




"""simple docstring"""




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				:     Optional[Any]					=				16
lowerCAmelCase				:     Optional[int]					=				32



def        a__					(							snake_case__			,					snake_case__ = 16			,					snake_case__ = "bert-base-cased"							)		->   Any:
			lowerCamelCase      =   AutoTokenizer.from_pretrained(lowerCamelCase__							)
			lowerCamelCase      =   load_dataset("""glue"""			,					"""mrpc"""							)

			def tokenize_function(snake_case__							):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase      =   tokenizer(examples["""sentence1"""]			,					examples["""sentence2"""]			,					truncation=lowerCamelCase__			,					max_length=lowerCamelCase__							)
						return outputs

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

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

			def collate_fn(snake_case__							):
						# 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(lowerCamelCase__			,					padding="""max_length"""			,					max_length=1_28			,					return_tensors="""pt"""							)
						return tokenizer.pad(lowerCamelCase__			,					padding="""longest"""			,					return_tensors="""pt"""							)

			# Instantiate dataloaders.
			lowerCamelCase      =   DataLoader(
			    tokenized_datasets["""train"""]			,					shuffle=lowerCamelCase__			,					collate_fn=lowerCamelCase__			,					batch_size=lowerCamelCase__							)
			lowerCamelCase      =   DataLoader(
			    tokenized_datasets["""validation"""]			,					shuffle=lowerCamelCase__			,					collate_fn=lowerCamelCase__			,					batch_size=lowerCamelCase__							)

			return train_dataloader, eval_dataloader



def        a__					(							snake_case__			,					snake_case__							)		->   Any:
			# Initialize accelerator
			lowerCamelCase      =   Accelerator()

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

			set_seed(lowerCamelCase__							)
			lowerCamelCase ,  lowerCamelCase      =   get_dataloaders(lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__							)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase      =   AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__			,					return_dict=lowerCamelCase__							)

			# Instantiate optimizer
			lowerCamelCase      =   (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			lowerCamelCase      =   optimizer_cls(params=model.parameters()			,					lr=lowerCamelCase__							)

			if accelerator.state.deepspeed_plugin is not None:
						lowerCamelCase      =   accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						lowerCamelCase      =   1
			lowerCamelCase      =   (len(lowerCamelCase__							) * 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      =   get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase__			,					num_warmup_steps=0			,					num_training_steps=lowerCamelCase__			,					)
			else:
						lowerCamelCase      =   DummyScheduler(lowerCamelCase__			,					total_num_steps=lowerCamelCase__			,					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      =   accelerator.prepare(
			    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__							)

			# We need to keep track of how many total steps we have iterated over
			lowerCamelCase      =   0
			# We also need to keep track of the stating epoch so files are named properly
			lowerCamelCase      =   0

			# Now we train the model
			lowerCamelCase      =   evaluate.load("""glue"""			,					"""mrpc"""							)
			lowerCamelCase      =   0
			lowerCamelCase      =   {}
			for epoch in range(lowerCamelCase__			,					lowerCamelCase__							):
						model.train()
						for step, batch in enumerate(lowerCamelCase__							):
									lowerCamelCase      =   model(**lowerCamelCase__							)
									lowerCamelCase      =   outputs.loss
									lowerCamelCase      =   loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase__							)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

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

						lowerCamelCase      =   metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}:'			,					lowerCamelCase__							)
						lowerCamelCase      =   eval_metric["""accuracy"""]

						if best_performance < eval_metric["accuracy"]:
									lowerCamelCase      =   eval_metric["""accuracy"""]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,					"""all_results.json"""							)			,					"""w"""							) as f:
									json.dump(lowerCamelCase__			,					lowerCamelCase__							)



def        a__					(							)		->   Optional[int]:
			lowerCamelCase      =   argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""							)
			parser.add_argument(
			    """--model_name_or_path"""			,					type=lowerCamelCase__			,					default="""bert-base-cased"""			,					help="""Path to pretrained model or model identifier from huggingface.co/models."""			,					required=lowerCamelCase__			,					)
			parser.add_argument(
			    """--output_dir"""			,					type=lowerCamelCase__			,					default="""."""			,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,					)
			parser.add_argument(
			    """--performance_lower_bound"""			,					type=lowerCamelCase__			,					default=lowerCamelCase__			,					help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""			,					)
			parser.add_argument(
			    """--num_epochs"""			,					type=lowerCamelCase__			,					default=3			,					help="""Number of train epochs."""			,					)
			lowerCamelCase      =   parser.parse_args()
			lowerCamelCase      =   {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase__			,					lowerCamelCase__							)


if __name__ == "__main__":
					main()





import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__A							='''http://www.mocksite.com/file1.txt'''
__A							='''"text": ["foo", "foo"]'''
__A							='''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''




class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					2_00
	lowerCAmelCase__   =					{'Content-Length': '100'}
	lowerCAmelCase__   =					{}



	def        SCREAMING_SNAKE_CASE_(       self		,    **lowercase					)							->			int:
					return [bytes(lowercase		,    "utf-8"					)]





def      lowerCamelCase_					(							*lowerCamelCase__      ,					**lowerCamelCase__       ):
				return MockResponse()





@pytest.mark.parametrize("urls_type"      ,					[str, list, dict]       )
def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				import requests

				monkeypatch.setattr(lowerCamelCase__      ,					"request"      ,					lowerCamelCase__       )

				lowerCamelCase_         =     URL
				if issubclass(lowerCamelCase__      ,					lowerCamelCase__       ):
								lowerCamelCase_         =     url
				elif issubclass(lowerCamelCase__      ,					lowerCamelCase__       ):
								lowerCamelCase_         =     [url]
				elif issubclass(lowerCamelCase__      ,					lowerCamelCase__       ):
								lowerCamelCase_         =     {"train": url}
				lowerCamelCase_         =     "dummy"
				lowerCamelCase_         =     "downloads"
				lowerCamelCase_         =     tmp_path
				lowerCamelCase_         =     DownloadConfig(
				    cache_dir=os.path.join(lowerCamelCase__      ,					lowerCamelCase__       )      ,					use_etag=lowerCamelCase__      ,					)
				lowerCamelCase_         =     DownloadManager(dataset_name=lowerCamelCase__      ,					download_config=lowerCamelCase__       )
				lowerCamelCase_         =     dl_manager.download(lowerCamelCase__       )
				lowerCamelCase_         =     urls
				for downloaded_paths in [downloaded_paths]:
								if isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
												lowerCamelCase_         =     [downloaded_paths]
												lowerCamelCase_         =     [urls]
								elif isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
												assert "train" in downloaded_paths.keys()
												lowerCamelCase_         =     downloaded_paths.values()
												lowerCamelCase_         =     urls.values()
								assert downloaded_paths
								for downloaded_path, input_url in zip(lowerCamelCase__      ,					lowerCamelCase__       ):
												assert downloaded_path == dl_manager.downloaded_paths[input_url]
												lowerCamelCase_         =     Path(lowerCamelCase__       )
												lowerCamelCase_         =     downloaded_path.parts
												assert parts[-1] == HASH
												assert parts[-2] == cache_subdir
												assert downloaded_path.exists()
												lowerCamelCase_         =     downloaded_path.read_text()
												assert content == CONTENT
												lowerCamelCase_         =     downloaded_path.with_suffix(".json"       )
												assert metadata_downloaded_path.exists()
												lowerCamelCase_         =     json.loads(metadata_downloaded_path.read_text()       )
												assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize("paths_type"      ,					[str, list, dict]       )
def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     str(lowerCamelCase__       )
				if issubclass(lowerCamelCase__      ,					lowerCamelCase__       ):
								lowerCamelCase_         =     filename
				elif issubclass(lowerCamelCase__      ,					lowerCamelCase__       ):
								lowerCamelCase_         =     [filename]
				elif issubclass(lowerCamelCase__      ,					lowerCamelCase__       ):
								lowerCamelCase_         =     {"train": filename}
				lowerCamelCase_         =     "dummy"
				lowerCamelCase_         =     xz_file.parent
				lowerCamelCase_         =     "extracted"
				lowerCamelCase_         =     DownloadConfig(
				    cache_dir=lowerCamelCase__      ,					use_etag=lowerCamelCase__      ,					)
				lowerCamelCase_         =     DownloadManager(dataset_name=lowerCamelCase__      ,					download_config=lowerCamelCase__       )
				lowerCamelCase_         =     dl_manager.extract(lowerCamelCase__       )
				lowerCamelCase_         =     paths
				for extracted_paths in [extracted_paths]:
								if isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
												lowerCamelCase_         =     [extracted_paths]
												lowerCamelCase_         =     [paths]
								elif isinstance(lowerCamelCase__      ,					lowerCamelCase__       ):
												assert "train" in extracted_paths.keys()
												lowerCamelCase_         =     extracted_paths.values()
												lowerCamelCase_         =     paths.values()
								assert extracted_paths
								for extracted_path, input_path in zip(lowerCamelCase__      ,					lowerCamelCase__       ):
												assert extracted_path == dl_manager.extracted_paths[input_path]
												lowerCamelCase_         =     Path(lowerCamelCase__       )
												lowerCamelCase_         =     extracted_path.parts
												assert parts[-1] == hash_url_to_filename(lowerCamelCase__      ,					etag=lowerCamelCase__       )
												assert parts[-2] == extracted_subdir
												assert extracted_path.exists()
												lowerCamelCase_         =     extracted_path.read_text()
												lowerCamelCase_         =     text_file.read_text()
												assert extracted_file_content == expected_file_content





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				assert path.endswith(".jsonl"       )
				for num_items, line in enumerate(lowerCamelCase__      ,					start=1       ):
								lowerCamelCase_         =     json.loads(line.decode("utf-8"       )       )
								assert item.keys() == {"col_1", "col_2", "col_3"}
				assert num_items == 4





@pytest.mark.parametrize("archive_jsonl"      ,					["tar_jsonl_path", "zip_jsonl_path"]       )
def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     request.getfixturevalue(lowerCamelCase__       )
				lowerCamelCase_         =     DownloadManager()
				for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase__       )      ,					start=1       ):
								_test_jsonl(lowerCamelCase__      ,					lowerCamelCase__       )
				assert num_jsonl == 2





@pytest.mark.parametrize("archive_nested_jsonl"      ,					["tar_nested_jsonl_path", "zip_nested_jsonl_path"]       )
def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__       ):
				lowerCamelCase_         =     request.getfixturevalue(lowerCamelCase__       )
				lowerCamelCase_         =     DownloadManager()
				for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase__       )      ,					start=1       ):
								for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowerCamelCase__       )      ,					start=1       ):
												_test_jsonl(lowerCamelCase__      ,					lowerCamelCase__       )
				assert num_tar == 1
				assert num_jsonl == 2





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     DownloadManager()
				for num_file, file in enumerate(dl_manager.iter_files(lowerCamelCase__       )      ,					start=1       ):
								assert os.path.basename(lowerCamelCase__       ) == ("test.txt" if num_file == 1 else "train.txt")
				assert num_file == 2


