





'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__						:      Any	=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:      Optional[Any]	=    []
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
  )
  rename_keys.append(
      (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
  )
  rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
  rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
  rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
  rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
  rename_keys.append(
      (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
  )
  rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
  rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
  rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
  )
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
  )
  rename_keys.append(
      (
          F"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
          F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
      )
  )
  rename_keys.append(
      (
          F"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
          F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
      )
  )
  rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
  rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
  rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
  rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
  )
  rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
  )
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
  )
  rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
  rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

  # q, k, v projections in self/cross-attention in decoder for conditional DETR
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
  )
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
  )
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
  )
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
  )
  rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
  )
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
  )
  rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
  )

  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
  )
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
  )
  rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
  rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
  rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
  )
  rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
  rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
  rename_keys.append(
      (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
  )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)







def  a__    (    lowercase :					Union[str, Any], lowercase :					Any, lowercase :					Any				)		->		List[Any]:





     """simple docstring"""

     _UpperCamelCase       =	state_dict.pop(lowercase				)
     _UpperCamelCase       =	val







def  a__    (    lowercase :					List[str]				)		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               _UpperCamelCase       =	key.replace('''backbone.0.body''', '''backbone.conv_encoder.model'''				)
               _UpperCamelCase       =	value
          else:
               _UpperCamelCase       =	value

     return new_state_dict







def  a__    (    lowercase :					List[str], lowercase :					Dict=False				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	''''''
     if is_panoptic:
          _UpperCamelCase       =	'''conditional_detr.'''

     # first: transformer encoder
     for i in range(6				):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""				)
          _UpperCamelCase       =	state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""				)
          # next, add query, keys and values (in that order) to the state dict
          _UpperCamelCase       =	in_proj_weight[:256, :]
          _UpperCamelCase       =	in_proj_bias[:256]
          _UpperCamelCase       =	in_proj_weight[256:512, :]
          _UpperCamelCase       =	in_proj_bias[256:512]
          _UpperCamelCase       =	in_proj_weight[-256:, :]
          _UpperCamelCase       =	in_proj_bias[-256:]







def  a__    (    )		->		Optional[Any]:





     """simple docstring"""

     _UpperCamelCase       =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _UpperCamelCase       =	Image.open(requests.get(lowercase, stream=lowercase				).raw				)

     return im







@torch.no_grad()
def  a__    (    lowercase :					Optional[Any], lowercase :					Dict				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase       =	ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          _UpperCamelCase       =	'''resnet101'''
     if "dc5" in model_name:
          _UpperCamelCase       =	True
     _UpperCamelCase       =	'''panoptic''' in model_name
     if is_panoptic:
          _UpperCamelCase       =	250
     else:
          _UpperCamelCase       =	91
          _UpperCamelCase       =	'''huggingface/label-files'''
          _UpperCamelCase       =	'''coco-detection-id2label.json'''
          _UpperCamelCase       =	json.load(open(hf_hub_download(lowercase, lowercase, repo_type='''dataset'''				), '''r'''				)				)
          _UpperCamelCase       =	{int(lowercase				): v for k, v in idalabel.items()}
          _UpperCamelCase       =	idalabel
          _UpperCamelCase       =	{v: k for k, v in idalabel.items()}

     # load image processor
     _UpperCamelCase       =	'''coco_panoptic''' if is_panoptic else '''coco_detection'''
     _UpperCamelCase       =	ConditionalDetrImageProcessor(format=lowercase				)

     # prepare image
     _UpperCamelCase       =	prepare_img()
     _UpperCamelCase       =	image_processor(images=lowercase, return_tensors='''pt'''				)
     _UpperCamelCase       =	encoding['''pixel_values''']

     logger.info(F"""Converting model {model_name}..."""				)

     # load original model from torch hub
     _UpperCamelCase       =	torch.hub.load('''DeppMeng/ConditionalDETR''', lowercase, pretrained=lowercase				).eval()
     _UpperCamelCase       =	conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               _UpperCamelCase       =	'''conditional_detr.''' + src
          rename_key(lowercase, lowercase, lowercase				)
     _UpperCamelCase       =	rename_backbone_keys(lowercase				)
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase, is_panoptic=lowercase				)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _UpperCamelCase       =	'''conditional_detr.model.''' if is_panoptic else '''model.'''
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith('''conditional_detr'''				)
                   and not key.startswith('''class_labels_classifier'''				)
                   and not key.startswith('''bbox_predictor'''				)
               ):
                    _UpperCamelCase       =	state_dict.pop(lowercase				)
                    _UpperCamelCase       =	val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    _UpperCamelCase       =	state_dict.pop(lowercase				)
                    _UpperCamelCase       =	val
               elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
                    continue
               else:
                    _UpperCamelCase       =	state_dict.pop(lowercase				)
                    _UpperCamelCase       =	val
          else:
               if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
                    _UpperCamelCase       =	state_dict.pop(lowercase				)
                    _UpperCamelCase       =	val
    # finally, create HuggingFace model and load state dict
     _UpperCamelCase       =	ConditionalDetrForSegmentation(lowercase				) if is_panoptic else ConditionalDetrForObjectDetection(lowercase				)
     model.load_state_dict(lowercase				)
     model.eval()
     model.push_to_hub(repo_id=lowercase, organization='''DepuMeng''', commit_message='''Add model'''				)
     # verify our conversion
     _UpperCamelCase       =	conditional_detr(lowercase				)
     _UpperCamelCase       =	model(lowercase				)
     assert torch.allclose(outputs.logits, original_outputs['''pred_logits'''], atol=1e-4				)
     assert torch.allclose(outputs.pred_boxes, original_outputs['''pred_boxes'''], atol=1e-4				)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks, original_outputs['''pred_masks'''], atol=1e-4				)

     # Save model and image processor
     logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""				)
     Path(lowercase				).mkdir(exist_ok=lowercase				)
     model.save_pretrained(lowercase				)
     image_processor.save_pretrained(lowercase				)


if __name__ == "__main__":
  lowercase__						:      List[str]	=    argparse.ArgumentParser()

  parser.add_argument(
      '--model_name',
      default='conditional_detr_resnet50',
      type=str,
      help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  lowercase__						:      List[str]	=    parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''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



