"""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 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     int(_UpperCamelCase   )
						snake_case   ,snake_case   ,snake_case        =     t // 3_6_0_0, (t // 6_0) % 6_0, t % 6_0
						return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			List[str]=3_0_0   )   ->			int:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     '<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:
																		snake_case        =     f"""{elt:.6f}""" if isinstance(_UpperCamelCase	,     _UpperCamelCase   ) else str(_UpperCamelCase   )
																		html_code += f"""      <td>{elt}</td>\n"""
												html_code += "    </tr>\n"
						html_code += "  </tbody>\n</table><p>"
						return html_code



class 					lowerCAmelCase_      :



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  5
				_lowerCAmelCase       :      Optional[int]     					=  0.2

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase = None						,      lowerCAmelCase = True						,      lowerCAmelCase = None						,      lowerCAmelCase = 3_00						,      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = False						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										snake_case        =     value
										if comment is not None:
																snake_case        =     comment
										if self.last_value is None:
																snake_case        =     snake_case        =     time.time()
																snake_case        =     snake_case        =     value
																snake_case        =     snake_case        =     None
																snake_case        =     self.warmup
																snake_case        =     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
																snake_case        =     time.time()
																snake_case        =     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:
																						snake_case        =     self.elapsed_time / (value - self.start_value)
																else:
																						snake_case        =     None
																if value >= self.total:
																						snake_case        =     self.total
																						snake_case        =     None
																						if not self.leave:
																												self.close()
																elif self.average_time_per_item is not None:
																						snake_case        =     self.average_time_per_item * (self.total - value)
																self.update_bar(lowerCAmelCase      )
																snake_case        =     value
																snake_case        =     current_time
																if self.average_time_per_item is None:
																						snake_case        =     1
																else:
																						snake_case        =     max(int(self.update_every / self.average_time_per_item      )						,      1      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase=None      ):






										"""simple docstring"""

										snake_case        =     ' ' * (len(str(self.total      )      ) - len(str(lowerCAmelCase      )      )) + str(lowerCAmelCase      )
										if self.elapsed_time is None:
																snake_case        =     F"""[{spaced_value}/{self.total} : < :"""
										elif self.predicted_remaining is None:
																snake_case        =     F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time      )}"""
										else:
																snake_case        =     (
																    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      ):






										"""simple docstring"""

										snake_case        =     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:
																snake_case        =     disp.display(disp.HTML(self.html_code      )						,      display_id=lowerCAmelCase      )
										else:
																self.output.update(disp.HTML(self.html_code      )      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

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





class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=None      ):






										"""simple docstring"""

										super().__init__(lowerCAmelCase      )
										snake_case        =     None if column_names is None else [column_names]
										snake_case        =     None

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     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:
																snake_case        =     disp.display(disp.HTML(self.html_code      )						,      display_id=lowerCAmelCase      )
										else:
																self.output.update(disp.HTML(self.html_code      )      )

				def 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										if self.inner_table is None:
																snake_case        =     [list(values.keys()      ), list(values.values()      )]
										else:
																snake_case        =     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      )
																						snake_case        =     columns
																self.inner_table.append([values[c] for c in columns]      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase=None						,      lowerCAmelCase=3_00      ):






										"""simple docstring"""

										snake_case        =     NotebookProgressBar(lowerCAmelCase						,      prefix=lowerCAmelCase						,      parent=self						,      width=lowerCAmelCase      )
										return self.child_bar

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     None
										self.display()





class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""

				def __init__(			self      ):






										"""simple docstring"""

										snake_case        =     None
										snake_case        =     None
										snake_case        =     False

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
										snake_case        =     0
										snake_case        =     0
										snake_case        =     [self.first_column] + ['Training Loss']
										if args.evaluation_strategy != IntervalStrategy.NO:
																column_names.append('Validation Loss'      )
										snake_case        =     NotebookTrainingTracker(state.max_steps						,      lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     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						,      )
										snake_case        =     False

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=None						,      **lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=None						,      **lowerCAmelCase      ):






										"""simple docstring"""

										if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
																snake_case        =     {'Training Loss': logs['loss']}
																# First column is necessarily Step sine we're not in epoch eval strategy
																snake_case        =     state.global_step
																self.training_tracker.write_line(lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase=None						,      **lowerCAmelCase      ):






										"""simple docstring"""

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

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      **lowerCAmelCase      ):






										"""simple docstring"""

										self.training_tracker.update(
										    state.global_step						,      comment=F"""Epoch {int(state.epoch      )}/{state.num_train_epochs}"""						,      force_update=lowerCAmelCase      )
										snake_case        =     None



"""simple docstring"""
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


SCREAMING_SNAKE_CASE__   =					"true"





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			Dict=8_2	,     _UpperCamelCase    :			int=1_6   )   ->			str:





						"""simple docstring"""






						set_seed(4_2   )
						snake_case        =     RegressionModel()
						snake_case        =     deepcopy(_UpperCamelCase   )
						snake_case        =     RegressionDataset(length=_UpperCamelCase   )
						snake_case        =     DataLoader(_UpperCamelCase	,     batch_size=_UpperCamelCase   )
						model.to(accelerator.device   )
						snake_case   ,snake_case        =     accelerator.prepare(_UpperCamelCase	,     _UpperCamelCase   )
						return model, ddp_model, dataloader





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Optional[Any]=False   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'   )
						snake_case        =     load_dataset('glue'	,     'mrpc'	,     split='validation'   )

						def tokenize_function(_UpperCamelCase    :			Optional[Any]   ):
												snake_case        =     tokenizer(examples['sentence1']	,     examples['sentence2']	,     truncation=_UpperCamelCase	,     max_length=_UpperCamelCase   )
												return outputs

						with accelerator.main_process_first():
												snake_case        =     dataset.map(
												    _UpperCamelCase	,     batched=_UpperCamelCase	,     remove_columns=['idx', 'sentence1', 'sentence2']	,     )

						snake_case        =     tokenized_datasets.rename_column('label'	,     'labels'   )

						def collate_fn(_UpperCamelCase    :			Optional[Any]   ):
												if use_longest:
																		return tokenizer.pad(_UpperCamelCase	,     padding='longest'	,     return_tensors='pt'   )
												return tokenizer.pad(_UpperCamelCase	,     padding='max_length'	,     max_length=1_2_8	,     return_tensors='pt'   )

						return DataLoader(_UpperCamelCase	,     shuffle=_UpperCamelCase	,     collate_fn=_UpperCamelCase	,     batch_size=1_6   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Any   )   ->			List[Any]:





						"""simple docstring"""






						snake_case        =     Accelerator(dispatch_batches=_UpperCamelCase	,     split_batches=_UpperCamelCase   )
						snake_case        =     get_dataloader(_UpperCamelCase	,     not dispatch_batches   )
						snake_case        =     AutoModelForSequenceClassification.from_pretrained(
						    'hf-internal-testing/mrpc-bert-base-cased'	,     return_dict=_UpperCamelCase   )
						snake_case   ,snake_case        =     accelerator.prepare(_UpperCamelCase	,     _UpperCamelCase   )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			Optional[Any]   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     []
						for batch in dataloader:
												snake_case   ,snake_case        =     batch.values()
												with torch.no_grad():
																		snake_case        =     model(_UpperCamelCase   )
																		snake_case   ,snake_case        =     accelerator.gather_for_metrics((logit, target)   )
																		logits_and_targets.append((logit, target)   )
						snake_case   ,snake_case        =     [], []
						for logit, targ in logits_and_targets:
												logits.append(_UpperCamelCase   )
												targs.append(_UpperCamelCase   )
						snake_case   ,snake_case        =     torch.cat(_UpperCamelCase   ), torch.cat(_UpperCamelCase   )
						return logits, targs





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Accelerator	,     _UpperCamelCase    :			Tuple=8_2	,     _UpperCamelCase    :			Optional[Any]=False	,     _UpperCamelCase    :			int=False	,     _UpperCamelCase    :			List[str]=1_6   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case   ,snake_case   ,snake_case        =     get_basic_setup(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						snake_case   ,snake_case        =     generate_predictions(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )
						assert (
						    len(_UpperCamelCase   ) == num_samples
						), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_UpperCamelCase   )}"""





def 				lowerCAmelCase__      (     _UpperCamelCase    :			bool = False	,     _UpperCamelCase    :			bool = False   )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     evaluate.load('glue'	,     'mrpc'   )
						snake_case   ,snake_case        =     get_mrpc_setup(_UpperCamelCase	,     _UpperCamelCase   )
						# First do baseline
						snake_case   ,snake_case   ,snake_case        =     setup['no']
						model.to(_UpperCamelCase   )
						model.eval()
						for batch in dataloader:
												batch.to(_UpperCamelCase   )
												with torch.inference_mode():
																		snake_case        =     model(**_UpperCamelCase   )
												snake_case        =     outputs.logits.argmax(dim=-1   )
												metric.add_batch(predictions=_UpperCamelCase	,     references=batch['labels']   )
						snake_case        =     metric.compute()

						# Then do distributed
						snake_case   ,snake_case   ,snake_case        =     setup['ddp']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		snake_case        =     model(**_UpperCamelCase   )
												snake_case        =     outputs.logits.argmax(dim=-1   )
												snake_case        =     batch['labels']
												snake_case   ,snake_case        =     accelerator.gather_for_metrics((preds, references)   )
												metric.add_batch(predictions=_UpperCamelCase	,     references=_UpperCamelCase   )
						snake_case        =     metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key]	,     distributed[key]   ), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""





def 				lowerCAmelCase__      (     )   ->			Tuple:





						"""simple docstring"""






						snake_case        =     Accelerator(split_batches=_UpperCamelCase	,     dispatch_batches=_UpperCamelCase   )
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print('**Testing gather_for_metrics**'   )
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""   )
																								test_mrpc(_UpperCamelCase	,     _UpperCamelCase   )
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test torch metrics**'   )
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		snake_case        =     Accelerator(split_batches=_UpperCamelCase	,     dispatch_batches=_UpperCamelCase   )
																		if accelerator.is_local_main_process:
																								print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""   )
																		test_torch_metrics(_UpperCamelCase	,     9_9   )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test last batch is not dropped when perfectly divisible**'   )
						snake_case        =     Accelerator()
						test_torch_metrics(_UpperCamelCase	,     5_1_2   )
						accelerator.state._reset_state()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple   )   ->			str:





						"""simple docstring"""






						main()


if __name__ == "__main__":
					main()


