import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase_           =				2_5_6_0_4_7
UpperCAmelCase_           =				2_5_6_1_4_5




@require_sentencepiece
@require_tokenizers
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Dict	      = NllbTokenizer
   lowerCAmelCase   :     Dict	      = NllbTokenizerFast
   lowerCAmelCase   :     Any	      = True
   lowerCAmelCase   :     str	      = True
   lowerCAmelCase   :     List[Any]	      = {}



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    super().setUp()

    # We have a SentencePiece fixture for testing
    _UpperCamelCase:  Any    						=			NllbTokenizer(_lowercase		,	keep_accents=_lowercase				)
    tokenizer.save_pretrained(self.tmpdirname				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			NllbTokenizer(_lowercase		,	keep_accents=_lowercase				)

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

    self.assertListEqual(
        tokenizer.convert_tokens_to_ids(_lowercase				)		,	[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,	)

    _UpperCamelCase:  Dict    						=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
    self.assertListEqual(
        _lowercase		,	[
            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''',
            '''.''',
        ]		,	)
    _UpperCamelCase:  Optional[Any]    						=			tokenizer.convert_tokens_to_ids(_lowercase				)
    self.assertListEqual(
        _lowercase		,	[
            value + tokenizer.fairseq_offset
            for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
        ]		,	)

    _UpperCamelCase:  int    						=			tokenizer.convert_ids_to_tokens(_lowercase				)
    self.assertListEqual(
        _lowercase		,	[
            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 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
      _UpperCamelCase:  str    						=			self.rust_tokenizer_class.from_pretrained(_lowercase		,	**_lowercase				)
      _UpperCamelCase:  int    						=			self.tokenizer_class.from_pretrained(_lowercase		,	**_lowercase				)

      _UpperCamelCase:  List[Any]    						=			tempfile.mkdtemp()

      _UpperCamelCase:  List[Any]    						=			tokenizer_r.save_pretrained(_lowercase				)
      _UpperCamelCase:  int    						=			tokenizer_p.save_pretrained(_lowercase				)

      # 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				)				)
      _UpperCamelCase:  str    						=			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
      self.assertSequenceEqual(_lowercase		,	_lowercase				)

      # Checks everything loads correctly in the same way
      _UpperCamelCase:  Union[str, Any]    						=			tokenizer_r.from_pretrained(_lowercase				)
      _UpperCamelCase:  List[Any]    						=			tokenizer_p.from_pretrained(_lowercase				)

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

      shutil.rmtree(_lowercase				)

      # Save tokenizer rust, legacy_format=True
      _UpperCamelCase:  Optional[int]    						=			tempfile.mkdtemp()

      _UpperCamelCase:  str    						=			tokenizer_r.save_pretrained(_lowercase		,	legacy_format=_lowercase				)
      _UpperCamelCase:  Union[str, Any]    						=			tokenizer_p.save_pretrained(_lowercase				)

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

      # Checks everything loads correctly in the same way
      _UpperCamelCase:  Any    						=			tokenizer_r.from_pretrained(_lowercase				)
      _UpperCamelCase:  Union[str, Any]    						=			tokenizer_p.from_pretrained(_lowercase				)

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

      shutil.rmtree(_lowercase				)

      # Save tokenizer rust, legacy_format=False
      _UpperCamelCase:  List[str]    						=			tempfile.mkdtemp()

      _UpperCamelCase:  Optional[int]    						=			tokenizer_r.save_pretrained(_lowercase		,	legacy_format=_lowercase				)
      _UpperCamelCase:  Optional[int]    						=			tokenizer_p.save_pretrained(_lowercase				)

      # 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
      _UpperCamelCase:  List[Any]    						=			tokenizer_r.from_pretrained(_lowercase				)
      _UpperCamelCase:  str    						=			tokenizer_p.from_pretrained(_lowercase				)

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

      shutil.rmtree(_lowercase				)



   @require_torch
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    if not self.test_seqaseq:
     return

    _UpperCamelCase:  int    						=			self.get_tokenizers()
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
      # Longer text that will definitely require truncation.
      _UpperCamelCase:  Union[str, Any]    						=			[
          ''' UN Chief Says There Is No Military Solution in Syria''',
          ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
          ''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
          ''' will only worsen the violence and misery for millions of people.''',
      ]
      _UpperCamelCase:  List[str]    						=			[
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
          '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
          ''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
          ''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
      ]
      try:
       _UpperCamelCase:  List[Any]    						=			tokenizer.prepare_seqaseq_batch(
           src_texts=_lowercase		,	tgt_texts=_lowercase		,	max_length=3		,	max_target_length=10		,	return_tensors='''pt'''		,	src_lang='''eng_Latn'''		,	tgt_lang='''ron_Latn'''		,	)
      except NotImplementedError:
       return
      self.assertEqual(batch.input_ids.shape[1]		,	3				)
      self.assertEqual(batch.labels.shape[1]		,	10				)
      # max_target_length will default to max_length if not specified
      _UpperCamelCase:  str    						=			tokenizer.prepare_seqaseq_batch(
          _lowercase		,	tgt_texts=_lowercase		,	max_length=3		,	return_tensors='''pt'''				)
      self.assertEqual(batch.input_ids.shape[1]		,	3				)
      self.assertEqual(batch.labels.shape[1]		,	3				)

      _UpperCamelCase:  Tuple    						=			tokenizer.prepare_seqaseq_batch(
          src_texts=_lowercase		,	max_length=3		,	max_target_length=10		,	return_tensors='''pt'''				)
      self.assertEqual(batch_encoder_only.input_ids.shape[1]		,	3				)
      self.assertEqual(batch_encoder_only.attention_mask.shape[1]		,	3				)
      self.assertNotIn('''decoder_input_ids'''		,	_lowercase				)



   @unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.'''				)
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
      _UpperCamelCase:  Union[str, Any]    						=			[AddedToken('''<special>'''		,	lstrip=_lowercase				)]

      _UpperCamelCase:  Tuple    						=			self.rust_tokenizer_class.from_pretrained(
          _lowercase		,	additional_special_tokens=_lowercase		,	**_lowercase				)
      _UpperCamelCase:  Tuple    						=			tokenizer_r.encode('''Hey this is a <special> token'''				)

      _UpperCamelCase:  Union[str, Any]    						=			tokenizer_r.encode('''<special>'''		,	add_special_tokens=_lowercase				)[0]

      self.assertTrue(special_token_id in r_output				)

      if self.test_slow_tokenizer:
       _UpperCamelCase:  Any    						=			self.rust_tokenizer_class.from_pretrained(
           _lowercase		,	additional_special_tokens=_lowercase		,	**_lowercase		,	)
       _UpperCamelCase:  Dict    						=			self.tokenizer_class.from_pretrained(
           _lowercase		,	additional_special_tokens=_lowercase		,	**_lowercase				)

       _UpperCamelCase:  Optional[Any]    						=			tokenizer_p.encode('''Hey this is a <special> token'''				)

       _UpperCamelCase:  Union[str, Any]    						=			tokenizer_cr.encode('''Hey this is a <special> token'''				)

       self.assertEqual(_lowercase		,	_lowercase				)
       self.assertEqual(_lowercase		,	_lowercase				)
       self.assertTrue(special_token_id in p_output				)
       self.assertTrue(special_token_id in cr_output				)




@require_torch
@require_sentencepiece
@require_tokenizers
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Union[str, Any]	      = '''facebook/nllb-200-distilled-600M'''
   lowerCAmelCase   :     Union[str, Any]	      = [
       ''' UN Chief Says There Is No Military Solution in Syria''',
       ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
   ]
   lowerCAmelCase   :     int	      = [
       '''Şeful ONU declară că nu există o soluţie militară în Siria''',
       '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
       ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
       ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
   ]
   lowerCAmelCase   :     List[str]	      = [
       2_5_6_0_4_7,
       1_6_2_9_7,
       1_3_4_4_0_8,
       8_1_6_5,
       2_4_8_0_6_6,
       1_4_7_3_4,
       9_5_0,
       1_1_3_5,
       1_0_5_7_2_1,
       3_5_7_3,
       8_3,
       2_7_3_5_2,
       1_0_8,
       4_9_4_8_6,
       2,
   ]



   @classmethod
   def 	lowerCAmelCase    (						cls       :	int				):



    """simple docstring"""



    _UpperCamelCase:  NllbTokenizer    						=			NllbTokenizer.from_pretrained(
        cls.checkpoint_name		,	src_lang='''eng_Latn'''		,	tgt_lang='''ron_Latn'''				)
    _UpperCamelCase:  Union[str, Any]    						=			1
    return cls



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab''']		,	256_001				)
    self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn''']		,	256_002				)
    self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn''']		,	256_057				)



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
    self.assertListEqual(self.expected_src_tokens		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    self.assertIn(_lowercase		,	self.tokenizer.all_special_ids				)
    # fmt: off
    _UpperCamelCase:  List[Any]    						=			[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
    # fmt: on

    _UpperCamelCase:  Tuple    						=			self.tokenizer.decode(_lowercase		,	skip_special_tokens=_lowercase				)
    _UpperCamelCase:  Tuple    						=			self.tokenizer.decode(generated_ids[1:]		,	skip_special_tokens=_lowercase				)
    self.assertEqual(_lowercase		,	_lowercase				)
    self.assertNotIn(self.tokenizer.eos_token		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			['''this is gunna be a long sentence ''' * 20]
    assert isinstance(src_text[0]		,	_lowercase				)
    _UpperCamelCase:  Union[str, Any]    						=			10
    _UpperCamelCase:  Optional[Any]    						=			self.tokenizer(_lowercase		,	max_length=_lowercase		,	truncation=_lowercase				).input_ids[0]
    self.assertEqual(ids[-1]		,	2				)
    self.assertEqual(ids[0]		,	_lowercase				)
    self.assertEqual(len(_lowercase				)		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']				)		,	[256_203, 3]				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			tempfile.mkdtemp()
    _UpperCamelCase:  Any    						=			self.tokenizer.fairseq_tokens_to_ids
    self.tokenizer.save_pretrained(_lowercase				)
    _UpperCamelCase:  int    						=			NllbTokenizer.from_pretrained(_lowercase				)
    self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,	_lowercase				)



   @require_torch
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.tokenizer(
        self.src_text		,	text_target=self.tgt_text		,	padding=_lowercase		,	truncation=_lowercase		,	max_length=len(self.expected_src_tokens				)		,	return_tensors='''pt'''		,	)
    _UpperCamelCase:  Optional[Any]    						=			shift_tokens_right(
        batch['''labels''']		,	self.tokenizer.pad_token_id		,	self.tokenizer.lang_code_to_id['''ron_Latn''']				)

    self.assertIsInstance(_lowercase		,	_lowercase				)

    self.assertEqual((2, 15)		,	batch.input_ids.shape				)
    self.assertEqual((2, 15)		,	batch.attention_mask.shape				)
    _UpperCamelCase:  str    						=			batch.input_ids.tolist()[0]
    self.assertListEqual(self.expected_src_tokens		,	_lowercase				)
    self.assertEqual(_lowercase		,	batch.decoder_input_ids[0, 0]				)  # EOS
    # Test that special tokens are reset
    self.assertEqual(self.tokenizer.prefix_tokens		,	[EN_CODE]				)
    self.assertEqual(self.tokenizer.suffix_tokens		,	[self.tokenizer.eos_token_id]				)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			self.tokenizer(self.src_text		,	padding=_lowercase		,	truncation=_lowercase		,	max_length=3		,	return_tensors='''pt'''				)
    _UpperCamelCase:  Any    						=			self.tokenizer(
        text_target=self.tgt_text		,	padding=_lowercase		,	truncation=_lowercase		,	max_length=10		,	return_tensors='''pt'''				)
    _UpperCamelCase:  Union[str, Any]    						=			targets['''input_ids''']
    _UpperCamelCase:  Dict    						=			shift_tokens_right(
        _lowercase		,	self.tokenizer.pad_token_id		,	decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]		,	)

    self.assertEqual(batch.input_ids.shape[1]		,	3				)
    self.assertEqual(batch.decoder_input_ids.shape[1]		,	10				)



   @require_torch
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			self.tokenizer._build_translation_inputs(
        '''A test'''		,	return_tensors='''pt'''		,	src_lang='''eng_Latn'''		,	tgt_lang='''fra_Latn'''				)

    self.assertEqual(
        nested_simplify(_lowercase				)		,	{
            # A, test, EOS, en_XX
            '''input_ids''': [[256_047, 70, 7_356, 2]],
            '''attention_mask''': [[1, 1, 1, 1]],
            # ar_AR
            '''forced_bos_token_id''': 256_057,
        }		,	)



   @require_torch
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			True
    _UpperCamelCase:  Union[str, Any]    						=			self.tokenizer(
        '''UN Chief says there is no military solution in Syria'''		,	src_lang='''eng_Latn'''		,	tgt_lang='''fra_Latn'''				)
    self.assertEqual(
        inputs.input_ids		,	[16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]				)

    _UpperCamelCase:  List[str]    						=			False
    _UpperCamelCase:  List[str]    						=			self.tokenizer(
        '''UN Chief says there is no military solution in Syria'''		,	src_lang='''eng_Latn'''		,	tgt_lang='''fra_Latn'''				)
    self.assertEqual(
        inputs.input_ids		,	[256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]				)
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def    lowerCAmelCase_  (		lowercase:				Dict		)     ->					Any:







 '''simple docstring'''
 _UpperCamelCase:  Optional[Any]    						=			fname.split(os.path.sep		)[-1]
 return re.search(R'''^(.*)_\d+\.jpg$'''	,       lowercase		).groups()[0]




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   def __init__(						self       :	Dict		,	_lowercase       :	Any		,	_lowercase       :	Any=None		,	_lowercase       :	List[str]=None				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			file_names
    _UpperCamelCase:  List[Any]    						=			image_transform
    _UpperCamelCase:  Tuple    						=			label_to_id



   def __len__(						self       :	List[str]				):



    """simple docstring"""



    return len(self.file_names				)



   def __getitem__(						self       :	List[str]		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			self.file_names[idx]
    _UpperCamelCase:  Optional[int]    						=			PIL.Image.open(_lowercase				)
    _UpperCamelCase:  List[str]    						=			raw_image.convert('''RGB'''				)
    if self.image_transform is not None:
     _UpperCamelCase:  Optional[Any]    						=			self.image_transform(_lowercase				)
    _UpperCamelCase:  Tuple    						=			extract_label(_lowercase				)
    if self.label_to_id is not None:
     _UpperCamelCase:  Any    						=			self.label_to_id[label]
    return {"image": image, "label": label}




def    lowerCAmelCase_  (		lowercase:				Optional[Any]	,       lowercase:				Any		)     ->					str:







 '''simple docstring'''
 # Initialize accelerator
 if args.with_tracking:
  _UpperCamelCase:  Tuple    						=			Accelerator(
      cpu=args.cpu	,       mixed_precision=args.mixed_precision	,       log_with='''all'''	,       project_dir=args.project_dir		)
 else:
  _UpperCamelCase:  Dict    						=			Accelerator(cpu=args.cpu	,       mixed_precision=args.mixed_precision		)

 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 _UpperCamelCase:  List[str]    						=			config['''lr''']
 _UpperCamelCase:  int    						=			int(config['''num_epochs''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''seed''']		)
 _UpperCamelCase:  Optional[Any]    						=			int(config['''batch_size''']		)
 _UpperCamelCase:  List[str]    						=			config['''image_size''']
 if not isinstance(lowercase	,       (list, tuple)		):
  _UpperCamelCase:  Optional[int]    						=			(image_size, image_size)

 # Parse out whether we are saving every epoch or after a certain number of batches
 if hasattr(args.checkpointing_steps	,       '''isdigit'''		):
  if args.checkpointing_steps == "epoch":
   _UpperCamelCase:  Any    						=			args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   _UpperCamelCase:  List[str]    						=			int(args.checkpointing_steps		)
  else:
   raise ValueError(
       F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""		)
 else:
  _UpperCamelCase:  Any    						=			None

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  _UpperCamelCase:  Union[str, Any]    						=			os.path.split(lowercase		)[-1].split('''.'''		)[0]
  accelerator.init_trackers(lowercase	,       lowercase		)

 # Grab all the image filenames
 _UpperCamelCase:  List[str]    						=			[os.path.join(args.data_dir	,       lowercase		) for fname in os.listdir(args.data_dir		) if fname.endswith('''.jpg'''		)]

 # Build the label correspondences
 _UpperCamelCase:  Optional[Any]    						=			[extract_label(lowercase		) for fname in file_names]
 _UpperCamelCase:  int    						=			list(set(lowercase		)		)
 id_to_label.sort()
 _UpperCamelCase:  Tuple    						=			{lbl: i for i, lbl in enumerate(lowercase		)}

 # Set the seed before splitting the data.
 np.random.seed(lowercase		)
 torch.manual_seed(lowercase		)
 torch.cuda.manual_seed_all(lowercase		)

 # Split our filenames between train and validation
 _UpperCamelCase:  List[str]    						=			np.random.permutation(len(lowercase		)		)
 _UpperCamelCase:  Dict    						=			int(0.8 * len(lowercase		)		)
 _UpperCamelCase:  Optional[int]    						=			random_perm[:cut]
 _UpperCamelCase:  Optional[int]    						=			random_perm[cut:]

 # For training we use a simple RandomResizedCrop
 _UpperCamelCase:  List[Any]    						=			Compose([RandomResizedCrop(lowercase	,       scale=(0.5, 1.0)		), ToTensor()]		)
 _UpperCamelCase:  int    						=			PetsDataset(
     [file_names[i] for i in train_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # For evaluation, we use a deterministic Resize
 _UpperCamelCase:  Dict    						=			Compose([Resize(lowercase		), ToTensor()]		)
 _UpperCamelCase:  str    						=			PetsDataset([file_names[i] for i in eval_split]	,       image_transform=lowercase	,       label_to_id=lowercase		)

 # Instantiate dataloaders.
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)
 _UpperCamelCase:  str    						=			DataLoader(lowercase	,       shuffle=lowercase	,       batch_size=lowercase	,       num_workers=4		)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 _UpperCamelCase:  str    						=			create_model('''resnet50d'''	,       pretrained=lowercase	,       num_classes=len(lowercase		)		)

 # 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).
 _UpperCamelCase:  Optional[int]    						=			model.to(accelerator.device		)

 # Freezing the base model
 for param in model.parameters():
  _UpperCamelCase:  Optional[Any]    						=			False
 for param in model.get_classifier().parameters():
  _UpperCamelCase:  Optional[int]    						=			True

 # We normalize the batches of images to be a bit faster.
 _UpperCamelCase:  Union[str, Any]    						=			torch.tensor(model.default_cfg['''mean''']		)[None, :, None, None].to(accelerator.device		)
 _UpperCamelCase:  List[Any]    						=			torch.tensor(model.default_cfg['''std''']		)[None, :, None, None].to(accelerator.device		)

 # Instantiate optimizer
 _UpperCamelCase:  List[str]    						=			torch.optim.Adam(params=model.parameters()	,       lr=lr / 25		)

 # Instantiate learning rate scheduler
 _UpperCamelCase:  Any    						=			OneCycleLR(optimizer=lowercase	,       max_lr=lowercase	,       epochs=lowercase	,       steps_per_epoch=len(lowercase		)		)

 # 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:  List[str]    						=			accelerator.prepare(
     lowercase	,       lowercase	,       lowercase	,       lowercase	,       lowercase		)
 # We need to keep track of how many total steps we have iterated over
 _UpperCamelCase:  Tuple    						=			0
 # We also need to keep track of the starting epoch so files are named properly
 _UpperCamelCase:  List[str]    						=			0

 # Potentially load in the weights and states from a previous save
 if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
   accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""		)
   accelerator.load_state(args.resume_from_checkpoint		)
   _UpperCamelCase:  Union[str, Any]    						=			os.path.basename(args.resume_from_checkpoint		)
  else:
   # Get the most recent checkpoint
   _UpperCamelCase:  Union[str, Any]    						=			[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
   dirs.sort(key=os.path.getctime		)
   _UpperCamelCase:  Union[str, Any]    						=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  _UpperCamelCase:  List[Any]    						=			os.path.splitext(lowercase		)[0]

  if "epoch" in training_difference:
   _UpperCamelCase:  Any    						=			int(training_difference.replace('''epoch_'''	,       ''''''		)		) + 1
   _UpperCamelCase:  List[str]    						=			None
  else:
   _UpperCamelCase:  int    						=			int(training_difference.replace('''step_'''	,       ''''''		)		)
   _UpperCamelCase:  List[Any]    						=			resume_step // len(lowercase		)
   resume_step -= starting_epoch * len(lowercase		)

    # Now we train the model
 for epoch in range(lowercase	,       lowercase		):
  model.train()
  if args.with_tracking:
   _UpperCamelCase:  Optional[int]    						=			0
  if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
   # We need to skip steps until we reach the resumed step
   _UpperCamelCase:  Union[str, Any]    						=			accelerator.skip_first_batches(lowercase	,       lowercase		)
   overall_step += resume_step
  else:
   # After the first iteration though, we need to go back to the original dataloader
   _UpperCamelCase:  Optional[int]    						=			train_dataloader
  for batch in active_dataloader:
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  int    						=			(batch['''image'''] - mean) / std
   _UpperCamelCase:  List[str]    						=			model(lowercase		)
   _UpperCamelCase:  Dict    						=			torch.nn.functional.cross_entropy(lowercase	,       batch['''label''']		)
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(lowercase		)
   optimizer.step()
   lr_scheduler.step()
   optimizer.zero_grad()
   overall_step += 1
   if isinstance(lowercase	,       lowercase		):
    _UpperCamelCase:  Any    						=			F"""step_{overall_step}"""
    if overall_step % checkpointing_steps == 0:
     if args.output_dir is not None:
      _UpperCamelCase:  List[Any]    						=			os.path.join(args.output_dir	,       lowercase		)
     accelerator.save_state(lowercase		)
  model.eval()
  _UpperCamelCase:  List[str]    						=			0
  _UpperCamelCase:  Union[str, Any]    						=			0
  for step, batch in enumerate(lowercase		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   _UpperCamelCase:  Optional[Any]    						=			{k: v.to(accelerator.device		) for k, v in batch.items()}
   _UpperCamelCase:  Union[str, Any]    						=			(batch['''image'''] - mean) / std
   with torch.no_grad():
    _UpperCamelCase:  List[Any]    						=			model(lowercase		)
   _UpperCamelCase:  Optional[Any]    						=			outputs.argmax(dim=-1		)
   _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			accelerator.gather_for_metrics((predictions, batch['''label'''])		)
   _UpperCamelCase:  List[str]    						=			predictions == references
   num_elems += accurate_preds.shape[0]
   accurate += accurate_preds.long().sum()

  _UpperCamelCase:  Optional[int]    						=			accurate.item() / num_elems
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""		)
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': 100 * eval_metric,
           '''train_loss''': total_loss.item() / len(lowercase		),
           '''epoch''': epoch,
       }	,       step=lowercase	,       )
  if checkpointing_steps == "epoch":
   _UpperCamelCase:  Tuple    						=			F"""epoch_{epoch}"""
   if args.output_dir is not None:
    _UpperCamelCase:  Any    						=			os.path.join(args.output_dir	,       lowercase		)
   accelerator.save_state(lowercase		)

 if args.with_tracking:
  accelerator.end_training()




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







 '''simple docstring'''
 _UpperCamelCase:  str    						=			argparse.ArgumentParser(description='''Simple example of training script.'''		)
 parser.add_argument('''--data_dir'''	,       required=lowercase	,       help='''The data folder on disk.'''		)
 parser.add_argument('''--fp16'''	,       action='''store_true'''	,       help='''If passed, will use FP16 training.'''		)
 parser.add_argument(
     '''--mixed_precision'''	,       type=lowercase	,       default=lowercase	,       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.'''	,       )
 parser.add_argument('''--cpu'''	,       action='''store_true'''	,       help='''If passed, will train on the CPU.'''		)
 parser.add_argument(
     '''--checkpointing_steps'''	,       type=lowercase	,       default=lowercase	,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,       )
 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(
     '''--resume_from_checkpoint'''	,       type=lowercase	,       default=lowercase	,       help='''If the training should continue from a checkpoint folder.'''	,       )
 parser.add_argument(
     '''--with_tracking'''	,       action='''store_true'''	,       help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,       )
 parser.add_argument(
     '''--project_dir'''	,       type=lowercase	,       default='''logs'''	,       help='''Location on where to store experiment tracking logs` and relevent project information'''	,       )
 _UpperCamelCase:  Any    						=			parser.parse_args()
 _UpperCamelCase:  Optional[int]    						=			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
 training_function(lowercase	,       lowercase		)


if __name__ == "__main__":
  main()