




import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowerCamelCase      :  Any      						=						1_6
_lowerCamelCase      :  int      						=						3_2




def 							_UpperCAmelCase  (UpperCamelCase_ : Accelerator      ,    UpperCamelCase_ : int = 16      ,    UpperCamelCase_ : str = "bert-base-cased"       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      AutoTokenizer.from_pretrained(A__       )
				_lowerCAmelCase    :    List[str]										=      load_dataset("""glue"""      ,    """mrpc"""       )

				def tokenize_function(UpperCamelCase_ : Optional[Any]       ):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCAmelCase    :    List[Any]										=      tokenizer(examples["""sentence1"""]      ,    examples["""sentence2"""]      ,    truncation=A__      ,    max_length=A__       )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				_lowerCAmelCase    :    Any										=      datasets.map(
				    A__      ,    batched=A__      ,    remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,    load_from_cache_file=A__       )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_lowerCAmelCase    :    Optional[int]										=      tokenized_datasets.rename_column("""label"""      ,    """labels"""       )

				def collate_fn(UpperCamelCase_ : List[str]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(A__      ,    padding="""max_length"""      ,    max_length=128      ,    return_tensors="""pt"""       )
								return tokenizer.pad(A__      ,    padding="""longest"""      ,    return_tensors="""pt"""       )

				# Instantiate dataloaders.
				_lowerCAmelCase    :    List[str]										=      DataLoader(
				    tokenized_datasets["""train"""]      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__       )
				_lowerCAmelCase    :    List[str]										=      DataLoader(
				    tokenized_datasets["""validation"""]      ,    shuffle=A__      ,    collate_fn=A__      ,    batch_size=A__       )

				return train_dataloader, eval_dataloader




def 							_UpperCAmelCase  (UpperCamelCase_ : Dict      ,    UpperCamelCase_ : Union[str, Any]      ,    UpperCamelCase_ : Any      ,    UpperCamelCase_ : Dict       ):
				'''simple docstring'''
				model.eval()
				_lowerCAmelCase    :    List[Any]										=      0
				for step, batch in enumerate(A__       ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device       )
								with torch.no_grad():
												_lowerCAmelCase    :    List[str]										=      model(**A__       )
								_lowerCAmelCase    :    Any										=      outputs.logits.argmax(dim=-1       )
								# It is slightly faster to call this once, than multiple times
								_lowerCAmelCase					,    _lowerCAmelCase    :    Union[str, Any]										=      accelerator.gather(
								    (predictions, batch["""labels"""])       )  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(A__       ) - 1:
																_lowerCAmelCase    :    Any										=      predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																_lowerCAmelCase    :    Optional[int]										=      references[: len(eval_dataloader.dataset       ) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=A__      ,    references=A__      ,    )

				_lowerCAmelCase    :    Any										=      metric.compute()
				return eval_metric["accuracy"]




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : Dict       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[Any]										=      Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_lowerCAmelCase    :    Dict										=      config["""lr"""]
				_lowerCAmelCase    :    str										=      int(config["""num_epochs"""]       )
				_lowerCAmelCase    :    int										=      int(config["""seed"""]       )
				_lowerCAmelCase    :    Tuple										=      int(config["""batch_size"""]       )
				_lowerCAmelCase    :    Dict										=      args.model_name_or_path

				set_seed(A__       )
				_lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      get_dataloaders(A__      ,    A__      ,    A__       )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_lowerCAmelCase    :    Tuple										=      AutoModelForSequenceClassification.from_pretrained(A__      ,    return_dict=A__       )

				# Instantiate optimizer
				_lowerCAmelCase    :    int										=      (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				_lowerCAmelCase    :    Union[str, Any]										=      optimizer_cls(params=model.parameters()      ,    lr=A__       )

				if accelerator.state.deepspeed_plugin is not None:
								_lowerCAmelCase    :    Union[str, Any]										=      accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								_lowerCAmelCase    :    Any										=      1
				_lowerCAmelCase    :    List[Any]										=      (len(A__       ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								_lowerCAmelCase    :    Any										=      get_linear_schedule_with_warmup(
								    optimizer=A__      ,    num_warmup_steps=0      ,    num_training_steps=A__      ,    )
				else:
								_lowerCAmelCase    :    str										=      DummyScheduler(A__      ,    total_num_steps=A__      ,    warmup_num_steps=0       )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      accelerator.prepare(
				    A__      ,    A__      ,    A__      ,    A__      ,    A__       )

				# We need to keep track of how many total steps we have iterated over
				_lowerCAmelCase    :    Optional[Any]										=      0
				# We also need to keep track of the stating epoch so files are named properly
				_lowerCAmelCase    :    Tuple										=      0
				_lowerCAmelCase    :    int										=      evaluate.load("""glue"""      ,    """mrpc"""       )
				_lowerCAmelCase    :    Tuple										=      num_epochs

				if args.partial_train_epoch is not None:
								_lowerCAmelCase    :    int										=      args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint       )
								_lowerCAmelCase    :    Tuple										=      args.resume_from_checkpoint.split("""epoch_"""       )[1]
								_lowerCAmelCase    :    Dict										=      """"""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								_lowerCAmelCase    :    Optional[int]										=      int(A__       ) + 1
								_lowerCAmelCase    :    List[str]										=      evaluation_loop(A__      ,    A__      ,    A__      ,    A__       )
								accelerator.print("""resumed checkpoint performance:"""      ,    A__       )
								accelerator.print("""resumed checkpoint's scheduler's lr:"""      ,    lr_scheduler.get_lr()[0]       )
								accelerator.print("""resumed optimizers's lr:"""      ,    optimizer.param_groups[0]["""lr"""]       )
								with open(os.path.join(args.output_dir      ,    F"state_{starting_epoch-1}.json"       )      ,    """r"""       ) as f:
												_lowerCAmelCase    :    List[str]										=      json.load(A__       )
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				_lowerCAmelCase    :    Any										=      {}
				for epoch in range(A__      ,    A__       ):
								model.train()
								for step, batch in enumerate(A__       ):
												_lowerCAmelCase    :    Tuple										=      model(**A__       )
												_lowerCAmelCase    :    List[Any]										=      outputs.loss
												_lowerCAmelCase    :    Any										=      loss / gradient_accumulation_steps
												accelerator.backward(A__       )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								_lowerCAmelCase    :    int										=      F"epoch_{epoch}"
								_lowerCAmelCase    :    Tuple										=      os.path.join(args.output_dir      ,    A__       )
								accelerator.save_state(A__       )
								_lowerCAmelCase    :    List[str]										=      evaluation_loop(A__      ,    A__      ,    A__      ,    A__       )
								_lowerCAmelCase    :    Optional[Any]										=      accuracy
								_lowerCAmelCase    :    List[str]										=      lr_scheduler.get_lr()[0]
								_lowerCAmelCase    :    List[Any]										=      optimizer.param_groups[0]["""lr"""]
								_lowerCAmelCase    :    Any										=      epoch
								_lowerCAmelCase    :    str										=      overall_step
								accelerator.print(F"epoch {epoch}:"      ,    A__       )

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir      ,    F"state_{epoch}.json"       )      ,    """w"""       ) as f:
																json.dump(A__      ,    A__       )




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""       )
				parser.add_argument(
				    """--model_name_or_path"""      ,    type=A__      ,    default="""bert-base-cased"""      ,    help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,    required=A__      ,    )
				parser.add_argument(
				    """--output_dir"""      ,    type=A__      ,    default="""."""      ,    help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,    )
				parser.add_argument(
				    """--resume_from_checkpoint"""      ,    type=A__      ,    default=A__      ,    help="""If the training should continue from a checkpoint folder."""      ,    )
				parser.add_argument(
				    """--partial_train_epoch"""      ,    type=A__      ,    default=A__      ,    help="""If passed, the training will stop after this number of epochs."""      ,    )
				parser.add_argument(
				    """--num_epochs"""      ,    type=A__      ,    default=2      ,    help="""Number of train epochs."""      ,    )
				_lowerCAmelCase    :    Tuple										=      parser.parse_args()
				_lowerCAmelCase    :    Optional[int]										=      {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

				training_function(A__      ,    A__       )


if __name__ == "__main__":
	main()







import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


_lowerCamelCase      :  Optional[Any]      						=						TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


_lowerCamelCase      :  Optional[int]      						=						[]
_lowerCamelCase      :  Tuple      						=						[]

_lowerCamelCase      :  List[str]      						=						{"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

_lowerCamelCase      :  Optional[Any]      						=						[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": F'''🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results''',
            "emoji": True,
        },
    }
]

_lowerCamelCase      :  str      						=						0
for log in Path().glob("*.log"):
	_lowerCamelCase      :  str      						=						0
	with open(log, "r") as f:
		for line in f:
			_lowerCamelCase      :  Optional[Any]      						=						json.loads(line)
			if line.get("nodeid", "") != "":
				_lowerCamelCase      :  str      						=						line["nodeid"]
				if line.get("duration", None) is not None:
					_lowerCamelCase      :  str      						=						F'''{line['duration']:.4f}'''
					if line.get("outcome", "") == "failed":
						section_num_failed += 1
						failed.append([test, duration, log.name.split("_")[0]])
						total_num_failed += 1
	group_info.append([str(log), section_num_failed, failed])
	_lowerCamelCase      :  Optional[int]      						=						[]
	log.unlink()

_lowerCamelCase      :  str      						=						""
_lowerCamelCase      :  Optional[Any]      						=						[]
if total_num_failed > 0:
	for name, num_failed, failed_tests in group_info:
		if num_failed > 0:
			if num_failed == 1:
				message += F"*{name[1:]}: {num_failed} failed test*\n"
			else:
				message += F"*{name[1:]}: {num_failed} failed tests*\n"
			_lowerCamelCase      :  Any      						=						[]
			_lowerCamelCase      :  Dict      						=						{}
			for test in failed_tests:
				_lowerCamelCase      :  Union[str, Any]      						=						test[0].split("::")
				_lowerCamelCase      :  str      						=						data[0].split("/")[-1]
				if data[0] not in filesafailed:
					_lowerCamelCase      :  List[str]      						=						[data[1:]]
				else:
					filesafailed[data[0]] += [data[1:]]
				failed_table.append(data)

			_lowerCamelCase      :  List[Any]      						=						[test[0] for test in failed_table]
			_lowerCamelCase      :  Optional[Any]      						=						list(set(files))
			# Count number of instances in failed_tests
			_lowerCamelCase      :  Union[str, Any]      						=						[]
			for file in individual_files:
				table.append([file, len(filesafailed[file])])

			_lowerCamelCase      :  Optional[Any]      						=						tabulate(
			    table,
			    headers=["Test Location", "Num Failed"],
			    tablefmt=hf_table_format,
			    stralign="right",
			)
			message += F"\n```\n{failed_table}\n```"
			all_filesafailed.append(filesafailed)
	if len(message) > 3_0_0_0:
		_lowerCamelCase      :  List[str]      						=						"Too many failed tests, please see the full report in the Action results."
		_lowerCamelCase      :  Tuple      						=						len(err) + 1_0
		_lowerCamelCase      :  Union[str, Any]      						=						message[: 3_0_0_0 - offset] + F'''\n...\n```\n{err}'''
	print(F'''### {message}''')
else:
	_lowerCamelCase      :  List[str]      						=						"No failed tests! 🤗"
	print(F'''## {message}''')
	payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
	from slack_sdk import WebClient

	_lowerCamelCase      :  Optional[int]      						=						WebClient(token=os.environ["SLACK_API_TOKEN"])
	if message != "No failed tests! 🤗":
		_lowerCamelCase      :  int      						=						{
		    "type": "section",
		    "text": {
		        "type": "mrkdwn",
		        "text": message,
		    },
		}
		payload.append(md_report)
		_lowerCamelCase      :  List[Any]      						=						{
		    "type": "section",
		    "text": {
		        "type": "mrkdwn",
		        "text": "*For more details:*",
		    },
		    "accessory": {
		        "type": "button",
		        "text": {
		            "type": "plain_text",
		            "text": "Check Action results",
		            "emoji": True,
		        },
		        "url": F'''https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
		    },
		}
		payload.append(action_button)
		_lowerCamelCase      :  Union[str, Any]      						=						{
		    "type": "context",
		    "elements": [
		        {
		            "type": "plain_text",
		            "text": F'''Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}''',
		        }
		    ],
		}
		payload.append(date_report)
	_lowerCamelCase      :  int      						=						client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
	_lowerCamelCase      :  List[Any]      						=						response.data["ts"]
	for failed_file in all_filesafailed:
		for test_location, test_failures in failed_file.items():
			# Keep only the first instance of the test name
			_lowerCamelCase      :  Dict      						=						""
			for i, row in enumerate(test_failures):
				if row[0] != test_class:
					_lowerCamelCase      :  Optional[int]      						=						row[0]
				else:
					_lowerCamelCase      :  List[str]      						=						""

			_lowerCamelCase      :  int      						=						{
			    "type": "section",
			    "text": {
			        "type": "mrkdwn",
			        "text": F'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```''',
			    },
			}

			client.chat_postMessage(
			    channel="#accelerate-ci-daily",
			    thread_ts=ts,
			    blocks=[payload],
			)

