


import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


A			:						int        =  'hf-internal-testing/tiny-random-bert'
A			:						Optional[int]        =  os.path.join(TRANSFORMERS_CACHE, 'models--hf-internal-testing--tiny-random-bert')
A			:						Tuple        =  '9b8c223d42b2188cb49d29af482996f9d0f3e5a6'



class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		Tuple						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						cached_file(_UpperCAmelCase ,   _UpperCAmelCase						)
							# Should have downloaded the file in here
							self.assertTrue(os.path.isdir(_UpperCAmelCase						)						)
							# Cache should contain at least those three subfolders:
							for subfolder in ["blobs", "refs", "snapshots"]:
									self.assertTrue(os.path.isdir(os.path.join(_UpperCAmelCase ,   _UpperCAmelCase						)						)						)
							with open(os.path.join(_UpperCAmelCase ,   """refs""" ,   """main"""						)						) as f:
									lowercase__	     =						f.read()
							self.assertEqual(_UpperCAmelCase ,   os.path.join(_UpperCAmelCase ,   """snapshots""" ,   _UpperCAmelCase ,   _UpperCAmelCase						)						)
							self.assertTrue(os.path.isfile(_UpperCAmelCase						)						)

							# File is cached at the same place the second time.
							lowercase__	     =						cached_file(_UpperCAmelCase ,   _UpperCAmelCase						)
							self.assertEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

							# Using a specific revision to test the full commit hash.
							lowercase__	     =						cached_file(_UpperCAmelCase ,   _UpperCAmelCase ,   revision="""9b8c223"""						)
							self.assertEqual(_UpperCAmelCase ,   os.path.join(_UpperCAmelCase ,   """snapshots""" ,   _UpperCAmelCase ,   _UpperCAmelCase						)						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							with self.assertRaisesRegex(_UpperCAmelCase ,   """is not a valid model identifier"""						):
									lowercase__	     =						cached_file("""tiny-random-bert""" ,   _UpperCAmelCase						)

							with self.assertRaisesRegex(_UpperCAmelCase ,   """is not a valid git identifier"""						):
									lowercase__	     =						cached_file(_UpperCAmelCase ,   _UpperCAmelCase ,   revision="""aaaa"""						)

							with self.assertRaisesRegex(_UpperCAmelCase ,   """does not appear to have a file named"""						):
									lowercase__	     =						cached_file(_UpperCAmelCase ,   """conf"""						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							with self.assertRaisesRegex(_UpperCAmelCase ,   """does not appear to have a file named"""						):
									lowercase__	     =						cached_file(_UpperCAmelCase ,   """conf"""						)

							with open(os.path.join(_UpperCAmelCase ,   """refs""" ,   """main"""						)						) as f:
									lowercase__	     =						f.read()
							self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase ,   """.no_exist""" ,   _UpperCAmelCase ,   """conf"""						)						)						)

							lowercase__	     =						cached_file(_UpperCAmelCase ,   """conf""" ,   _raise_exceptions_for_missing_entries=_UpperCAmelCase						)
							self.assertIsNone(_UpperCAmelCase						)

							lowercase__	     =						cached_file(_UpperCAmelCase ,   """conf""" ,   local_files_only=_UpperCAmelCase ,   _raise_exceptions_for_missing_entries=_UpperCAmelCase						)
							self.assertIsNone(_UpperCAmelCase						)

							lowercase__	     =						mock.Mock()
							lowercase__	     =						500
							lowercase__	     =						{}
							lowercase__	     =						HTTPError
							lowercase__	     =						{}

							# Under the mock environment we get a 500 error when trying to reach the tokenizer.
							with mock.patch("""requests.Session.request""" ,   return_value=_UpperCAmelCase						) as mock_head:
									lowercase__	     =						cached_file(_UpperCAmelCase ,   """conf""" ,   _raise_exceptions_for_connection_errors=_UpperCAmelCase						)
									self.assertIsNone(_UpperCAmelCase						)
									# This check we did call the fake head request
									mock_head.assert_called()

					def     lowerCamelCase__	(self       :		str						)		->  Union[str, Any]:




							"""simple docstring"""


							self.assertTrue(has_file("""hf-internal-testing/tiny-bert-pt-only""" ,   _UpperCAmelCase						)						)
							self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only""" ,   _UpperCAmelCase						)						)
							self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only""" ,   _UpperCAmelCase						)						)

					def     lowerCamelCase__	(self       :		int						)		->  int:




							"""simple docstring"""


							self.assertIsNone(get_file_from_repo("""bert-base-cased""" ,   """ahah.txt"""						)						)

							# The function raises if the repository does not exist.
							with self.assertRaisesRegex(_UpperCAmelCase ,   """is not a valid model identifier"""						):
									get_file_from_repo("""bert-base-case""" ,   _UpperCAmelCase						)

							# The function raises if the revision does not exist.
							with self.assertRaisesRegex(_UpperCAmelCase ,   """is not a valid git identifier"""						):
									get_file_from_repo("""bert-base-cased""" ,   _UpperCAmelCase ,   revision="""ahaha"""						)

							lowercase__	     =						get_file_from_repo("""bert-base-cased""" ,   _UpperCAmelCase						)
							# The name is the cached name which is not very easy to test, so instead we load the content.
							lowercase__	     =						json.loads(open(_UpperCAmelCase ,   """r"""						).read()						)
							self.assertEqual(config["""hidden_size"""] ,   768						)


					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[int]:




							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmp_dir:
									lowercase__	     =						Path(_UpperCAmelCase						) / """a.txt"""
									filename.touch()
									self.assertEqual(get_file_from_repo(_UpperCAmelCase ,   """a.txt"""						) ,   str(_UpperCAmelCase						)						)

									self.assertIsNone(get_file_from_repo(_UpperCAmelCase ,   """b.txt"""						)						)





import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A			:						Optional[Any]        =  1_6
A			:						str        =  3_2






def 					UpperCamelCase   (      __magic_name__	:				Accelerator       ,       __magic_name__	:				int = 16	)  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
		lowercase__	     =						load_dataset("""glue"""       ,       """mrpc"""	)

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

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowercase__	     =						datasets.map(
				    __magic_name__       ,       batched=__magic_name__       ,       remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,       )

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

		def collate_fn(__magic_name__	:				Optional[int]	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__	     =						128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__	     =						16
				elif accelerator.mixed_precision != "no":
						lowercase__	     =						8
				else:
						lowercase__	     =						None

				return tokenizer.pad(
				    __magic_name__       ,       padding="""longest"""       ,       max_length=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_tensors="""pt"""       ,       )

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

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	A			:						Union[str, Any]        =  mocked_dataloaders  # noqa: F811






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				List[Any]	)  ->    Dict:
		"""simple docstring"""




		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,       __magic_name__	) == "1":
				lowercase__	     =						2
		# New Code #
		lowercase__	     =						int(args.gradient_accumulation_steps	)
		# Initialize accelerator
		lowercase__	     =						Accelerator(
		    cpu=args.cpu       ,       mixed_precision=args.mixed_precision       ,       gradient_accumulation_steps=__magic_name__	)
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__	     =						config["""lr"""]
		lowercase__	     =						int(config["""num_epochs"""]	)
		lowercase__	     =						int(config["""seed"""]	)
		lowercase__	     =						int(config["""batch_size"""]	)

		lowercase__	     =						evaluate.load("""glue"""       ,       """mrpc"""	)

		set_seed(__magic_name__	)
		lowercase__     ,      lowercase__	     =						get_dataloaders(__magic_name__       ,       __magic_name__	)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowercase__	     =						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""       ,       return_dict=__magic_name__	)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		lowercase__	     =						model.to(accelerator.device	)

		# Instantiate optimizer
		lowercase__	     =						AdamW(params=model.parameters()       ,       lr=__magic_name__	)

		# Instantiate scheduler
		lowercase__	     =						get_linear_schedule_with_warmup(
		    optimizer=__magic_name__       ,       num_warmup_steps=100       ,       num_training_steps=(len(__magic_name__	) * num_epochs)       ,       )

		# 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.
		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						accelerator.prepare(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)

		# Now we train the model
		for epoch in range(__magic_name__	):
				model.train()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(__magic_name__	):
								lowercase__	     =						model(**__magic_name__	)
								lowercase__	     =						output.loss
								accelerator.backward(__magic_name__	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								lowercase__	     =						model(**__magic_name__	)
						lowercase__	     =						outputs.logits.argmax(dim=-1	)
						lowercase__     ,      lowercase__	     =						accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
						metric.add_batch(
						    predictions=__magic_name__       ,       references=__magic_name__       ,       )

				lowercase__	     =						metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''       ,       __magic_name__	)






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						argparse.ArgumentParser(description="""Simple example of training script."""	)
		parser.add_argument(
		    """--mixed_precision"""       ,       type=__magic_name__       ,       default=__magic_name__       ,       choices=["""no""", """fp16""", """bf16""", """fp8"""]       ,       help="""Whether to use mixed precision. Choose"""
		    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
		    """and an Nvidia Ampere GPU."""       ,       )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""       ,       type=__magic_name__       ,       default=1       ,       help="""The number of minibatches to be ran before gradients are accumulated."""       ,       )
		parser.add_argument("""--cpu"""       ,       action="""store_true"""       ,       help="""If passed, will train on the CPU."""	)
		lowercase__	     =						parser.parse_args()
		lowercase__	     =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(__magic_name__       ,       __magic_name__	)


if __name__ == "__main__":
	main()

