





'''simple docstring'''

from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
   import torch

if is_tf_available():
   import tensorflow as tf
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					int						    =   ['image_processor']
       _snake_case     :					Tuple						    =   'SamImageProcessor'





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

            '''simple docstring'''




            super().__init__(lowerCAmelCase__					)
            _UpperCamelCase       =	self.image_processor
            _UpperCamelCase       =	-10
            _UpperCamelCase       =	self.image_processor.size['''longest_edge''']





       def __call__(							self       :		List[str]	,			lowerCAmelCase__       :		Any=None	,			lowerCAmelCase__       :		Tuple=None	,			lowerCAmelCase__       :		Dict=None	,			lowerCAmelCase__       :		Union[str, Any]=None	,			lowerCAmelCase__       :		Optional[Union[str, TensorType]] = None	,			**lowerCAmelCase__       :		Union[str, Any]	,			)   ->					BatchEncoding:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processor(
                lowerCAmelCase__	,			return_tensors=lowerCAmelCase__	,			**lowerCAmelCase__	,			)

            # pop arguments that are not used in the foward but used nevertheless
            _UpperCamelCase       =	encoding_image_processor['''original_sizes''']

            if hasattr(lowerCAmelCase__	,			'''numpy'''					):  # Checks if Torch or TF tensor
                 _UpperCamelCase       =	original_sizes.numpy()

            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	self._check_and_preprocess_points(
                input_points=lowerCAmelCase__	,			input_labels=lowerCAmelCase__	,			input_boxes=lowerCAmelCase__	,			)

            _UpperCamelCase       =	self._normalize_and_convert(
                lowerCAmelCase__	,			lowerCAmelCase__	,			input_points=lowerCAmelCase__	,			input_labels=lowerCAmelCase__	,			input_boxes=lowerCAmelCase__	,			return_tensors=lowerCAmelCase__	,			)

            return encoding_image_processor





       def snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		str	,			lowerCAmelCase__       :		List[Any]	,			lowerCAmelCase__       :		Dict=None	,			lowerCAmelCase__       :		Optional[int]=None	,			lowerCAmelCase__       :		Tuple=None	,			lowerCAmelCase__       :		List[str]="pt"	,			)   ->					Tuple:

            '''simple docstring'''




            if input_points is not None:
                 if len(lowerCAmelCase__					) != len(lowerCAmelCase__					):
                      _UpperCamelCase       =	[
                          self._normalize_coordinates(self.target_size	,			lowerCAmelCase__	,			original_sizes[0]					) for point in input_points
                      ]
                 else:
                      _UpperCamelCase       =	[
                          self._normalize_coordinates(self.target_size	,			lowerCAmelCase__	,			lowerCAmelCase__					)
                          for point, original_size in zip(lowerCAmelCase__	,			lowerCAmelCase__					)
                      ]
                 # check that all arrays have the same shape
                 if not all(point.shape == input_points[0].shape for point in input_points					):
                      if input_labels is not None:
                           _UpperCamelCase   ,    _UpperCamelCase       =	self._pad_points_and_labels(lowerCAmelCase__	,			lowerCAmelCase__					)

                 _UpperCamelCase       =	np.array(lowerCAmelCase__					)

            if input_labels is not None:
                 _UpperCamelCase       =	np.array(lowerCAmelCase__					)

            if input_boxes is not None:
                 if len(lowerCAmelCase__					) != len(lowerCAmelCase__					):
                      _UpperCamelCase       =	[
                          self._normalize_coordinates(self.target_size	,			lowerCAmelCase__	,			original_sizes[0]	,			is_bounding_box=lowerCAmelCase__					)
                          for box in input_boxes
                      ]
                 else:
                      _UpperCamelCase       =	[
                          self._normalize_coordinates(self.target_size	,			lowerCAmelCase__	,			lowerCAmelCase__	,			is_bounding_box=lowerCAmelCase__					)
                          for box, original_size in zip(lowerCAmelCase__	,			lowerCAmelCase__					)
                      ]
                 _UpperCamelCase       =	np.array(lowerCAmelCase__					)

            if input_boxes is not None:
                 if return_tensors == "pt":
                      _UpperCamelCase       =	torch.from_numpy(lowerCAmelCase__					)
                      # boxes batch size of 1 by default
                      _UpperCamelCase       =	input_boxes.unsqueeze(1					) if len(input_boxes.shape					) != 3 else input_boxes
                 elif return_tensors == "tf":
                      _UpperCamelCase       =	tf.convert_to_tensor(lowerCAmelCase__					)
                      # boxes batch size of 1 by default
                      _UpperCamelCase       =	tf.expand_dims(lowerCAmelCase__	,			1					) if len(input_boxes.shape					) != 3 else input_boxes
                 encoding_image_processor.update({'''input_boxes''': input_boxes}					)
            if input_points is not None:
                 if return_tensors == "pt":
                      _UpperCamelCase       =	torch.from_numpy(lowerCAmelCase__					)
                      # point batch size of 1 by default
                      _UpperCamelCase       =	input_points.unsqueeze(1					) if len(input_points.shape					) != 4 else input_points
                 elif return_tensors == "tf":
                      _UpperCamelCase       =	tf.convert_to_tensor(lowerCAmelCase__					)
                      # point batch size of 1 by default
                      _UpperCamelCase       =	tf.expand_dims(lowerCAmelCase__	,			1					) if len(input_points.shape					) != 4 else input_points
                 encoding_image_processor.update({'''input_points''': input_points}					)
            if input_labels is not None:
                 if return_tensors == "pt":
                      _UpperCamelCase       =	torch.from_numpy(lowerCAmelCase__					)
                      # point batch size of 1 by default
                      _UpperCamelCase       =	input_labels.unsqueeze(1					) if len(input_labels.shape					) != 3 else input_labels
                 elif return_tensors == "tf":
                      _UpperCamelCase       =	tf.convert_to_tensor(lowerCAmelCase__					)
                      # point batch size of 1 by default
                      _UpperCamelCase       =	tf.expand_dims(lowerCAmelCase__	,			1					) if len(input_labels.shape					) != 3 else input_labels
                 encoding_image_processor.update({'''input_labels''': input_labels}					)

            return encoding_image_processor





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Optional[Any]					)   ->					Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase       =	max([point.shape[0] for point in input_points]					)
            _UpperCamelCase       =	[]
            for i, point in enumerate(lowerCAmelCase__					):
                 if point.shape[0] != expected_nb_points:
                      _UpperCamelCase       =	np.concatenate(
                          [point, np.zeros((expected_nb_points - point.shape[0], 2)					) + self.point_pad_value]	,			axis=0					)
                      _UpperCamelCase       =	np.append(input_labels[i]	,			[self.point_pad_value]					)
                 processed_input_points.append(lowerCAmelCase__					)
            _UpperCamelCase       =	processed_input_points
            return input_points, input_labels





       def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		np.ndarray	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		str=False					)   ->					np.ndarray:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase       =	original_size
            _UpperCamelCase   ,    _UpperCamelCase       =	self.image_processor._get_preprocess_shape(lowerCAmelCase__	,			longest_edge=lowerCAmelCase__					)
            _UpperCamelCase       =	deepcopy(lowerCAmelCase__					).astype(lowerCAmelCase__					)

            if is_bounding_box:
                 _UpperCamelCase       =	coords.reshape(-1	,			2	,			2					)

            _UpperCamelCase       =	coords[..., 0] * (new_w / old_w)
            _UpperCamelCase       =	coords[..., 1] * (new_h / old_h)

            if is_bounding_box:
                 _UpperCamelCase       =	coords.reshape(-1	,			4					)

            return coords





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Tuple=None	,			lowerCAmelCase__       :		List[str]=None	,			lowerCAmelCase__       :		Optional[int]=None	,			)   ->					List[str]:

            '''simple docstring'''




            if input_points is not None:
                 if hasattr(lowerCAmelCase__	,			'''numpy'''					):  # Checks for TF or Torch tensor
                      _UpperCamelCase       =	input_points.numpy().tolist()

                 if not isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) or not isinstance(input_points[0]	,			lowerCAmelCase__					):
                      raise ValueError('''Input points must be a list of list of floating points.'''					)
                 _UpperCamelCase       =	[np.array(lowerCAmelCase__					) for input_point in input_points]
            else:
                 _UpperCamelCase       =	None

            if input_labels is not None:
                 if hasattr(lowerCAmelCase__	,			'''numpy'''					):
                      _UpperCamelCase       =	input_labels.numpy().tolist()

                 if not isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) or not isinstance(input_labels[0]	,			lowerCAmelCase__					):
                      raise ValueError('''Input labels must be a list of list integers.'''					)
                 _UpperCamelCase       =	[np.array(lowerCAmelCase__					) for label in input_labels]
            else:
                 _UpperCamelCase       =	None

            if input_boxes is not None:
                 if hasattr(lowerCAmelCase__	,			'''numpy'''					):
                      _UpperCamelCase       =	input_boxes.numpy().tolist()

                 if (
                     not isinstance(lowerCAmelCase__	,			lowerCAmelCase__					)
                     or not isinstance(input_boxes[0]	,			lowerCAmelCase__					)
                     or not isinstance(input_boxes[0][0]	,			lowerCAmelCase__					)
                 ):
                      raise ValueError('''Input boxes must be a list of list of list of floating points.'''					)
                 _UpperCamelCase       =	[np.array(lowerCAmelCase__					).astype(np.floataa					) for box in input_boxes]
            else:
                 _UpperCamelCase       =	None

            return input_points, input_labels, input_boxes





       @property
       def snake_case__						(							self       :		Any					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	self.image_processor.model_input_names
            return list(dict.fromkeys(lowerCAmelCase__					)					)







       def snake_case__						(							self       :		List[str]	,			*lowerCAmelCase__       :		Dict	,			**lowerCAmelCase__       :		str					)   ->					Any:

            '''simple docstring'''




            return self.image_processor.post_process_masks(*lowerCAmelCase__	,			**lowerCAmelCase__					)










'''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  a__    (    lowercase :					Tuple				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	int(lowercase				)
     _UpperCamelCase   ,    _UpperCamelCase   ,    _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  a__    (    lowercase :					List[Any], lowercase :					Dict, lowercase :					Optional[int], lowercase :					Union[str, Any], lowercase :					Any=300				)		->		Any:





     """simple docstring"""

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







def  a__    (    lowercase :					Optional[Any]				)		->		Any:





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






       """simple docstring"""
       _snake_case     :					str						    =   5
       _snake_case     :					Optional[int]						    =   0.2





       def __init__(							self       :		Dict	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Optional[str] = None	,			lowerCAmelCase__       :		bool = True	,			lowerCAmelCase__       :		Optional["NotebookTrainingTracker"] = None	,			lowerCAmelCase__       :		int = 300	,			)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	total
            _UpperCamelCase       =	'''''' if prefix is None else prefix
            _UpperCamelCase       =	leave
            _UpperCamelCase       =	parent
            _UpperCamelCase       =	width
            _UpperCamelCase       =	None
            _UpperCamelCase       =	None
            _UpperCamelCase       =	None





       def snake_case__						(							self       :		str	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		bool = False	,			lowerCAmelCase__       :		str = None					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	value
            if comment is not None:
                 _UpperCamelCase       =	comment
            if self.last_value is None:
                 _UpperCamelCase       =	_UpperCamelCase       =	time.time()
                 _UpperCamelCase       =	_UpperCamelCase       =	value
                 _UpperCamelCase       =	_UpperCamelCase       =	None
                 _UpperCamelCase       =	self.warmup
                 _UpperCamelCase       =	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
                 _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(lowerCAmelCase__					)
                 _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 snake_case__						(							self       :		str	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		Tuple=None					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	''' ''' * (len(str(self.total					)					) - len(str(lowerCAmelCase__					)					)) + str(lowerCAmelCase__					)
            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 snake_case__						(							self       :		List[Any]					)   ->					Tuple:

            '''simple docstring'''




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







       def snake_case__						(							self       :		Tuple					)   ->					Any:

            '''simple docstring'''




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

class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





       def __init__(							self       :		Any	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		List[Any]=None					)   ->					Dict:

            '''simple docstring'''




            super().__init__(lowerCAmelCase__					)
            _UpperCamelCase       =	None if column_names is None else [column_names]
            _UpperCamelCase       =	None





       def snake_case__						(							self       :		List[Any]					)   ->					Any:

            '''simple docstring'''




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





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

            '''simple docstring'''




            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(lowerCAmelCase__					)
                      _UpperCamelCase       =	columns
                 self.inner_table.append([values[c] for c in columns]					)





       def snake_case__						(							self       :		Dict	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Any=None	,			lowerCAmelCase__       :		List[str]=300					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	NotebookProgressBar(lowerCAmelCase__	,			prefix=lowerCAmelCase__	,			parent=self	,			width=lowerCAmelCase__					)
            return self.child_bar







       def snake_case__						(							self       :		Any					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	None
            self.display()

class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





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

            '''simple docstring'''




            _UpperCamelCase       =	None
            _UpperCamelCase       =	None
            _UpperCamelCase       =	False





       def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		str	,			**lowerCAmelCase__       :		Any					)   ->					Dict:

            '''simple docstring'''




            _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	,			lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Dict	,			**lowerCAmelCase__       :		Dict					)   ->					Dict:

            '''simple docstring'''




            _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 snake_case__						(							self       :		Dict	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		List[Any]=None	,			**lowerCAmelCase__       :		Dict					)   ->					Dict:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		Tuple	,			lowerCAmelCase__       :		Dict	,			**lowerCAmelCase__       :		Any					)   ->					Optional[int]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Union[str, Any]	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Optional[Any]	,			lowerCAmelCase__       :		Optional[int]=None	,			**lowerCAmelCase__       :		Optional[int]					)   ->					Tuple:

            '''simple docstring'''




            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(lowerCAmelCase__					)





       def snake_case__						(							self       :		Optional[Any]	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Optional[int]	,			lowerCAmelCase__       :		List[Any]=None	,			**lowerCAmelCase__       :		List[str]					)   ->					List[str]:

            '''simple docstring'''




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







       def snake_case__						(							self       :		int	,			lowerCAmelCase__       :		Dict	,			lowerCAmelCase__       :		Any	,			lowerCAmelCase__       :		Any	,			**lowerCAmelCase__       :		List[str]					)   ->					Optional[Any]:

            '''simple docstring'''




            self.training_tracker.update(
                state.global_step	,			comment=f"""Epoch {int(state.epoch					)}/{state.num_train_epochs}"""	,			force_update=lowerCAmelCase__					)
            _UpperCamelCase       =	None



