
'''simple docstring'''







import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
lowercase    :							str      =					pytest.mark.integration


lowercase    :							str      =					{"comet"}
lowercase    :							List[Any]      =					importlib.util.find_spec("fairseq") is not None

lowercase    :							str      =					{"code_eval"}
lowercase    :							Optional[Any]      =					os.name == "nt"

lowercase    :							Optional[Any]      =					{"bertscore", "frugalscore", "perplexity"}
lowercase    :							str      =					importlib.util.find_spec("transformers") is not None







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	int:
	@wraps(__A	)
	def wrapper(self			,				__A	):
		if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
			self.skipTest('"test requires Fairseq"'	)
		else:
			test_case(self			,				__A	)

	return wrapper







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	@wraps(__A	)
	def wrapper(self			,				__A	):
		if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
			self.skipTest('"test requires transformers"'	)
		else:
			test_case(self			,				__A	)

	return wrapper







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	@wraps(__A	)
	def wrapper(self			,				__A	):
		if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
			self.skipTest('"test not supported on Windows"'	)
		else:
			test_case(self			,				__A	)

	return wrapper







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Any:
	_snake_case   						=		[metric_dir.split(os.sep	)[-2] for metric_dir in glob.glob('./metrics/*/'	)]
	return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished


@parameterized.named_parameters(get_local_metric_names()						)
@for_all_test_methods(
    _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						)
@local
class 						__UpperCAmelCase      (					parameterized.TestCase						):
							__lowercase      		=       {}
							__lowercase      		=       None


							@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'  )
							@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'  )
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		'[...]'
								_snake_case   						=		importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join('metrics'    ,   lowerCAmelCase_  )  ).module_path  )
								_snake_case   						=		datasets.load.import_main_class(metric_module.__name__    ,   dataset=lowerCAmelCase_  )
								# check parameters
								_snake_case   						=		inspect.signature(metric._compute  ).parameters
								self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()  )  )  # no **kwargs
								# run doctest
								with self.patch_intensive_calls(lowerCAmelCase_    ,   metric_module.__name__  ):
									with self.use_local_metrics():
										try:
											_snake_case   						=		doctest.testmod(lowerCAmelCase_    ,   verbose=lowerCAmelCase_    ,   raise_on_error=lowerCAmelCase_  )
										except doctest.UnexpectedException as e:
											raise e.exc_info[1]  # raise the exception that doctest caught
								self.assertEqual(results.failed    ,   0  )
								self.assertGreater(results.attempted    ,   1  )


							@slow
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		'[...]'
								_snake_case   						=		importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join('metrics'    ,   lowerCAmelCase_  )  ).module_path  )
								# run doctest
								with self.use_local_metrics():
									_snake_case   						=		doctest.testmod(lowerCAmelCase_    ,   verbose=lowerCAmelCase_    ,   raise_on_error=lowerCAmelCase_  )
								self.assertEqual(results.failed    ,   0  )
								self.assertGreater(results.attempted    ,   1  )


							@contextmanager
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase_  ):
										yield
								else:
									yield


							@contextmanager
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								def load_local_metric(lowerCAmelCase_    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):
									return load_metric(os.path.join('metrics'    ,   lowerCAmelCase_  )    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  )

								with patch('datasets.load_metric'  ) as mock_load_metric:
									_snake_case   						=		load_local_metric
									yield




							@classmethod
							def  lowerCamelCase				(       cls    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								def wrapper(lowerCAmelCase_  ):
									_snake_case   						=		contextmanager(lowerCAmelCase_  )
									_snake_case   						=		patcher
									return patcher

								return wrapper







@LocalMetricTest.register_intensive_calls_patcher('bleurt'	)
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	List[str]:
	import tensorflow.compat.va as tf
	from bleurt.score import Predictor

	tf.flags.DEFINE_string('sv'			,				''			,				''	)  # handle pytest cli flags


	class 						__UpperCAmelCase      (					_lowerCamelCase						):


								def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





									"""simple docstring"""






									assert len(input_dict['input_ids']  ) == 2
									return np.array([1.03, 1.04]  )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
	with patch('bleurt.score._create_predictor'	) as mock_create_predictor:
		_snake_case   						=		MockedPredictor()
		yield







@LocalMetricTest.register_intensive_calls_patcher('bertscore'	)
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Dict:
	import torch

	def bert_cos_score_idf(__A			,				__A			,				*__A			,				**__A	):
		return torch.tensor([[1.0, 1.0, 1.0]] * len(__A	)	)

	# mock get_model which is supposed to do download a bert model
	# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
	with patch('bert_score.scorer.get_model'	), patch(
	    'bert_score.scorer.bert_cos_score_idf'	) as mock_bert_cos_score_idf:
		_snake_case   						=		bert_cos_score_idf
		yield







@LocalMetricTest.register_intensive_calls_patcher('comet'	)
def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	def load_from_checkpoint(__A	):


		class 						__UpperCAmelCase      :


									def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   *lowerCAmelCase_    ,   **lowerCAmelCase_  ):





										"""simple docstring"""






										assert len(lowerCAmelCase_  ) == 2
										_snake_case   						=		[0.19, 0.92]
										return scores, sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )
		return Model()

	# mock load_from_checkpoint which is supposed to do download a bert model
	# mock load_from_checkpoint which is supposed to do download a bert model
	with patch('comet.download_model'	) as mock_download_model:
		_snake_case   						=		None
		with patch('comet.load_from_checkpoint'	) as mock_load_from_checkpoint:
			_snake_case   						=		load_from_checkpoint
			yield







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Union[str, Any]:
	_snake_case   						=		load_metric(os.path.join('metrics'			,				'seqeval'	)	)
	_snake_case   						=		'ERROR'
	_snake_case   						=		F'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'
	with pytest.raises(__A			,				match=re.escape(__A	)	):
		metric.compute(predictions=[]			,				references=[]			,				scheme=__A	)


import argparse
import gc
import json
import os

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


_SCREAMING_SNAKE_CASE										=						16
_SCREAMING_SNAKE_CASE										=						32






def  snake_case  (       snake_case__			:Optional[int])						->   str:
       return int(x / 2**20)



class 				a							:







 """simple docstring"""






 def __enter__(					self							)  ->					List[str]:
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        _A        =  torch.cuda.memory_allocated()
        return self



 def __exit__(					self		,				*lowerCAmelCase_							)  ->					Optional[int]:
        gc.collect()
        torch.cuda.empty_cache()
        _A        =  torch.cuda.memory_allocated()
        _A        =  torch.cuda.max_memory_allocated()
        _A        =  bamb(self.end - self.begin							)
        _A        =  bamb(self.peak - self.begin							)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  snake_case  (       snake_case__			:Accelerator   ,	snake_case__			:int = 16   ,	snake_case__			:str = "bert-base-cased"   ,	snake_case__			:int = 320   ,	snake_case__			:int = 160   ,	)						->   Dict:
       _A        =  AutoTokenizer.from_pretrained(snake_case__)
       _A        =  load_dataset(
           """glue"""   ,	"""mrpc"""   ,	split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''})

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

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

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

       def collate_fn(snake_case__			: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(snake_case__   ,	padding="""max_length"""   ,	max_length=128   ,	return_tensors="""pt""")
              return tokenizer.pad(snake_case__   ,	padding="""longest"""   ,	return_tensors="""pt""")

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

       return train_dataloader, eval_dataloader






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Optional[int])						->   Optional[int]:
       # Initialize accelerator
       _A        =  Accelerator()

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

       set_seed(snake_case__)
       _A   ,  _A        =  get_dataloaders(snake_case__   ,	snake_case__   ,	snake_case__   ,	args.n_train   ,	args.n_val)

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

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

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

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

       # Now we train the model
       _A        =  {}
       for epoch in range(snake_case__   ,	snake_case__):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(snake_case__):
                            _A        =  model(**snake_case__)
                            _A        =  outputs.loss
                            _A        =  loss / gradient_accumulation_steps
                            accelerator.backward(snake_case__)
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
              accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin)))
              accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used))
              accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked))
              accelerator.print(
                  """Total Peak Memory consumed during the train (max): {}""".format(
                      tracemalloc.peaked + bamb(tracemalloc.begin)))
              _A        =  tracemalloc.peaked + bamb(tracemalloc.begin)
              if args.peak_memory_upper_bound is not None:
                     assert (
                         train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
                     ), "Peak memory usage exceeded the upper bound"

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






def  snake_case  (       )						->   Optional[int]:
       _A        =  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
       parser.add_argument(
           """--model_name_or_path"""   ,	type=snake_case__   ,	default="""bert-base-cased"""   ,	help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,	required=snake_case__   ,	)
       parser.add_argument(
           """--output_dir"""   ,	type=snake_case__   ,	default="""."""   ,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,	)
       parser.add_argument(
           """--peak_memory_upper_bound"""   ,	type=snake_case__   ,	default=snake_case__   ,	help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""   ,	)
       parser.add_argument(
           """--n_train"""   ,	type=snake_case__   ,	default=320   ,	help="""Number of training examples to use."""   ,	)
       parser.add_argument(
           """--n_val"""   ,	type=snake_case__   ,	default=160   ,	help="""Number of validation examples to use."""   ,	)
       parser.add_argument(
           """--num_epochs"""   ,	type=snake_case__   ,	default=1   ,	help="""Number of train epochs."""   ,	)
       _A        =  parser.parse_args()
       _A        =  {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(snake_case__   ,	snake_case__)


if __name__ == "__main__":
    main()




