





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase_												=	logging.getLogger()






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=argparse.ArgumentParser()
   parser.add_argument("-f"     )
   UpperCAmelCase__						=parser.parse_args()
   return args.f




class     snake_case_		(   a							):






 '''simple docstring'''





 def  __UpperCAmelCase						(  self		)	->       None:
    UpperCAmelCase__						=logging.StreamHandler(sys.stdout		)
    logger.addHandler(A_		)

 def  __UpperCAmelCase						(  self,		A_		)	->       List[str]:
    UpperCAmelCase__						=get_gpu_count()

    if n_gpu > 1:
       pass
       # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
       # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
       # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
       # cmd = [sys.executable] + distributed_args + args
       # execute_subprocess_async(cmd, env=self.get_env())
       # XXX: test the results - need to save them first into .json file
    else:
       args.insert(0,		"run_glue_deebert.py"		)
       with patch.object(A_,		"argv",		A_		):
          UpperCAmelCase__						=run_glue_deebert.main()
          for value in result.values():
             self.assertGreaterEqual(A_,		0.6_66		)






 @slow
 @require_torch_non_multi_gpu
 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    UpperCAmelCase__						="\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
    self.run_and_check(A_		)

    UpperCAmelCase__						="\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
    self.run_and_check(A_		)

    UpperCAmelCase__						="\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
    self.run_and_check(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



