





import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case    :       Union[str, Any]		       =    get_tests_dir('fixtures/spiece.model')



@require_sentencepiece
@require_tokenizers
class 							_UpperCAmelCase       (       UpperCAmelCase__						,       unittest.TestCase       ):

  """simple docstring"""


  a_              =					DebertaVaTokenizer
  a_              =					DebertaVaTokenizerFast
  a_              =					True
  a_              =					True






  def 							lowercase						(							self					:						Any	)				->		Union[str, Any]:
     super().setUp()

     # We have a SentencePiece fixture for testing
     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase   ,    unk_token='<unk>'	)
     tokenizer.save_pretrained(self.tmpdirname	)






  def 							lowercase						(							self					:						Any   ,    lowerCAmelCase_					:						Dict	)				->		Dict:
     __lowerCAmelCase				      =      'this is a test'
     __lowerCAmelCase				      =      'this is a test'
     return input_text, output_text






  def 							lowercase						(							self					:						Dict	)				->		Any:
     __lowerCAmelCase				      =      '<pad>'
     __lowerCAmelCase				      =      0

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






  def 							lowercase						(							self					:						Any	)				->		Dict:
     __lowerCAmelCase				      =      list(self.get_tokenizer().get_vocab().keys()	)
     self.assertEqual(vocab_keys[0]   ,    '<pad>'	)
     self.assertEqual(vocab_keys[1]   ,    '<unk>'	)
     self.assertEqual(vocab_keys[-1]   ,    '[PAD]'	)
     self.assertEqual(len(__lowercase	)   ,    3_0_0_0_1	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		List[Any]:
     self.assertEqual(self.get_tokenizer().vocab_size   ,    3_0_0_0_0	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Tuple:
     __lowerCAmelCase				      =      ' \tHeLLo!how  \n Are yoU?  '
     __lowerCAmelCase				      =      ['▁hello', '!', 'how', '▁are', '▁you', '?']
     # fmt: on

     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase   ,    do_lower_case=__lowercase	)
     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      DebertaVaTokenizerFast(__lowercase   ,    do_lower_case=__lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)






  @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'	)
  def 							lowercase						(							self					:						List[str]	)				->		str:
     pass






  @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'	)
  def 							lowercase						(							self					:						List[Any]	)				->		str:
     pass






  def 							lowercase						(							self					:						Any	)				->		Any:
     __lowerCAmelCase				      =      'I was born in 92000, and this is falsé.'
     __lowerCAmelCase				      =      ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
     # fmt: on

     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      DebertaVaTokenizerFast(__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Tuple:
     __lowerCAmelCase				      =      'I was born in 92000, and this is falsé.'
     __lowerCAmelCase				      =      ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
     # fmt: on

     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase   ,    do_lower_case=__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)
     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      DebertaVaTokenizerFast(__lowercase   ,    do_lower_case=__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)
     self.assertListEqual(__lowercase   ,    __lowercase	)






  def 							lowercase						(							self					:						Optional[int]	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      'I was born in 92000, and this is falsé.'
     __lowerCAmelCase				      =      ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
     # fmt: on

     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase   ,    do_lower_case=__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      DebertaVaTokenizerFast(__lowercase   ,    do_lower_case=__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)






  def 							lowercase						(							self					:						Tuple	)				->		Optional[int]:
     __lowerCAmelCase				      =      'I was born in 92000, and this is falsé.'
     __lowerCAmelCase				      =      ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
     # fmt: on

     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase   ,    do_lower_case=__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      DebertaVaTokenizerFast(__lowercase   ,    do_lower_case=__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)






  def 							lowercase						(							self					:						int	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      ' \tHeLLo!how  \n Are yoU?  '
     __lowerCAmelCase				      =      ['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
     # fmt: on

     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase   ,    do_lower_case=__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      DebertaVaTokenizerFast(__lowercase   ,    do_lower_case=__lowercase   ,    split_by_punct=__lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)

     self.assertListEqual(__lowercase   ,    __lowercase	)






  def 							lowercase						(							self					:						int	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      self.get_tokenizer()
     __lowerCAmelCase				      =      self.get_rust_tokenizer()

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

     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)	)
     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      self.get_rust_tokenizer()
     __lowerCAmelCase				      =      tokenizer.encode(__lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.encode(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)






  def 							lowercase						(							self					:						int	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      'This is a test'
     __lowerCAmelCase				      =      [1_3, 1, 4_3_9_8, 2_5, 2_1, 1_2_8_9]
     __lowerCAmelCase				      =      ['▁', 'T', 'his', '▁is', '▁a', '▁test']
     __lowerCAmelCase				      =      ['▁', '<unk>', 'his', '▁is', '▁a', '▁test']

     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase   ,    keep_accents=__lowercase	)
     __lowerCAmelCase				      =      DebertaVaTokenizerFast(__lowercase   ,    keep_accents=__lowercase	)

     __lowerCAmelCase				      =      tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)
     __lowerCAmelCase				      =      tokenizer.tokenize(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)
     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.tokenize(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)

     # fmt: off
     __lowerCAmelCase				      =      'I was born in 92000, and this is falsé.'
     __lowerCAmelCase				      =      [1_3, 1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]
     __lowerCAmelCase				      =      ['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
     __lowerCAmelCase				      =      ['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
     # fmt: on

     __lowerCAmelCase				      =      tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)
     __lowerCAmelCase				      =      tokenizer.tokenize(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)
     __lowerCAmelCase				      =      tokenizer.convert_ids_to_tokens(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)

     __lowerCAmelCase				      =      rust_tokenizer.encode(__lowercase   ,    add_special_tokens=__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.tokenize(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)
     __lowerCAmelCase				      =      rust_tokenizer.convert_ids_to_tokens(__lowercase	)
     self.assertListEqual(__lowercase   ,    __lowercase	)






  def 							lowercase						(							self					:						Tuple	)				->		Union[str, Any]:
     __lowerCAmelCase				      =      DebertaVaTokenizer(__lowercase	)

     __lowerCAmelCase				      =      tokenizer.encode('sequence builders'	)
     __lowerCAmelCase				      =      tokenizer.encode('multi-sequence build'	)

     __lowerCAmelCase				      =      tokenizer.build_inputs_with_special_tokens(__lowercase	)
     __lowerCAmelCase				      =      tokenizer.build_inputs_with_special_tokens(__lowercase   ,    __lowercase	)

     self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]   ,    __lowercase	)
     self.assertEqual(
         [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]   ,    __lowercase   ,    )






  @slow
  def 							lowercase						(							self					:						List[Any]	)				->		str:
     __lowerCAmelCase				      =      {'input_ids': [[1, 3_9_8_6_7, 3_6, 1_9_3_9_0, 4_8_6, 2_7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 6_0_6_8_5, 1_2_2_5, 7, 3_5_0_5_2, 8_1_4_3_6, 1_8, 9_3_6_7, 1_6_8_9_9, 1_8, 1_5_9_3_7, 5_3, 5_9_4, 7_7_3, 1_8, 1_6_2_8_7, 3_0_4_6_5, 3_6, 1_5_9_3_7, 6, 4_1_1_3_9, 3_8, 3_6_9_7_9, 6_0_7_6_3, 1_9_1, 6, 3_4_1_3_2, 9_9, 6, 5_0_5_3_8, 3_9_0, 4_3_2_3_0, 6, 3_4_1_3_2, 2_7_7_9, 2_0_8_5_0, 1_4, 6_9_9, 1_0_7_2, 1_1_9_4, 3_6, 3_8_2, 1_0_9_0_1, 5_3, 7, 6_9_9, 1_0_7_2, 2_0_8_4, 3_6, 2_0_4_2_2, 6_3_0, 5_3, 1_9, 1_0_5, 3_0_4_9, 1_8_9_6, 1_0_5_3, 1_6_8_9_9, 1_5_0_6, 1_1, 3_7_9_7_8, 4_2_4_3, 7, 1_2_3_7, 3_1_8_6_9, 2_0_0, 1_6_5_6_6, 6_5_4, 6, 3_5_0_5_2, 8_1_4_3_6, 7, 5_5_6_3_0, 1_3_5_9_3, 4, 2], [1, 2_6, 1_5_0_1_1, 1_3, 6_6_7, 8, 1_0_5_3, 1_8, 2_3_6_1_1, 1_2_3_7, 7_2_3_5_6, 1_2_8_2_0, 3_4, 1_0_4_1_3_4, 1_2_0_9, 3_5, 1_3_3_1_3, 6_6_2_7, 2_1, 2_0_2, 3_4_7, 7, 1_6_4, 2_3_9_9, 1_1, 4_6, 4_4_8_5, 4, 2, 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, 5, 1_2_3_2, 2_8_6_4, 1_5_7_8_5, 1_4_9_5_1, 1_0_5, 5, 8_5_8_1, 1_2_5_0, 4, 2, 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]], 'token_type_ids': [[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, 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, 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]], '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, 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, 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=__lowercase   ,    model_name='microsoft/deberta-v2-xlarge'   ,    revision='ad6e42c1532ddf3a15c39246b63f5559d558b670'   ,    )







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__							:	Dict											=		16
lowercase__							:	str											=		32


def 		lowerCamelCase__       (						_A	,      _A = 16	,      _A = "bert-base-cased"				):







	'''simple docstring'''





	snake_case_						       =	AutoTokenizer.from_pretrained(_A				)
	snake_case_						       =	load_dataset("glue"	,      "mrpc"				)

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

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

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

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

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

	return train_dataloader, eval_dataloader


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	Accelerator()

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

	set_seed(_A				)
	snake_case_       , snake_case_						       =	get_dataloaders(_A	,      _A	,      _A				)

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

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

	if accelerator.state.deepspeed_plugin is not None:
		snake_case_						       =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		snake_case_						       =	1
	snake_case_						       =	(len(_A				) * num_epochs) // gradient_accumulation_steps

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

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

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

			overall_step += 1

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

		snake_case_						       =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"epoch {epoch}:"	,      _A				)
		snake_case_						       =	eval_metric["accuracy"]

		if best_performance < eval_metric["accuracy"]:
			snake_case_						       =	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(_A	,      _A				)


def 		lowerCamelCase__       (						):







	'''simple docstring'''





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


if __name__ == "__main__":
		main()
