

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_			      =      get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_sentencepiece
@require_tokenizers
class       snake_case_  (						__A      ,			unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  List[Any]		     =							XLMRobertaTokenizer
			SCREAMING_SNAKE_CASE	:  str		     =							XLMRobertaTokenizerFast
			SCREAMING_SNAKE_CASE	:  Tuple		     =							True
			SCREAMING_SNAKE_CASE	:  List[str]		     =							True







			def 							snake_case__(	self					:							Any				) ->Optional[Any]:
									super().setUp()

									# We have a SentencePiece fixture for testing
									snake_case_  =      XLMRobertaTokenizer(_UpperCamelCase				,  keep_accents=_UpperCamelCase				)
									tokenizer.save_pretrained(self.tmpdirname				)







			def 							snake_case__(	self					:							Optional[int]				) ->int:
									snake_case_  =      '''<pad>'''
									snake_case_  =      1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase				)				,  _UpperCamelCase				)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase				)				,  _UpperCamelCase				)







			def 							snake_case__(	self					:							str				) ->Optional[Any]:
									snake_case_  =      list(self.get_tokenizer().get_vocab().keys()				)

									self.assertEqual(vocab_keys[0]				,  '''<s>'''				)
									self.assertEqual(vocab_keys[1]				,  '''<pad>'''				)
									self.assertEqual(vocab_keys[-1]				,  '''<mask>'''				)
									self.assertEqual(len(_UpperCamelCase				)				,  1_0_0_2				)







			def 							snake_case__(	self					:							Union[str, Any]				) ->Optional[int]:
									self.assertEqual(self.get_tokenizer().vocab_size				,  1_0_0_2				)







			def 							snake_case__(	self					:							int				) ->Any:
									snake_case_  =      XLMRobertaTokenizer(_UpperCamelCase				,  keep_accents=_UpperCamelCase				)

									snake_case_  =      tokenizer.tokenize('''This is a test'''				)
									self.assertListEqual(_UpperCamelCase				,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_UpperCamelCase				)				,  [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]				,  )

									snake_case_  =      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
									self.assertListEqual(
									    _UpperCamelCase				,  [
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''9''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''é''',
									        '''.''',
									    ]				,  )
									snake_case_  =      tokenizer.convert_tokens_to_ids(_UpperCamelCase				)
									self.assertListEqual(
									    _UpperCamelCase				,  [
									        value + tokenizer.fairseq_offset
									        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
									        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
									    ]				,  )

									snake_case_  =      tokenizer.convert_ids_to_tokens(_UpperCamelCase				)
									self.assertListEqual(
									    _UpperCamelCase				,  [
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''<unk>''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''<unk>''',
									        '''.''',
									    ]				,  )







			def 							snake_case__(	self					:							Tuple				) ->Optional[int]:
									if not self.test_slow_tokenizer:
															# as we don't have a slow version, we can't compare the outputs between slow and fast versions
															return

									snake_case_  =      (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
																					snake_case_  =      self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,  **_UpperCamelCase				)
																					snake_case_  =      self.tokenizer_class.from_pretrained(_UpperCamelCase				,  **_UpperCamelCase				)

																					snake_case_  =      tempfile.mkdtemp()

																					snake_case_  =      tokenizer_r.save_pretrained(_UpperCamelCase				)
																					snake_case_  =      tokenizer_p.save_pretrained(_UpperCamelCase				)

																					# Checks it save with the same files + the tokenizer.json file for the fast one
																					self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)
																					snake_case_  =      tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
																					self.assertSequenceEqual(_UpperCamelCase				,  _UpperCamelCase				)

																					# Checks everything loads correctly in the same way
																					snake_case_  =      tokenizer_r.from_pretrained(_UpperCamelCase				)
																					snake_case_  =      tokenizer_p.from_pretrained(_UpperCamelCase				)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(_UpperCamelCase				,  _UpperCamelCase				)				)
																											# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																											# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																					shutil.rmtree(_UpperCamelCase				)

																					# Save tokenizer rust, legacy_format=True
																					snake_case_  =      tempfile.mkdtemp()

																					snake_case_  =      tokenizer_r.save_pretrained(_UpperCamelCase				,  legacy_format=_UpperCamelCase				)
																					snake_case_  =      tokenizer_p.save_pretrained(_UpperCamelCase				)

																					# Checks it save with the same files
																					self.assertSequenceEqual(_UpperCamelCase				,  _UpperCamelCase				)

																					# Checks everything loads correctly in the same way
																					snake_case_  =      tokenizer_r.from_pretrained(_UpperCamelCase				)
																					snake_case_  =      tokenizer_p.from_pretrained(_UpperCamelCase				)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(_UpperCamelCase				,  _UpperCamelCase				)				)

																					shutil.rmtree(_UpperCamelCase				)

																					# Save tokenizer rust, legacy_format=False
																					snake_case_  =      tempfile.mkdtemp()

																					snake_case_  =      tokenizer_r.save_pretrained(_UpperCamelCase				,  legacy_format=_UpperCamelCase				)
																					snake_case_  =      tokenizer_p.save_pretrained(_UpperCamelCase				)

																					# Checks it saved the tokenizer.json file
																					self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)

																					# Checks everything loads correctly in the same way
																					snake_case_  =      tokenizer_r.from_pretrained(_UpperCamelCase				)
																					snake_case_  =      tokenizer_p.from_pretrained(_UpperCamelCase				)

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																											self.assertTrue(hasattr(_UpperCamelCase				,  _UpperCamelCase				)				)

																					shutil.rmtree(_UpperCamelCase				)







			@cached_property
			def 							snake_case__(	self					:							int				) ->List[str]:
									return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)







			def 							snake_case__(	self					:							Any				) ->Union[str, Any]:
									with tempfile.NamedTemporaryFile() as f:
															shutil.copyfile(_UpperCamelCase				,  f.name				)
															snake_case_  =      XLMRobertaTokenizer(f.name				,  keep_accents=_UpperCamelCase				)
															snake_case_  =      pickle.dumps(_UpperCamelCase				)
									pickle.loads(_UpperCamelCase				)







			def 							snake_case__(	self					:							Any				) ->Optional[int]:
									if not self.test_rust_tokenizer:
															return

									snake_case_  =      self.get_tokenizer()
									snake_case_  =      self.get_rust_tokenizer()

									snake_case_  =      '''I was born in 92000, and this is falsé.'''

									snake_case_  =      tokenizer.tokenize(_UpperCamelCase				)
									snake_case_  =      rust_tokenizer.tokenize(_UpperCamelCase				)
									self.assertListEqual(_UpperCamelCase				,  _UpperCamelCase				)

									snake_case_  =      tokenizer.encode(_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				)
									snake_case_  =      rust_tokenizer.encode(_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				)
									self.assertListEqual(_UpperCamelCase				,  _UpperCamelCase				)

									snake_case_  =      self.get_rust_tokenizer()
									snake_case_  =      tokenizer.encode(_UpperCamelCase				)
									snake_case_  =      rust_tokenizer.encode(_UpperCamelCase				)
									self.assertListEqual(_UpperCamelCase				,  _UpperCamelCase				)







			@slow
			def 							snake_case__(	self					:							Optional[Any]				) ->str:
									snake_case_  =      '''Hello World!'''
									snake_case_  =      [0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(_UpperCamelCase				,  self.big_tokenizer.encode(_UpperCamelCase				)				)







			@slow
			def 							snake_case__(	self					:							str				) ->Tuple:
									snake_case_  =      (
									    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
									    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
									)
									snake_case_  =      [
									    0,
									    3_2_9_3,
									    8_3,
									    1_0,
									    4_5_5_2,
									    4_9_8_9,
									    7_9_8_6,
									    6_7_8,
									    1_0,
									    5_9_1_5,
									    1_1_1,
									    1_7_9_4_5_9,
									    1_2_4_8_5_0,
									    4,
									    6_0_4_4,
									    2_3_7,
									    1_2,
									    6,
									    5,
									    6,
									    4,
									    6_7_8_0,
									    7_0_5,
									    1_5,
									    1_3_8_8,
									    4_4,
									    3_7_8,
									    1_0_1_1_4,
									    7_1_1,
									    1_5_2,
									    2_0,
									    6,
									    5,
									    2_2_3_7_6,
									    6_4_2,
									    1_2_2_1,
									    1_5_1_9_0,
									    3_4_1_5_3,
									    4_5_0,
									    5_6_0_8,
									    9_5_9,
									    1_1_1_9,
									    5_7_7_0_2,
									    1_3_6,
									    1_8_6,
									    4_7,
									    1_0_9_8,
									    2_9_3_6_7,
									    4_7,
									    # 4426, # What fairseq tokenizes from "<unk>": "_<"
									    # 3678, # What fairseq tokenizes from "<unk>": "unk"
									    # 2740, # What fairseq tokenizes from "<unk>": ">"
									    3,  # What we tokenize from "<unk>": "<unk>"
									    6,  # Residue from the tokenization: an extra sentencepiece underline
									    4,
									    6_0_4_4,
									    2_3_7,
									    6_2_8_4,
									    5_0_9_0_1,
									    5_2_8,
									    3_1,
									    9_0,
									    3_4,
									    9_2_7,
									    2,
									]
									# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
									# xlmr.eval()
									# xlmr.encode(symbols)

									self.assertListEqual(_UpperCamelCase				,  self.big_tokenizer.encode(_UpperCamelCase				)				)







			@slow
			def 							snake_case__(	self					:							Dict				) ->List[str]:
									# fmt: off
									snake_case_  =      {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCamelCase				,  model_name='''xlm-roberta-base'''				,  revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''				,  )


"""simple docstring"""

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


_UpperCamelCase		     =    1_6
_UpperCamelCase		     =    3_2



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						str:
 return int(x / 2**2_0   )






class 			__a					:





       """simple docstring"""


       def __enter__(   self   ):





        """simple docstring"""

        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        lowerCAmelCase__   :				str 			=				torch.cuda.memory_allocated()
        return self






       def __exit__(   self			,					*snake_case   ):





        """simple docstring"""

        gc.collect()
        torch.cuda.empty_cache()
        lowerCAmelCase__   :				List[str] 			=				torch.cuda.memory_allocated()
        lowerCAmelCase__   :				Optional[int] 			=				torch.cuda.max_memory_allocated()
        lowerCAmelCase__   :				List[str] 			=				bamb(self.end - self.begin   )
        lowerCAmelCase__   :				Union[str, Any] 			=				bamb(self.peak - self.begin   )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__ = 1_6					,					lowercase__ = "bert-base-cased"					,					lowercase__ = 3_2_0					,					lowercase__ = 1_6_0					,					)			->						str:
 lowerCAmelCase__   :				Union[str, Any] 			=				AutoTokenizer.from_pretrained(lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				load_dataset(
     "glue"					,					"mrpc"					,					split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

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

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

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

 def collate_fn(lowercase__   ):
  # 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(lowercase__					,					padding="max_length"					,					max_length=1_2_8					,					return_tensors="pt"   )
  return tokenizer.pad(lowercase__					,					padding="longest"					,					return_tensors="pt"   )

 # Instantiate dataloaders.
 lowerCAmelCase__   :				Tuple 			=				DataLoader(
     tokenized_datasets["train"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )
 lowerCAmelCase__   :				Optional[Any] 			=				DataLoader(
     tokenized_datasets["validation"]					,					shuffle=lowercase__					,					collate_fn=lowercase__					,					batch_size=lowercase__   )

 return train_dataloader, eval_dataloader



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						Dict:
 # Initialize accelerator
 lowerCAmelCase__   :				Optional[Any] 			=				Accelerator()

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

 set_seed(lowercase__   )
 lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[Any] 			=				get_dataloaders(lowercase__					,					lowercase__					,					lowercase__					,					args.n_train					,					args.n_val   )

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

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

 if accelerator.state.deepspeed_plugin is not None:
  lowerCAmelCase__   :				List[str] 			=				accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  lowerCAmelCase__   :				Dict 			=				1
 lowerCAmelCase__   :				List[str] 			=				(len(lowercase__   ) * 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=lowercase__					,					num_warmup_steps=0					,					num_training_steps=lowercase__					,					)
 else:
  lowerCAmelCase__   :				Optional[int] 			=				DummyScheduler(lowercase__					,					total_num_steps=lowercase__					,					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__   :				Dict 			=				accelerator.prepare(
     lowercase__					,					lowercase__					,					lowercase__					,					lowercase__					,					lowercase__   )

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

 # Now we train the model
 lowerCAmelCase__   :				List[Any] 			=				{}
 for epoch in range(lowercase__					,					lowercase__   ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(lowercase__   ):
    lowerCAmelCase__   :				Optional[Any] 			=				model(**lowercase__   )
    lowerCAmelCase__   :				Optional[int] 			=				outputs.loss
    lowerCAmelCase__   :				Any 			=				loss / gradient_accumulation_steps
    accelerator.backward(lowercase__   )
    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   )   )   )
  lowerCAmelCase__   :				Optional[int] 			=				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(lowercase__					,					lowercase__   )



def       SCREAMING_SNAKE_CASE							( )			->						Optional[Any]:
 lowerCAmelCase__   :				int 			=				argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
 parser.add_argument(
     "--model_name_or_path"					,					type=lowercase__					,					default="bert-base-cased"					,					help="Path to pretrained model or model identifier from huggingface.co/models."					,					required=lowercase__					,					)
 parser.add_argument(
     "--output_dir"					,					type=lowercase__					,					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=lowercase__					,					default=lowercase__					,					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=lowercase__					,					default=3_2_0					,					help="Number of training examples to use."					,					)
 parser.add_argument(
     "--n_val"					,					type=lowercase__					,					default=1_6_0					,					help="Number of validation examples to use."					,					)
 parser.add_argument(
     "--num_epochs"					,					type=lowercase__					,					default=1					,					help="Number of train epochs."					,					)
 lowerCAmelCase__   :				Optional[int] 			=				parser.parse_args()
 lowerCAmelCase__   :				Dict 			=				{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
 training_function(lowercase__					,					lowercase__   )


if __name__ == "__main__":
    main()






