperCamelCase			,					image=_UpperCamelCase			,					strength=0.85			,					generator=_UpperCamelCase			,					negative_prompt=''			,					).to_tuple()

					_lowercase :    Optional[Any]							=							pipeline(
					    image=_UpperCamelCase			,					image_embeds=_UpperCamelCase			,					negative_image_embeds=_UpperCamelCase			,					hint=_UpperCamelCase			,					generator=_UpperCamelCase			,					num_inference_steps=1_0_0			,					height=5_1_2			,					width=5_1_2			,					strength=0.5			,					output_type='np'			,					)

					_lowercase :    List[Any]							=							output.images[0]

					assert image.shape == (5_1_2, 5_1_2, 3)

					assert_mean_pixel_difference(_UpperCamelCase			,					_UpperCamelCase						)




"""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(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Optional[int]							=							int(__UpperCAmelCase			)
	_lowercase				,      _lowercase				,      _lowercase :    Union[str, Any]							=							t // 3_600, (t // 60) % 60, t % 60
	return F'''{h}:{m:02d}:{s:02d}''' if h != 0 else F'''{m:02d}:{s:02d}'''


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase=300			):






	"""simple docstring"""





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


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[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:
			_lowercase :    Optional[Any]							=							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  _lowerCamelCase			:
				_snake_case     =  5
				_snake_case     =  0.2







				def __init__( self    :       List[str]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       Optional[str] = None			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Optional["NotebookTrainingTracker"] = None			,					lowerCamelCase_    :       int = 3_0_0			,					):

					"""simple docstring"""






					_lowercase :    int							=							total
					_lowercase :    Dict							=							'' if prefix is None else prefix
					_lowercase :    List[Any]							=							leave
					_lowercase :    Dict							=							parent
					_lowercase :    str							=							width
					_lowercase :    Any							=							None
					_lowercase :    Optional[int]							=							None
					_lowercase :    Union[str, Any]							=							None







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       bool = False			,					lowerCamelCase_    :       str = None						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       str			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       Optional[Any]=None						):

					"""simple docstring"""






					_lowercase :    str							=							' ' * (len(str(self.total						)						) - len(str(lowerCamelCase_						)						)) + str(lowerCamelCase_						)
					if self.elapsed_time is None:
						_lowercase :    Dict							=							F'''[{spaced_value}/{self.total} : < :'''
					elif self.predicted_remaining is None:
						_lowercase :    Optional[int]							=							F'''[{spaced_value}/{self.total} {format_time(self.elapsed_time						)}'''
					else:
						_lowercase :    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 							__UpperCAmelCase					( self    :       int						):

					"""simple docstring"""






					_lowercase :    int							=							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:
						_lowercase :    List[str]							=							disp.display(disp.HTML(self.html_code						)			,					display_id=lowerCamelCase_						)
					else:
						self.output.update(disp.HTML(self.html_code						)						)







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






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



class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Optional[int]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Tuple=None						):

					"""simple docstring"""






					super().__init__(lowerCamelCase_						)
					_lowercase :    Tuple							=							None if column_names is None else [column_names]
					_lowercase :    Optional[int]							=							None







				def 							__UpperCAmelCase					( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    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:
						_lowercase :    Dict							=							disp.display(disp.HTML(self.html_code						)			,					display_id=lowerCamelCase_						)
					else:
						self.output.update(disp.HTML(self.html_code						)						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Optional[int]						):

					"""simple docstring"""






					if self.inner_table is None:
						_lowercase :    Optional[int]							=							[list(values.keys()						), list(values.values()						)]
					else:
						_lowercase :    str							=							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(lowerCamelCase_						)
							_lowercase :    List[Any]							=							columns
						self.inner_table.append([values[c] for c in columns]						)







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       Union[str, Any]=None			,					lowerCamelCase_    :       int=3_0_0						):

					"""simple docstring"""






					_lowercase :    List[str]							=							NotebookProgressBar(lowerCamelCase_			,					prefix=lowerCamelCase_			,					parent=self			,					width=lowerCamelCase_						)
					return self.child_bar







				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    Any							=							None
					self.display()



class  _lowerCamelCase			(__lowerCamelCase     ):







				def __init__( self    :       Optional[Any]						):

					"""simple docstring"""






					_lowercase :    int							=							None
					_lowercase :    Any							=							None
					_lowercase :    Optional[int]							=							False







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    Any							=							'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
					_lowercase :    Dict							=							0
					_lowercase :    Optional[Any]							=							0
					_lowercase :    Union[str, Any]							=							[self.first_column] + ['Training Loss']
					if args.evaluation_strategy != IntervalStrategy.NO:
						column_names.append('Validation Loss'						)
					_lowercase :    Tuple							=							NotebookTrainingTracker(state.max_steps			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Union[str, Any]			,					lowerCamelCase_    :       Any			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Union[str, Any]			,					**lowerCamelCase_    :       Dict						):

					"""simple docstring"""






					_lowercase :    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			,					)
					_lowercase :    Tuple							=							False







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Union[str, Any]=None			,					**lowerCamelCase_    :       str						):

					"""simple docstring"""






					if not has_length(lowerCamelCase_						):
						return
					if self.prediction_bar is None:
						if self.training_tracker is not None:
							_lowercase :    Tuple							=							self.training_tracker.add_child(len(lowerCamelCase_						)						)
						else:
							_lowercase :    Optional[Any]							=							NotebookProgressBar(len(lowerCamelCase_						)						)
						self.prediction_bar.update(1						)
					else:
						self.prediction_bar.update(self.prediction_bar.value + 1						)







				def 							__UpperCAmelCase					( self    :       Tuple			,					lowerCamelCase_    :       Dict			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Dict			,					**lowerCamelCase_    :       List[str]						):

					"""simple docstring"""






					if self.prediction_bar is not None:
						self.prediction_bar.close()
					_lowercase :    Union[str, Any]							=							None







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       int=None			,					**lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       int			,					lowerCamelCase_    :       List[Any]			,					lowerCamelCase_    :       int			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       str=None			,					**lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






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

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







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       Tuple			,					lowerCamelCase_    :       Optional[Any]			,					lowerCamelCase_    :       Union[str, Any]			,					**lowerCamelCase_    :       Any						):

					"""simple docstring"""






					self.training_tracker.update(
					    state.global_step			,					comment=F'''Epoch {int(state.epoch						)}/{state.num_train_epochs}'''			,					force_update=lowerCamelCase_						)
					_lowercase :    List[Any]							=							None



