




"""simple docstring"""


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 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					2_00
 lowercase__ 					=					{"""Content-Length""": """100"""}
 lowercase__ 					=					{}


 def  UpperCamelCase_   (				self:		int,      **a_:		Dict		):




   '''simple docstring'''



   return [bytes(__lowercase,      """utf-8"""		)]







def 					UpperCAmelCase__       (*snake_case__							:					Dict		,   **snake_case__							:					List[str]    ):
  """simple docstring"""

  return MockResponse()


@pytest.mark.parametrize("""urls_type"""		,   [str, list, dict]    )
def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  import requests

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

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


@pytest.mark.parametrize("""paths_type"""		,   [str, list, dict]    )
def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Optional[int]		,   snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		Dict								=  str(snake_case__    )
  if issubclass(snake_case__		,   snake_case__    ):
    _snake_case							:		Optional[Any]								=  filename
  elif issubclass(snake_case__		,   snake_case__    ):
    _snake_case							:		Optional[Any]								=  [filename]
  elif issubclass(snake_case__		,   snake_case__    ):
    _snake_case							:		int								=  {'''train''': filename}
  _snake_case							:		Any								=  '''dummy'''
  _snake_case							:		Dict								=  xz_file.parent
  _snake_case							:		str								=  '''extracted'''
  _snake_case							:		str								=  DownloadConfig(
      cache_dir=snake_case__		,   use_etag=snake_case__		,   )
  _snake_case							:		int								=  DownloadManager(dataset_name=snake_case__		,   download_config=snake_case__    )
  _snake_case							:		int								=  dl_manager.extract(snake_case__    )
  _snake_case							:		Union[str, Any]								=  paths
  for extracted_paths in [extracted_paths]:
    if isinstance(snake_case__		,   snake_case__    ):
      _snake_case							:		Optional[int]								=  [extracted_paths]
      _snake_case							:		Tuple								=  [paths]
    elif isinstance(snake_case__		,   snake_case__    ):
      assert "train" in extracted_paths.keys()
      _snake_case							:		Optional[int]								=  extracted_paths.values()
      _snake_case							:		Dict								=  paths.values()
    assert extracted_paths
    for extracted_path, input_path in zip(snake_case__		,   snake_case__    ):
      assert extracted_path == dl_manager.extracted_paths[input_path]
      _snake_case							:		Union[str, Any]								=  Path(snake_case__    )
      _snake_case							:		Any								=  extracted_path.parts
      assert parts[-1] == hash_url_to_filename(snake_case__		,   etag=snake_case__    )
      assert parts[-2] == extracted_subdir
      assert extracted_path.exists()
      _snake_case							:		int								=  extracted_path.read_text()
      _snake_case							:		List[Any]								=  text_file.read_text()
      assert extracted_file_content == expected_file_content


def 					UpperCAmelCase__       (snake_case__							:					Dict		,   snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  assert path.endswith(""".jsonl"""    )
  for num_items, line in enumerate(snake_case__		,   start=1    ):
    _snake_case							:		Tuple								=  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 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Dict								=  request.getfixturevalue(snake_case__    )
  _snake_case							:		Optional[int]								=  DownloadManager()
  for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(snake_case__    )		,   start=1    ):
    _test_jsonl(snake_case__		,   snake_case__    )
  assert num_jsonl == 2


@pytest.mark.parametrize("""archive_nested_jsonl"""		,   ["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]    )
def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		Dict								=  request.getfixturevalue(snake_case__    )
  _snake_case							:		int								=  DownloadManager()
  for num_tar, (path, file) in enumerate(dl_manager.iter_archive(snake_case__    )		,   start=1    ):
    for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(snake_case__    )		,   start=1    ):
      _test_jsonl(snake_case__		,   snake_case__    )
  assert num_tar == 1
  assert num_jsonl == 2






def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  DownloadManager()
  for num_file, file in enumerate(dl_manager.iter_files(snake_case__    )		,   start=1    ):
    assert os.path.basename(snake_case__    ) == ("test.txt" if num_file == 1 else "train.txt")
  assert num_file == 2



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


__lowercase							 =					16
__lowercase							 =					32



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 16  ,			SCREAMING_SNAKE_CASE = "bert-base-cased"	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Any			  =		load_dataset('''glue'''  ,			'''mrpc'''	)

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

    # Apply the method we just defined to all the examples in all the splits of the dataset
    __UpperCamelCase     :Tuple			  =		datasets.map(
        SCREAMING_SNAKE_CASE  ,			batched=SCREAMING_SNAKE_CASE  ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,			load_from_cache_file=SCREAMING_SNAKE_CASE	)

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

    def collate_fn(SCREAMING_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(SCREAMING_SNAKE_CASE  ,			padding='''max_length'''  ,			max_length=128  ,			return_tensors='''pt'''	)
        return tokenizer.pad(SCREAMING_SNAKE_CASE  ,			padding='''longest'''  ,			return_tensors='''pt'''	)

    # Instantiate dataloaders.
    __UpperCamelCase     :Union[str, Any]			  =		DataLoader(
        tokenized_datasets['''train''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Dict			  =		DataLoader(
        tokenized_datasets['''validation''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)

    return train_dataloader, eval_dataloader



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		Accelerator()

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

    set_seed(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		get_dataloaders(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

    # Instantiate optimizer
    __UpperCamelCase     :List[str]			  =		(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    __UpperCamelCase     :Optional[Any]			  =		optimizer_cls(params=model.parameters()  ,			lr=SCREAMING_SNAKE_CASE	)

    if accelerator.state.deepspeed_plugin is not None:
        __UpperCamelCase     :Dict			  =		accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        __UpperCamelCase     :Dict			  =		1
    __UpperCamelCase     :Tuple			  =		(len(SCREAMING_SNAKE_CASE	) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        __UpperCamelCase     :str			  =		get_linear_schedule_with_warmup(
            optimizer=SCREAMING_SNAKE_CASE  ,			num_warmup_steps=0  ,			num_training_steps=SCREAMING_SNAKE_CASE  ,			)
    else:
        __UpperCamelCase     :Dict			  =		DummyScheduler(SCREAMING_SNAKE_CASE  ,			total_num_steps=SCREAMING_SNAKE_CASE  ,			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.
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :int			  =		accelerator.prepare(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase     :List[Any]			  =		0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase     :Dict			  =		0

    # Now we train the model
    __UpperCamelCase     :Any			  =		evaluate.load('''glue'''  ,			'''mrpc'''	)
    __UpperCamelCase     :Union[str, Any]			  =		0
    __UpperCamelCase     :Optional[int]			  =		{}
    for epoch in range(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        model.train()
        for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :Optional[Any]			  =		model(**SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Tuple			  =		outputs.loss
            __UpperCamelCase     :str			  =		loss / gradient_accumulation_steps
            accelerator.backward(SCREAMING_SNAKE_CASE	)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        __UpperCamelCase     :Any			  =		0
        for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            with torch.no_grad():
                __UpperCamelCase     :Any			  =		model(**SCREAMING_SNAKE_CASE	)
            __UpperCamelCase     :Optional[int]			  =		outputs.logits.argmax(dim=-1	)
            # It is slightly faster to call this once, than multiple times
            __UpperCamelCase  ,       __UpperCamelCase     :List[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(SCREAMING_SNAKE_CASE	) - 1:
                    __UpperCamelCase     :List[str]			  =		predictions[: len(eval_dataloader.dataset	) - samples_seen]
                    __UpperCamelCase     :Optional[int]			  =		references[: len(eval_dataloader.dataset	) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=SCREAMING_SNAKE_CASE  ,			references=SCREAMING_SNAKE_CASE  ,			)

        __UpperCamelCase     :Dict			  =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""  ,			SCREAMING_SNAKE_CASE	)
        __UpperCamelCase     :str			  =		eval_metric['''accuracy''']

        if best_performance < eval_metric["accuracy"]:
            __UpperCamelCase     :int			  =		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(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''	)
    parser.add_argument(
        '''--model_name_or_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''bert-base-cased'''  ,			help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,			required=SCREAMING_SNAKE_CASE  ,			)
    parser.add_argument(
        '''--output_dir'''  ,			type=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			default=SCREAMING_SNAKE_CASE  ,			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=SCREAMING_SNAKE_CASE  ,			default=3  ,			help='''Number of train epochs.'''  ,			)
    __UpperCamelCase     :List[str]			  =		parser.parse_args()
    __UpperCamelCase     :Tuple			  =		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
    training_function(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    main()





