




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						(						_lowerCAmelCase							:      Optional[int]		):




 """simple docstring"""







 UpperCAmelCase__							       =  int(_lowerCAmelCase		)
 UpperCAmelCase__							       =  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						(						_lowerCAmelCase							:      Optional[int]  ,							_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Any  ,							_lowerCAmelCase							:      Optional[int]  ,							_lowerCAmelCase							:      int=300		):




 """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						(						_lowerCAmelCase							:      Dict		):




 """simple docstring"""







 UpperCAmelCase__							       =  """<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:
   UpperCAmelCase__							       =  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 	_UpperCamelCase					:
      UpperCAmelCase_						=						5
      UpperCAmelCase_						=						0.2
      def __init__(	self      :List[str]       ,					lowerCamelCase      :Dict       ,					lowerCamelCase      :Optional[int] = None       ,					lowerCamelCase      :List[Any] = True       ,					lowerCamelCase      :List[Any] = None       ,					lowerCamelCase      :int = 300       ,					)   ->	Dict:
       UpperCAmelCase__							       =  total
       UpperCAmelCase__							       =  """""" if prefix is None else prefix
       UpperCAmelCase__							       =  leave
       UpperCAmelCase__							       =  parent
       UpperCAmelCase__							       =  width
       UpperCAmelCase__							       =  None
       UpperCAmelCase__							       =  None
       UpperCAmelCase__							       =  None
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :int       ,					lowerCamelCase      :Any = False       ,					lowerCamelCase      :List[str] = None					)   ->	Optional[Any]:
       UpperCAmelCase__							       =  value
       if comment is not None:
        UpperCAmelCase__							       =  comment
       if self.last_value is None:
        UpperCAmelCase__							       =  time.time()
        UpperCAmelCase__							       =  value
        UpperCAmelCase__							       =  None
        UpperCAmelCase__							       =  self.warmup
        UpperCAmelCase__							       =  1
        self.update_bar(_SCREAMING_SNAKE_CASE					)
       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
        UpperCAmelCase__							       =  time.time()
        UpperCAmelCase__							       =  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:
         UpperCAmelCase__							       =  self.elapsed_time / (value - self.start_value)
        else:
         UpperCAmelCase__							       =  None
        if value >= self.total:
         UpperCAmelCase__							       =  self.total
         UpperCAmelCase__							       =  None
         if not self.leave:
          self.close()
        elif self.average_time_per_item is not None:
         UpperCAmelCase__							       =  self.average_time_per_item * (self.total - value)
        self.update_bar(_SCREAMING_SNAKE_CASE					)
        UpperCAmelCase__							       =  value
        UpperCAmelCase__							       =  current_time
        if self.average_time_per_item is None:
         UpperCAmelCase__							       =  1
        else:
         UpperCAmelCase__							       =  max(int(self.update_every / self.average_time_per_item					)       ,					1					)
      def 		UpperCAmelCase_  (	self      :Any       ,					lowerCamelCase      :int       ,					lowerCamelCase      :List[Any]=None					)   ->	List[Any]:
       UpperCAmelCase__							       =  """ """ * (len(str(self.total					)					) - len(str(_SCREAMING_SNAKE_CASE					)					)) + str(_SCREAMING_SNAKE_CASE					)
       if self.elapsed_time is None:
        UpperCAmelCase__							       =  f'''[{spaced_value}/{self.total} : < :'''
       elif self.predicted_remaining is None:
        UpperCAmelCase__							       =  f'''[{spaced_value}/{self.total} {format_time(self.elapsed_time					)}'''
       else:
        UpperCAmelCase__							       =  (
            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      :Any					)   ->	int:
       UpperCAmelCase__							       =  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:
        UpperCAmelCase__							       =  disp.display(disp.HTML(self.html_code					)       ,					display_id=_SCREAMING_SNAKE_CASE					)
       else:
        self.output.update(disp.HTML(self.html_code					)					)







      def 		UpperCAmelCase_  (	self      :Dict					)   ->	List[Any]:




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



class 	_UpperCamelCase					( UpperCAmelCase__						):
      def __init__(	self      :int       ,					lowerCamelCase      :Tuple       ,					lowerCamelCase      :List[Any]=None					)   ->	str:
       super().__init__(_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  None if column_names is None else [column_names]
       UpperCAmelCase__							       =  None
      def 		UpperCAmelCase_  (	self      :Optional[int]					)   ->	List[str]:
       UpperCAmelCase__							       =  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:
        UpperCAmelCase__							       =  disp.display(disp.HTML(self.html_code					)       ,					display_id=_SCREAMING_SNAKE_CASE					)
       else:
        self.output.update(disp.HTML(self.html_code					)					)
      def 		UpperCAmelCase_  (	self      :Any       ,					lowerCamelCase      :Optional[int]					)   ->	Optional[Any]:




       if self.inner_table is None:
        UpperCAmelCase__							       =  [list(values.keys()					), list(values.values()					)]
       else:
        UpperCAmelCase__							       =  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(_SCREAMING_SNAKE_CASE					)
         UpperCAmelCase__							       =  columns
        self.inner_table.append([values[c] for c in columns]					)
      def 		UpperCAmelCase_  (	self      :List[Any]       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :Dict=None       ,					lowerCamelCase      :Optional[int]=300					)   ->	List[Any]:
       UpperCAmelCase__							       =  NotebookProgressBar(_SCREAMING_SNAKE_CASE       ,					prefix=_SCREAMING_SNAKE_CASE       ,					parent=self       ,					width=_SCREAMING_SNAKE_CASE					)
       return self.child_bar







      def 		UpperCAmelCase_  (	self      :Optional[Any]					)   ->	Optional[Any]:
       UpperCAmelCase__							       =  None
       self.display()



class 	_UpperCamelCase					( UpperCAmelCase__						):
      def __init__(	self      :List[Any]					)   ->	Tuple:
       UpperCAmelCase__							       =  None
       UpperCAmelCase__							       =  None
       UpperCAmelCase__							       =  False
      def 		UpperCAmelCase_  (	self      :Dict       ,					lowerCamelCase      :int       ,					lowerCamelCase      :Dict       ,					lowerCamelCase      :int       ,					**lowerCamelCase      :List[Any]					)   ->	List[Any]:
       UpperCAmelCase__							       =  """Epoch""" if args.evaluation_strategy == IntervalStrategy.EPOCH else """Step"""
       UpperCAmelCase__							       =  0
       UpperCAmelCase__							       =  0
       UpperCAmelCase__							       =  [self.first_column] + ["""Training Loss"""]
       if args.evaluation_strategy != IntervalStrategy.NO:
        column_names.append("Validation Loss"					)
       UpperCAmelCase__							       =  NotebookTrainingTracker(state.max_steps       ,					_SCREAMING_SNAKE_CASE					)
      def 		UpperCAmelCase_  (	self      :str       ,					lowerCamelCase      :Optional[Any]       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :Tuple       ,					**lowerCamelCase      :str					)   ->	int:
       UpperCAmelCase__							       =  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       ,					)
       UpperCAmelCase__							       =  False
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :Union[str, Any]       ,					lowerCamelCase      :Union[str, Any]       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :List[Any]=None       ,					**lowerCamelCase      :int					)   ->	Optional[int]:




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




       if self.prediction_bar is not None:
        self.prediction_bar.close()
       UpperCAmelCase__							       =  None
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :Optional[Any]       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :Dict       ,					lowerCamelCase      :Optional[int]=None       ,					**lowerCamelCase      :str					)   ->	str:




       if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
        UpperCAmelCase__							       =  {"""Training Loss""": logs["""loss"""]}
        # First column is necessarily Step sine we're not in epoch eval strategy
        UpperCAmelCase__							       =  state.global_step
        self.training_tracker.write_line(_SCREAMING_SNAKE_CASE					)
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :List[Any]       ,					lowerCamelCase      :str       ,					lowerCamelCase      :Tuple       ,					lowerCamelCase      :Union[str, Any]=None       ,					**lowerCamelCase      :Dict					)   ->	str:




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

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







      def 		UpperCAmelCase_  (	self      :int       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :List[Any]       ,					**lowerCamelCase      :Optional[Any]					)   ->	Tuple:
       self.training_tracker.update(
           state.global_step       ,					comment=f'''Epoch {int(state.epoch					)}/{state.num_train_epochs}'''       ,					force_update=_SCREAMING_SNAKE_CASE					)
       UpperCAmelCase__							       =  None










"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


A:   List[str]								=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Tuple								=		["DPTFeatureExtractor"]
      A:   int								=		["DPTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[Any]								=		[
          "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPTForDepthEstimation",
          "DPTForSemanticSegmentation",
          "DPTModel",
          "DPTPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_dpt import DPTFeatureExtractor
            from .image_processing_dpt import DPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpt import (
                DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPTForDepthEstimation,
                DPTForSemanticSegmentation,
                DPTModel,
                DPTPreTrainedModel,
            )


else:
      import sys

      A:   str								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
