

import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel






class 						__magic_name__    :


 def __init__(						self		: int			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: List[Any]=9_9			,						lowerCAmelCase__		: int=1_3			,						lowerCAmelCase__		: Optional[Any]=7			,						lowerCAmelCase__		: Optional[int]=9			,						lowerCAmelCase__		: List[Any]=True			,						lowerCAmelCase__		: Union[str, Any]=True			,						lowerCAmelCase__		: int=False			,						lowerCAmelCase__		: Optional[int]=3_2			,						lowerCAmelCase__		: Union[str, Any]=5			,						lowerCAmelCase__		: Tuple=4			,						lowerCAmelCase__		: int=3_7			,						lowerCAmelCase__		: Any=8			,						lowerCAmelCase__		: List[str]=0.1			,						lowerCAmelCase__		: Tuple=0.002			,						lowerCAmelCase__		: Any=1			,						lowerCAmelCase__		: List[Any]=0			,						lowerCAmelCase__		: List[str]=0			,						lowerCAmelCase__		: int=None			,						lowerCAmelCase__		: List[str]=None			,						)    ->      List[str]:
   UpperCAmelCase					      =  parent
   UpperCAmelCase					      =  batch_size
   UpperCAmelCase					      =  encoder_seq_length
   UpperCAmelCase					      =  decoder_seq_length
   # For common tests
   UpperCAmelCase					      =  self.decoder_seq_length
   UpperCAmelCase					      =  is_training
   UpperCAmelCase					      =  use_attention_mask
   UpperCAmelCase					      =  use_labels
   UpperCAmelCase					      =  vocab_size
   UpperCAmelCase					      =  hidden_size
   UpperCAmelCase					      =  num_hidden_layers
   UpperCAmelCase					      =  num_attention_heads
   UpperCAmelCase					      =  d_ff
   UpperCAmelCase					      =  relative_attention_num_buckets
   UpperCAmelCase					      =  dropout_rate
   UpperCAmelCase					      =  initializer_factor
   UpperCAmelCase					      =  eos_token_id
   UpperCAmelCase					      =  pad_token_id
   UpperCAmelCase					      =  decoder_start_token_id
   UpperCAmelCase					      =  None
   UpperCAmelCase					      =  decoder_layers


 def _UpperCamelCase	(						self		: int    )    ->      List[Any]:
   return TaConfig.from_pretrained("google/umt5-base"    )


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Optional[Any]			,						lowerCAmelCase__		: List[str]=None			,						lowerCAmelCase__		: str=None			,						lowerCAmelCase__		: Tuple=None			,						lowerCAmelCase__		: Dict=None			,						lowerCAmelCase__		: Optional[int]=None			,						)    ->      str:
   if attention_mask is None:
     UpperCAmelCase					      =  input_ids.ne(config.pad_token_id    )
   if decoder_attention_mask is None:
     UpperCAmelCase					      =  decoder_input_ids.ne(config.pad_token_id    )
   if head_mask is None:
     UpperCAmelCase					      =  torch.ones(config.num_hidden_layers			,						config.num_attention_heads			,						device=lowerCAmelCase__    )
   if decoder_head_mask is None:
     UpperCAmelCase					      =  torch.ones(config.num_decoder_layers			,						config.num_attention_heads			,						device=lowerCAmelCase__    )
   if cross_attn_head_mask is None:
     UpperCAmelCase					      =  torch.ones(
         config.num_decoder_layers			,						config.num_attention_heads			,						device=lowerCAmelCase__    )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


 def _UpperCamelCase	(						self		: Dict    )    ->      Any:
   UpperCAmelCase					      =  ids_tensor([self.batch_size, self.encoder_seq_length]			,						self.vocab_size    )
   UpperCAmelCase					      =  ids_tensor([self.batch_size, self.decoder_seq_length]			,						self.vocab_size    )

   # we need to clamp the input ids here to avoid having pad token in between
   # this is because for NllbMoe the position_ids are prepared such that
   # all pad tokens have pos id = 2 and rest are between 2..seq_length
   # and the seq_length here is seq_length - num_pad_tokens
   # but when using past, there is no way of knowing if the past input ids had
   # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
   # position_ids being off by num_pad_tokens in past input
   UpperCAmelCase					      =  input_ids.clamp(self.pad_token_id + 1    )
   UpperCAmelCase					      =  decoder_input_ids.clamp(self.pad_token_id + 1    )

   UpperCAmelCase					      =  self.get_config()
   UpperCAmelCase					      =  config.num_attention_heads
   UpperCAmelCase					      =  self.prepare_inputs_dict(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )
   return config, input_dict


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Any:
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.prepare_config_and_inputs()
   return config, inputs_dict


 def _UpperCamelCase	(						self		: Dict    )    ->      List[str]:
   return TaConfig(
       vocab_size=1_6_6			,						d_model=self.hidden_size			,						d_ff=self.d_ff			,						d_kv=self.hidden_size // self.num_attention_heads			,						num_layers=self.num_hidden_layers			,						num_decoder_layers=self.decoder_layers			,						num_heads=self.num_attention_heads			,						relative_attention_num_buckets=self.relative_attention_num_buckets			,						dropout_rate=self.dropout_rate			,						initializer_factor=self.initializer_factor			,						eos_token_id=self.eos_token_id			,						bos_token_id=self.pad_token_id			,						pad_token_id=self.pad_token_id			,						decoder_start_token_id=self.decoder_start_token_id			,						)


 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      List[str]:
   return TaConfig(
       vocab_size=self.vocab_size			,						d_model=self.hidden_size			,						d_ff=self.d_ff			,						d_kv=self.hidden_size // self.num_attention_heads			,						num_layers=self.num_hidden_layers			,						num_decoder_layers=self.decoder_layers			,						num_heads=self.num_attention_heads			,						relative_attention_num_buckets=self.relative_attention_num_buckets			,						dropout_rate=self.dropout_rate			,						initializer_factor=self.initializer_factor			,						eos_token_id=self.eos_token_id			,						bos_token_id=self.pad_token_id			,						pad_token_id=self.pad_token_id			,						decoder_start_token_id=self.decoder_start_token_id			,						)


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: int			,						)    ->      List[str]:
   UpperCAmelCase					      =  UMTaModel(config=lowerCAmelCase__    )
   model.to(lowerCAmelCase__    )
   model.eval()
   UpperCAmelCase					      =  model(
       input_ids=lowerCAmelCase__			,						decoder_input_ids=lowerCAmelCase__			,						attention_mask=lowerCAmelCase__			,						decoder_attention_mask=lowerCAmelCase__			,						)
   UpperCAmelCase					      =  model(input_ids=lowerCAmelCase__			,						decoder_input_ids=lowerCAmelCase__    )
   UpperCAmelCase					      =  result.last_hidden_state
   UpperCAmelCase					      =  result.past_key_values
   UpperCAmelCase					      =  result.encoder_last_hidden_state

   self.parent.assertEqual(encoder_output.size()			,						(self.batch_size, self.encoder_seq_length, self.hidden_size)    )
   self.parent.assertEqual(decoder_output.size()			,						(self.batch_size, self.decoder_seq_length, self.hidden_size)    )
   # There should be `num_layers` key value embeddings stored in decoder_past
   self.parent.assertEqual(len(lowerCAmelCase__    )			,						config.num_layers    )
   # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
   self.parent.assertEqual(len(decoder_past[0]    )			,						4    )


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: Union[str, Any]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: Optional[Any]			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: Any			,						)    ->      Optional[Any]:
   UpperCAmelCase					      =  UMTaModel(config=lowerCAmelCase__    ).get_decoder().to(lowerCAmelCase__    ).eval()
   # first forward pass
   UpperCAmelCase					      =  model(lowerCAmelCase__			,						use_cache=lowerCAmelCase__    )
   UpperCAmelCase					      =  model(lowerCAmelCase__    )
   UpperCAmelCase					      =  model(lowerCAmelCase__			,						use_cache=lowerCAmelCase__    )

   self.parent.assertTrue(len(lowerCAmelCase__    ) == len(lowerCAmelCase__    )    )
   self.parent.assertTrue(len(lowerCAmelCase__    ) == len(lowerCAmelCase__    ) + 1    )

   UpperCAmelCase      ,	UpperCAmelCase					      =  outputs.to_tuple()

   # create hypothetical next token and extent to next_input_ids
   UpperCAmelCase					      =  ids_tensor((self.batch_size, 1)			,						config.vocab_size    )

   # append to next input_ids and
   UpperCAmelCase					      =  torch.cat([input_ids, next_tokens]			,						dim=-1    )

   UpperCAmelCase					      =  model(lowerCAmelCase__    )["last_hidden_state"]
   UpperCAmelCase					      =  model(lowerCAmelCase__			,						past_key_values=lowerCAmelCase__    )["last_hidden_state"]

   # select random slice
   UpperCAmelCase					      =  ids_tensor((1,)			,						output_from_past.shape[-1]    ).item()
   UpperCAmelCase					      =  output_from_no_past[:, -1, random_slice_idx].detach()
   UpperCAmelCase					      =  output_from_past[:, 0, random_slice_idx].detach()

   # test that outputs are equal for slice
   self.parent.assertTrue(torch.allclose(lowerCAmelCase__			,						lowerCAmelCase__			,						atol=1e-3    )    )


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: List[str]			,						)    ->      Optional[int]:
   UpperCAmelCase					      =  UMTaModel(config=lowerCAmelCase__    ).to(lowerCAmelCase__    ).half().eval()
   UpperCAmelCase					      =  model(**lowerCAmelCase__    )["last_hidden_state"]
   self.parent.assertFalse(torch.isnan(lowerCAmelCase__    ).any().item()    )






@require_torch
class 						__magic_name__    (       _snake_case	,						_snake_case	,						_snake_case	,						unittest.TestCase    ):
 UpperCAmelCase							    =						(
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 UpperCAmelCase							    =						(UMTaForConditionalGeneration,) if is_torch_available() else ()
 UpperCAmelCase							    =						(
     {
         """conversational""": UMTaForConditionalGeneration,
         """feature-extraction""": UMTaModel,
         """summarization""": UMTaForConditionalGeneration,
         """text2text-generation""": UMTaForConditionalGeneration,
         """translation""": UMTaForConditionalGeneration,
         """question-answering""": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						False
 UpperCAmelCase							    =						True
 UpperCAmelCase							    =						True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 UpperCAmelCase							    =						[0.8, 0.9]


 def _UpperCamelCase	(						self		: Union[str, Any]    )    ->      Any:
   UpperCAmelCase					      =  UMTaModelTester(self    )


 @unittest.skip("Test has a segmentation fault on torch 1.8.0"    )
 def _UpperCamelCase	(						self		: Tuple    )    ->      List[str]:
   UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()
   UpperCAmelCase					      =  UMTaModel(config_and_inputs[0]    ).to(lowerCAmelCase__    )
   with tempfile.TemporaryDirectory() as tmpdirname:
     torch.onnx.export(
         lowerCAmelCase__			,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,						f"{tmpdirname}/t5_test.onnx"			,						export_params=lowerCAmelCase__			,						opset_version=9			,						input_names=["input_ids", "decoder_input_ids"]			,						)


 @unittest.skipIf(torch_device == "cpu"			,						"Cant do half precision"    )
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      int:
   UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: int    )    ->      Optional[Any]:
   UpperCAmelCase					      =  ["encoder_attentions", "decoder_attentions", "cross_attentions"]
   UpperCAmelCase					      =  self.model_tester.prepare_config_and_inputs()
   UpperCAmelCase					      =  config_and_inputs[0]
   UpperCAmelCase					      =  UMTaForConditionalGeneration(lowerCAmelCase__    ).eval()
   model.to(lowerCAmelCase__    )

   UpperCAmelCase					      =  {
       "head_mask": torch.zeros(config.num_layers			,						config.num_heads			,						device=lowerCAmelCase__    ),
       "decoder_head_mask": torch.zeros(config.num_decoder_layers			,						config.num_heads			,						device=lowerCAmelCase__    ),
       "cross_attn_head_mask": torch.zeros(config.num_decoder_layers			,						config.num_heads			,						device=lowerCAmelCase__    ),
   }

   for attn_name, (name, mask) in zip(lowerCAmelCase__			,						head_masking.items()    ):
     UpperCAmelCase					      =  {name: mask}
     # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
     if name == "head_mask":
       UpperCAmelCase					      =  torch.ones(
           config.num_decoder_layers			,						config.num_heads			,						device=lowerCAmelCase__    )

     UpperCAmelCase					      =  model.generate(
         config_and_inputs[1]["input_ids"]			,						num_beams=1			,						max_length=3			,						output_attentions=lowerCAmelCase__			,						return_dict_in_generate=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
     # We check the state of decoder_attentions and cross_attentions just from the last step
     UpperCAmelCase					      =  out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
     self.assertEqual(sum([w.sum().item() for w in attn_weights]    )			,						0.0    )


 @unittest.skip("Does not work on the tiny model as we keep hitting edge cases."    )
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Optional[int]:
   pass






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


 @slow
 @unittest.skip(
     "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"    )
 def _UpperCamelCase	(						self		: Tuple    )    ->      str:
   UpperCAmelCase					      =  UMTaForConditionalGeneration.from_pretrained("google/umt5-small"			,						return_dict=lowerCAmelCase__    ).to(lowerCAmelCase__    )
   UpperCAmelCase					      =  AutoTokenizer.from_pretrained("google/umt5-small"			,						use_fast=lowerCAmelCase__			,						legacy=lowerCAmelCase__    )
   UpperCAmelCase					      =  [
       "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
       "No se como puedo <extra_id_0>.",
       "This is the reason why we <extra_id_0> them.",
       "The <extra_id_0> walks in <extra_id_1>, seats",
       "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
   ]
   UpperCAmelCase					      =  tokenizer(lowerCAmelCase__			,						return_tensors="pt"			,						padding=lowerCAmelCase__    ).input_ids
   # fmt: off
   UpperCAmelCase					      =  torch.tensor(
       [
           [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
           [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
           [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
       ]    )
   # fmt: on
   torch.testing.assert_allclose(lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  model.generate(input_ids.to(lowerCAmelCase__    )    )
   UpperCAmelCase					      =  [
       "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
       "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
       "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
       "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
       "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
   ]
   UpperCAmelCase					      =  tokenizer.batch_decode(lowerCAmelCase__    )
   self.assertEqual(lowerCAmelCase__			,						lowerCAmelCase__    )






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(	__A			):
  UpperCAmelCase					      =  fname.split(os.path.sep			)[-1]
  return re.search(r"^(.*)_\d+\.jpg$"   ,       __A			).groups()[0]






class 						__magic_name__    (       _snake_case    ):


 def __init__(						self		: Any			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Union[str, Any]=None			,						lowerCAmelCase__		: int=None    )    ->      Optional[Any]:
   UpperCAmelCase					      =  file_names
   UpperCAmelCase					      =  image_transform
   UpperCAmelCase					      =  label_to_id


 def __len__(						self		: Tuple    )    ->      List[str]:
   return len(self.file_names    )


 def __getitem__(						self		: Optional[int]			,						lowerCAmelCase__		: Tuple    )    ->      Dict:
   UpperCAmelCase					      =  self.file_names[idx]
   UpperCAmelCase					      =  PIL.Image.open(lowerCAmelCase__    )
   UpperCAmelCase					      =  raw_image.convert("RGB"    )
   if self.image_transform is not None:
     UpperCAmelCase					      =  self.image_transform(lowerCAmelCase__    )
   UpperCAmelCase					      =  extract_label(lowerCAmelCase__    )
   if self.label_to_id is not None:
     UpperCAmelCase					      =  self.label_to_id[label]
   return {"image": image, "label": label}

def      _lowerCAmelCase(	__A   ,       __A			):
  # Initialize accelerator
  if args.with_tracking:
    UpperCAmelCase					      =  Accelerator(
        cpu=args.cpu   ,       mixed_precision=args.mixed_precision   ,       log_with="all"   ,       project_dir=args.project_dir			)
  else:
    UpperCAmelCase					      =  Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision			)

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  UpperCAmelCase					      =  config["lr"]
  UpperCAmelCase					      =  int(config["num_epochs"]			)
  UpperCAmelCase					      =  int(config["seed"]			)
  UpperCAmelCase					      =  int(config["batch_size"]			)
  UpperCAmelCase					      =  config["image_size"]
  if not isinstance(__A   ,       (list, tuple)			):
    UpperCAmelCase					      =  (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					      =  args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      UpperCAmelCase					      =  int(args.checkpointing_steps			)
    else:
      raise ValueError(
          F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."			)
  else:
    UpperCAmelCase					      =  None

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    UpperCAmelCase					      =  os.path.split(__A			)[-1].split("."			)[0]
    accelerator.init_trackers(__A   ,       __A			)

  # Grab all the image filenames
  UpperCAmelCase					      =  [os.path.join(args.data_dir   ,       __A			) for fname in os.listdir(args.data_dir			) if fname.endswith(".jpg"			)]

  # Build the label correspondences
  UpperCAmelCase					      =  [extract_label(__A			) for fname in file_names]
  UpperCAmelCase					      =  list(set(__A			)			)
  id_to_label.sort()
  UpperCAmelCase					      =  {lbl: i for i, lbl in enumerate(__A			)}

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

  # Split our filenames between train and validation
  UpperCAmelCase					      =  np.random.permutation(len(__A			)			)
  UpperCAmelCase					      =  int(0.8 * len(__A			)			)
  UpperCAmelCase					      =  random_perm[:cut]
  UpperCAmelCase					      =  random_perm[cut:]

  # For training we use a simple RandomResizedCrop
  UpperCAmelCase					      =  Compose([RandomResizedCrop(__A   ,       scale=(0.5, 1.0)			), ToTensor()]			)
  UpperCAmelCase					      =  PetsDataset(
      [file_names[i] for i in train_split]   ,       image_transform=__A   ,       label_to_id=__A			)

  # For evaluation, we use a deterministic Resize
  UpperCAmelCase					      =  Compose([Resize(__A			), ToTensor()]			)
  UpperCAmelCase					      =  PetsDataset([file_names[i] for i in eval_split]   ,       image_transform=__A   ,       label_to_id=__A			)

  # Instantiate dataloaders.
  UpperCAmelCase					      =  DataLoader(__A   ,       shuffle=__A   ,       batch_size=__A   ,       num_workers=4			)
  UpperCAmelCase					      =  DataLoader(__A   ,       shuffle=__A   ,       batch_size=__A   ,       num_workers=4			)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase					      =  create_model("resnet50d"   ,       pretrained=__A   ,       num_classes=len(__A			)			)

  # 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					      =  model.to(accelerator.device			)

  # Freezing the base model
  for param in model.parameters():
    UpperCAmelCase					      =  False
  for param in model.get_classifier().parameters():
    UpperCAmelCase					      =  True

  # We normalize the batches of images to be a bit faster.
  UpperCAmelCase					      =  torch.tensor(model.default_cfg["mean"]			)[None, :, None, None].to(accelerator.device			)
  UpperCAmelCase					      =  torch.tensor(model.default_cfg["std"]			)[None, :, None, None].to(accelerator.device			)

  # Instantiate optimizer
  UpperCAmelCase					      =  torch.optim.Adam(params=model.parameters()   ,       lr=lr / 25			)

  # Instantiate learning rate scheduler
  UpperCAmelCase					      =  OneCycleLR(optimizer=__A   ,       max_lr=__A   ,       epochs=__A   ,       steps_per_epoch=len(__A			)			)

  # 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					      =  accelerator.prepare(
      __A   ,       __A   ,       __A   ,       __A   ,       __A			)
  # We need to keep track of how many total steps we have iterated over
  UpperCAmelCase					      =  0
  # We also need to keep track of the starting epoch so files are named properly
  UpperCAmelCase					      =  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					      =  os.path.basename(args.resume_from_checkpoint			)
    else:
      # Get the most recent checkpoint
      UpperCAmelCase					      =  [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
      dirs.sort(key=os.path.getctime			)
      UpperCAmelCase					      =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    UpperCAmelCase					      =  os.path.splitext(__A			)[0]

    if "epoch" in training_difference:
      UpperCAmelCase					      =  int(training_difference.replace("epoch_"   ,       ""			)			) + 1
      UpperCAmelCase					      =  None
    else:
      UpperCAmelCase					      =  int(training_difference.replace("step_"   ,       ""			)			)
      UpperCAmelCase					      =  resume_step // len(__A			)
      resume_step -= starting_epoch * len(__A			)

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

    UpperCAmelCase					      =  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(__A			),
              "epoch": epoch,
          }   ,       step=__A   ,       )
    if checkpointing_steps == "epoch":
      UpperCAmelCase					      =  F"epoch_{epoch}"
      if args.output_dir is not None:
        UpperCAmelCase					      =  os.path.join(args.output_dir   ,       __A			)
      accelerator.save_state(__A			)

  if args.with_tracking:
    accelerator.end_training()
def      _lowerCAmelCase(	):
  UpperCAmelCase					      =  argparse.ArgumentParser(description="Simple example of training script."			)
  parser.add_argument("--data_dir"   ,       required=__A   ,       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=__A   ,       default=__A   ,       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=__A   ,       default=__A   ,       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=__A   ,       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=__A   ,       default=__A   ,       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=__A   ,       default="logs"   ,       help="Location on where to store experiment tracking logs` and relevent project information"   ,       )
  UpperCAmelCase					      =  parser.parse_args()
  UpperCAmelCase					      =  {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
  training_function(__A   ,       __A			)


if __name__ == "__main__":
      main()



