





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ConditionalDetrImageProcessor

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def __init__(  self,		A_,		A_=7,		A_=3,		A_=30,		A_=400,		A_=True,		A_=None,		A_=True,		A_=[0.5, 0.5, 0.5],		A_=[0.5, 0.5, 0.5],		A_=True,		A_=1 / 255,		A_=True,		)	->       Union[str, Any]:
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    UpperCAmelCase__						=size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
    UpperCAmelCase__						=parent
    UpperCAmelCase__						=batch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=min_resolution
    UpperCAmelCase__						=max_resolution
    UpperCAmelCase__						=do_resize
    UpperCAmelCase__						=size
    UpperCAmelCase__						=do_normalize
    UpperCAmelCase__						=image_mean
    UpperCAmelCase__						=image_std
    UpperCAmelCase__						=do_rescale
    UpperCAmelCase__						=rescale_factor
    UpperCAmelCase__						=do_pad

 def  __UpperCAmelCase						(  self		)	->       Any:
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_pad": self.do_pad,
    }






 def  __UpperCAmelCase						(  self,		A_,		A_=False		)	->       str:
    if not batched:
       UpperCAmelCase__						=image_inputs[0]
       if isinstance(A_,		Image.Image		):
          UpperCAmelCase__			,							UpperCAmelCase__						=image.size
       else:
          UpperCAmelCase__			,							UpperCAmelCase__						=image.shape[1], image.shape[2]
       if w < h:
          UpperCAmelCase__						=int(self.size["shortest_edge"] * h / w		)
          UpperCAmelCase__						=self.size["shortest_edge"]
       elif w > h:
          UpperCAmelCase__						=self.size["shortest_edge"]
          UpperCAmelCase__						=int(self.size["shortest_edge"] * w / h		)
       else:
          UpperCAmelCase__						=self.size["shortest_edge"]
          UpperCAmelCase__						=self.size["shortest_edge"]

    else:
       UpperCAmelCase__						=[]
       for image in image_inputs:
          UpperCAmelCase__			,							UpperCAmelCase__						=self.get_expected_values([image]		)
          expected_values.append((expected_height, expected_width)		)
       UpperCAmelCase__						=max(A_,		key=lambda A_		: item[0]		)[0]
       UpperCAmelCase__						=max(A_,		key=lambda A_		: item[1]		)[1]

    return expected_height, expected_width

@require_torch
@require_vision
class     snake_case_		(   a,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      ConditionalDetrImageProcessor if is_vision_available() else None

 def  __UpperCAmelCase						(  self		)	->       Any:
    UpperCAmelCase__						=ConditionalDetrImageProcessingTester(self		)

 @property
 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    return self.image_processor_tester.prepare_image_processor_dict()

 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    self.assertTrue(hasattr(A_,		"image_mean"		)		)
    self.assertTrue(hasattr(A_,		"image_std"		)		)
    self.assertTrue(hasattr(A_,		"do_normalize"		)		)
    self.assertTrue(hasattr(A_,		"do_resize"		)		)
    self.assertTrue(hasattr(A_,		"size"		)		)

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.image_processing_class.from_dict(self.image_processor_dict		)
    self.assertEqual(image_processor.size,		{"shortest_edge": 18, "longest_edge": 1333}		)
    self.assertEqual(image_processor.do_pad,		A_		)

    UpperCAmelCase__						=self.image_processing_class.from_dict(
        self.image_processor_dict,		size=42,		max_size=84,		pad_and_return_pixel_mask=A_		)
    self.assertEqual(image_processor.size,		{"shortest_edge": 42, "longest_edge": 84}		)
    self.assertEqual(image_processor.do_pad,		A_		)

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    pass

 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    # Initialize image_processing
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    # create random PIL images
    UpperCAmelCase__						=prepare_image_inputs(self.image_processor_tester,		equal_resolution=A_		)
    for image in image_inputs:
       self.assertIsInstance(A_,		Image.Image		)

    # Test not batched input
    UpperCAmelCase__						=image_processing(image_inputs[0],		return_tensors="pt"		).pixel_values

    UpperCAmelCase__			,							UpperCAmelCase__						=self.image_processor_tester.get_expected_values(A_		)

    self.assertEqual(
        encoded_images.shape,		(1, self.image_processor_tester.num_channels, expected_height, expected_width),		)

    # Test batched
    UpperCAmelCase__			,							UpperCAmelCase__						=self.image_processor_tester.get_expected_values(A_,		batched=A_		)

    UpperCAmelCase__						=image_processing(A_,		return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ),		)

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    # Initialize image_processing
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    # create random numpy tensors
    UpperCAmelCase__						=prepare_image_inputs(self.image_processor_tester,		equal_resolution=A_,		numpify=A_		)
    for image in image_inputs:
       self.assertIsInstance(A_,		np.ndarray		)

    # Test not batched input
    UpperCAmelCase__						=image_processing(image_inputs[0],		return_tensors="pt"		).pixel_values

    UpperCAmelCase__			,							UpperCAmelCase__						=self.image_processor_tester.get_expected_values(A_		)

    self.assertEqual(
        encoded_images.shape,		(1, self.image_processor_tester.num_channels, expected_height, expected_width),		)

    # Test batched
    UpperCAmelCase__						=image_processing(A_,		return_tensors="pt"		).pixel_values

    UpperCAmelCase__			,							UpperCAmelCase__						=self.image_processor_tester.get_expected_values(A_,		batched=A_		)

    self.assertEqual(
        encoded_images.shape,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ),		)

 def  __UpperCAmelCase						(  self		)	->       str:
    # Initialize image_processing
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    # create random PyTorch tensors
    UpperCAmelCase__						=prepare_image_inputs(self.image_processor_tester,		equal_resolution=A_,		torchify=A_		)
    for image in image_inputs:
       self.assertIsInstance(A_,		torch.Tensor		)

    # Test not batched input
    UpperCAmelCase__						=image_processing(image_inputs[0],		return_tensors="pt"		).pixel_values

    UpperCAmelCase__			,							UpperCAmelCase__						=self.image_processor_tester.get_expected_values(A_		)

    self.assertEqual(
        encoded_images.shape,		(1, self.image_processor_tester.num_channels, expected_height, expected_width),		)

    # Test batched
    UpperCAmelCase__						=image_processing(A_,		return_tensors="pt"		).pixel_values

    UpperCAmelCase__			,							UpperCAmelCase__						=self.image_processor_tester.get_expected_values(A_,		batched=A_		)

    self.assertEqual(
        encoded_images.shape,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        ),		)

 @slow
 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    # prepare image and target
    UpperCAmelCase__						=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
    with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt",		"r"		) as f:
       UpperCAmelCase__						=json.loads(f.read()		)

    UpperCAmelCase__						={"image_id": 3_9769, "annotations": target}

    # encode them
    UpperCAmelCase__						=ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"		)
    UpperCAmelCase__						=image_processing(images=A_,		annotations=A_,		return_tensors="pt"		)

    # verify pixel values
    UpperCAmelCase__						=torch.Size([1, 3, 800, 1066]		)
    self.assertEqual(encoding["pixel_values"].shape,		A_		)

    UpperCAmelCase__						=torch.tensor([0.27_96, 0.31_38, 0.34_81]		)
    self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],		A_,		atol=1E-4		)		)

    # verify area
    UpperCAmelCase__						=torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["area"],		A_		)		)
    # verify boxes
    UpperCAmelCase__						=torch.Size([6, 4]		)
    self.assertEqual(encoding["labels"][0]["boxes"].shape,		A_		)
    UpperCAmelCase__						=torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],		A_,		atol=1E-3		)		)
    # verify image_id
    UpperCAmelCase__						=torch.tensor([3_9769]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],		A_		)		)
    # verify is_crowd
    UpperCAmelCase__						=torch.tensor([0, 0, 0, 0, 0, 0]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],		A_		)		)
    # verify class_labels
    UpperCAmelCase__						=torch.tensor([75, 75, 63, 65, 17, 17]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],		A_		)		)
    # verify orig_size
    UpperCAmelCase__						=torch.tensor([480, 640]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],		A_		)		)
    # verify size
    UpperCAmelCase__						=torch.tensor([800, 1066]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["size"],		A_		)		)






 @slow
 def  __UpperCAmelCase						(  self		)	->       str:
    # prepare image, target and masks_path
    UpperCAmelCase__						=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
    with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt",		"r"		) as f:
       UpperCAmelCase__						=json.loads(f.read()		)

    UpperCAmelCase__						={"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

    UpperCAmelCase__						=pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"		)

    # encode them
    UpperCAmelCase__						=ConditionalDetrImageProcessor(format="coco_panoptic"		)
    UpperCAmelCase__						=image_processing(images=A_,		annotations=A_,		masks_path=A_,		return_tensors="pt"		)

    # verify pixel values
    UpperCAmelCase__						=torch.Size([1, 3, 800, 1066]		)
    self.assertEqual(encoding["pixel_values"].shape,		A_		)

    UpperCAmelCase__						=torch.tensor([0.27_96, 0.31_38, 0.34_81]		)
    self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3],		A_,		atol=1E-4		)		)

    # verify area
    UpperCAmelCase__						=torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["area"],		A_		)		)
    # verify boxes
    UpperCAmelCase__						=torch.Size([6, 4]		)
    self.assertEqual(encoding["labels"][0]["boxes"].shape,		A_		)
    UpperCAmelCase__						=torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0],		A_,		atol=1E-3		)		)
    # verify image_id
    UpperCAmelCase__						=torch.tensor([3_9769]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"],		A_		)		)
    # verify is_crowd
    UpperCAmelCase__						=torch.tensor([0, 0, 0, 0, 0, 0]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"],		A_		)		)
    # verify class_labels
    UpperCAmelCase__						=torch.tensor([17, 17, 63, 75, 75, 93]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"],		A_		)		)
    # verify masks
    UpperCAmelCase__						=82_2873
    self.assertEqual(encoding["labels"][0]["masks"].sum().item(),		A_		)
    # verify orig_size
    UpperCAmelCase__						=torch.tensor([480, 640]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"],		A_		)		)
    # verify size
    UpperCAmelCase__						=torch.tensor([800, 1066]		)
    self.assertTrue(torch.allclose(encoding["labels"][0]["size"],		A_		)		)










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 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

   UpperCAmelCase__						=int(A     )
   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 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=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 				_UpperCAmelCase			(    A     ):




   '''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(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     snake_case_		:






 '''simple docstring'''





 __UpperCamelCase       =      5
 __UpperCamelCase       =      0.2

 def __init__(  self,		A_,		A_ = None,		A_ = True,		A_ = None,		A_ = 300,		)	->       Optional[Any]:
    UpperCAmelCase__						=total
    UpperCAmelCase__						="" if prefix is None else prefix
    UpperCAmelCase__						=leave
    UpperCAmelCase__						=parent
    UpperCAmelCase__						=width
    UpperCAmelCase__						=None
    UpperCAmelCase__						=None
    UpperCAmelCase__						=None

 def  __UpperCAmelCase						(  self,		A_,		A_ = False,		A_ = None		)	->       Any:
    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(A_		)
    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(A_		)
       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,		A_,		A_=None		)	->       Dict:
    UpperCAmelCase__						=" " * (len(str(self.total		)		) - len(str(A_		)		)) + str(A_		)
    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		)	->       Tuple:
    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=A_		)
    else:
       self.output.update(disp.HTML(self.html_code		)		)






 def  __UpperCAmelCase						(  self		)	->       str:
    if self.parent is None and self.output is not None:
       self.output.update(disp.HTML(""		)		)

class     snake_case_		(   a							):






 '''simple docstring'''





 def __init__(  self,		A_,		A_=None		)	->       Dict:
    super().__init__(A_		)
    UpperCAmelCase__						=None if column_names is None else [column_names]
    UpperCAmelCase__						=None

 def  __UpperCAmelCase						(  self		)	->       int:
    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=A_		)
    else:
       self.output.update(disp.HTML(self.html_code		)		)

 def  __UpperCAmelCase						(  self,		A_		)	->       Tuple:
    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(A_		)
          UpperCAmelCase__						=columns
       self.inner_table.append([values[c] for c in columns]		)

 def  __UpperCAmelCase						(  self,		A_,		A_=None,		A_=300		)	->       Union[str, Any]:
    UpperCAmelCase__						=NotebookProgressBar(A_,		prefix=A_,		parent=self,		width=A_		)
    return self.child_bar






 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__						=None
    self.display()

class     snake_case_		(   a							):






 '''simple docstring'''





 def __init__(  self		)	->       Optional[int]:
    UpperCAmelCase__						=None
    UpperCAmelCase__						=None
    UpperCAmelCase__						=False

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		**A_		)	->       str:
    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,		A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		**A_		)	->       str:
    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,		A_,		A_,		A_,		A_=None,		**A_		)	->       int:
    if not has_length(A_		):
       return
    if self.prediction_bar is None:
       if self.training_tracker is not None:
          UpperCAmelCase__						=self.training_tracker.add_child(len(A_		)		)
       else:
          UpperCAmelCase__						=NotebookProgressBar(len(A_		)		)
       self.prediction_bar.update(1		)
    else:
       self.prediction_bar.update(self.prediction_bar.value + 1		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		**A_		)	->       Optional[Any]:
    if self.prediction_bar is not None:
       self.prediction_bar.close()
    UpperCAmelCase__						=None

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_=None,		**A_		)	->       str:
    # Only for when there is no evaluation
    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(A_		)

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		A_=None,		**A_		)	->       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$",		"",		A_		)
       UpperCAmelCase__						=metrics.pop("total_flos",		A_		)
       UpperCAmelCase__						=metrics.pop("epoch",		A_		)
       UpperCAmelCase__						=metrics.pop(f"""{metric_key_prefix}_runtime""",		A_		)
       UpperCAmelCase__						=metrics.pop(f"""{metric_key_prefix}_samples_per_second""",		A_		)
       UpperCAmelCase__						=metrics.pop(f"""{metric_key_prefix}_steps_per_second""",		A_		)
       UpperCAmelCase__						=metrics.pop(f"""{metric_key_prefix}_jit_compilation_time""",		A_		)
       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(A_		)
       self.training_tracker.remove_child()
       UpperCAmelCase__						=None
       # Evaluation takes a long time so we should force the next update.
       UpperCAmelCase__						=True






 def  __UpperCAmelCase						(  self,		A_,		A_,		A_,		**A_		)	->       List[str]:
    self.training_tracker.update(
        state.global_step,		comment=f"""Epoch {int(state.epoch		)}/{state.num_train_epochs}""",		force_update=A_		)
    UpperCAmelCase__						=None



