





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   (				__a  :int       )						->							List[str]:






		"""simple docstring"""

		A__     =  int(__a       )
		A__ ,     A__ ,     A__     =  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   (				__a  :str       ,	__a  :int       ,	__a  :Dict       ,	__a  :Optional[int]       ,	__a  :str=3_0_0       )						->							List[Any]:






		"""simple docstring"""

		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   (				__a  :Dict       )						->							str:






		"""simple docstring"""

		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(__a       ,	__a       ) else str(__a       )
						html_code += F'      <td>{elt}</td>\n'
				html_code += "    </tr>\n"
		html_code += "  </tbody>\n</table><p>"
		return html_code


class     A     :
			'''simple docstring'''




			__lowerCamelCase      :			int							     =       5
			__lowerCamelCase      :			Dict							     =       0.2






			def __init__(     self  :       List[str]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       Optional[str] = None				,      __lowerCAmelCase  :       bool = True				,      __lowerCAmelCase  :       Optional["NotebookTrainingTracker"] = None				,      __lowerCAmelCase  :       int = 3_00				,      )      ->      Optional[int]:







					"""simple docstring"""






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






			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       bool = False				,      __lowerCAmelCase  :       str = None  )      ->      List[str]:







					"""simple docstring"""






					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(__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
							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(__lowerCAmelCase  )
							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   a_			(     self  :       Optional[Any]				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]=None  )      ->      List[Any]:







					"""simple docstring"""






					A__     =  """ """ * (len(str(self.total  )  ) - len(str(__lowerCAmelCase  )  )) + str(__lowerCAmelCase  )
					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   a_			(     self  :       Tuple  )      ->      Optional[int]:







					"""simple docstring"""






					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=__lowerCAmelCase  )
					else:
							self.output.update(disp.HTML(self.html_code  )  )







			def   a_			(     self  :       Union[str, Any]  )      ->      Union[str, Any]:







					"""simple docstring"""






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


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




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







					"""simple docstring"""






					super().__init__(__lowerCAmelCase  )
					A__     =  None if column_names is None else [column_names]
					A__     =  None






			def   a_			(     self  :       Any  )      ->      Optional[Any]:







					"""simple docstring"""






					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=__lowerCAmelCase  )
					else:
							self.output.update(disp.HTML(self.html_code  )  )






			def   a_			(     self  :       Optional[Any]				,      __lowerCAmelCase  :       Union[str, Any]  )      ->      Tuple:







					"""simple docstring"""






					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(__lowerCAmelCase  )
									A__     =  columns
							self.inner_table.append([values[c] for c in columns]  )






			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       Optional[Any]=None				,      __lowerCAmelCase  :       str=3_00  )      ->      Any:







					"""simple docstring"""






					A__     =  NotebookProgressBar(__lowerCAmelCase				,      prefix=__lowerCAmelCase				,      parent=self				,      width=__lowerCAmelCase  )
					return self.child_bar







			def   a_			(     self  :       Optional[Any]  )      ->      Dict:







					"""simple docstring"""






					A__     =  None
					self.display()


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			def __init__(     self  :       Union[str, Any]  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  None
					A__     =  None
					A__     =  False






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       List[Any]				,      **__lowerCAmelCase  :       Union[str, Any]  )      ->      List[str]:







					"""simple docstring"""






					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				,      __lowerCAmelCase  )






			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Tuple				,      __lowerCAmelCase  :       Optional[Any]				,      __lowerCAmelCase  :       Union[str, Any]				,      **__lowerCAmelCase  :       List[str]  )      ->      List[str]:







					"""simple docstring"""






					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   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       Optional[int]				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       Tuple				,      __lowerCAmelCase  :       List[str]=None				,      **__lowerCAmelCase  :       Tuple  )      ->      Any:







					"""simple docstring"""






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






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       Optional[Any]				,      __lowerCAmelCase  :       Dict				,      **__lowerCAmelCase  :       Optional[Any]  )      ->      List[Any]:







					"""simple docstring"""






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






			def   a_			(     self  :       Tuple				,      __lowerCAmelCase  :       Tuple				,      __lowerCAmelCase  :       List[Any]				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       Any=None				,      **__lowerCAmelCase  :       List[Any]  )      ->      Any:







					"""simple docstring"""






					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(__lowerCAmelCase  )






			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Dict				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Optional[Any]=None				,      **__lowerCAmelCase  :       List[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					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$"""				,      """"""				,      __lowerCAmelCase  )
							A__     =  metrics.pop("""total_flos"""				,      __lowerCAmelCase  )
							A__     =  metrics.pop("""epoch"""				,      __lowerCAmelCase  )
							A__     =  metrics.pop(f'{metric_key_prefix}_runtime'				,      __lowerCAmelCase  )
							A__     =  metrics.pop(f'{metric_key_prefix}_samples_per_second'				,      __lowerCAmelCase  )
							A__     =  metrics.pop(f'{metric_key_prefix}_steps_per_second'				,      __lowerCAmelCase  )
							A__     =  metrics.pop(f'{metric_key_prefix}_jit_compilation_time'				,      __lowerCAmelCase  )
							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(__lowerCAmelCase  )
							self.training_tracker.remove_child()
							A__     =  None
							# Evaluation takes a long time so we should force the next update.
							A__     =  True







			def   a_			(     self  :       Tuple				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Optional[Any]				,      __lowerCAmelCase  :       Union[str, Any]				,      **__lowerCAmelCase  :       str  )      ->      Any:







					"""simple docstring"""






					self.training_tracker.update(
					    state.global_step				,      comment=f'Epoch {int(state.epoch  )}/{state.num_train_epochs}'				,      force_update=__lowerCAmelCase  )
					A__     =  None










from ...configuration_utils import PretrainedConfig
from ...utils import logging


A       :   Optional[int]					= logging.get_logger(__name__)

A       :   Dict					= {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       '''glpn'''






			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Union[str, Any]=3				,      __lowerCAmelCase  :       Any=4				,      __lowerCAmelCase  :       Tuple=[2, 2, 2, 2]				,      __lowerCAmelCase  :       int=[8, 4, 2, 1]				,      __lowerCAmelCase  :       int=[32, 64, 1_60, 2_56]				,      __lowerCAmelCase  :       str=[7, 3, 3, 3]				,      __lowerCAmelCase  :       Dict=[4, 2, 2, 2]				,      __lowerCAmelCase  :       List[str]=[1, 2, 5, 8]				,      __lowerCAmelCase  :       List[Any]=[4, 4, 4, 4]				,      __lowerCAmelCase  :       str="gelu"				,      __lowerCAmelCase  :       Union[str, Any]=0.0				,      __lowerCAmelCase  :       Tuple=0.0				,      __lowerCAmelCase  :       List[Any]=0.0_2				,      __lowerCAmelCase  :       Dict=0.1				,      __lowerCAmelCase  :       Dict=1e-6				,      __lowerCAmelCase  :       Tuple=64				,      __lowerCAmelCase  :       Tuple=10				,      __lowerCAmelCase  :       Union[str, Any]=-1				,      **__lowerCAmelCase  :       Any				,      )      ->      Tuple:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase  )

					A__     =  num_channels
					A__     =  num_encoder_blocks
					A__     =  depths
					A__     =  sr_ratios
					A__     =  hidden_sizes
					A__     =  patch_sizes
					A__     =  strides
					A__     =  mlp_ratios
					A__     =  num_attention_heads
					A__     =  hidden_act
					A__     =  hidden_dropout_prob
					A__     =  attention_probs_dropout_prob
					A__     =  initializer_range
					A__     =  drop_path_rate
					A__     =  layer_norm_eps
					A__     =  decoder_hidden_size
					A__     =  max_depth
					A__     =  head_in_index



