



import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
     import torch

     from transformers import (
         BioGptForCausalLM,
         BioGptForSequenceClassification,
         BioGptForTokenClassification,
         BioGptModel,
         BioGptTokenizer,
     )
     from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a					,      __a=13					,      __a=7					,      __a=True					,      __a=True					,      __a=False					,      __a=True					,      __a=99					,      __a=32					,      __a=5					,      __a=4					,      __a=37					,      __a="gelu"					,      __a=0.1					,      __a=0.1					,      __a=5_12					,      __a=16					,      __a=2					,      __a=0.02					,      __a=3					,      __a=4					,      __a=None					,      ):






           '''simple docstring'''



           lowerCamelCase             =		parent
           lowerCamelCase             =		batch_size
           lowerCamelCase             =		seq_length
           lowerCamelCase             =		is_training
           lowerCamelCase             =		use_input_mask
           lowerCamelCase             =		use_token_type_ids
           lowerCamelCase             =		use_labels
           lowerCamelCase             =		vocab_size
           lowerCamelCase             =		hidden_size
           lowerCamelCase             =		num_hidden_layers
           lowerCamelCase             =		num_attention_heads
           lowerCamelCase             =		intermediate_size
           lowerCamelCase             =		hidden_act
           lowerCamelCase             =		hidden_dropout_prob
           lowerCamelCase             =		attention_probs_dropout_prob
           lowerCamelCase             =		max_position_embeddings
           lowerCamelCase             =		type_vocab_size
           lowerCamelCase             =		type_sequence_label_size
           lowerCamelCase             =		initializer_range
           lowerCamelCase             =		num_labels
           lowerCamelCase             =		num_choices
           lowerCamelCase             =		scope







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size     )

           lowerCamelCase             =		None
           if self.use_input_mask:
                 lowerCamelCase             =		random_attention_mask([self.batch_size, self.seq_length]     )

           lowerCamelCase             =		None
           if self.use_token_type_ids:
                 lowerCamelCase             =		ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size     )

           lowerCamelCase             =		None
           lowerCamelCase             =		None
           lowerCamelCase             =		None
           if self.use_labels:
                 lowerCamelCase             =		ids_tensor([self.batch_size]					,      self.type_sequence_label_size     )
                 lowerCamelCase             =		ids_tensor([self.batch_size, self.seq_length]					,      self.num_labels     )
                 lowerCamelCase             =		ids_tensor([self.batch_size]					,      self.num_choices     )

           lowerCamelCase             =		self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







     def     _a   (self     ):






           '''simple docstring'''



           return BioGptConfig(
               vocab_size=self.vocab_size					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      type_vocab_size=self.type_vocab_size					,      is_decoder=__a					,      initializer_range=self.initializer_range					,      )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a					,      __a					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptModel(config=__a     )
           model.to(__a     )
           model.eval()
           lowerCamelCase             =		model(__a					,      attention_mask=__a     )
           lowerCamelCase             =		model(__a     )
           self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a					,      __a					,      __a					,      __a					,      __a					,      ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptForCausalLM(config=__a     )
           model.to(__a     )
           model.eval()
           lowerCamelCase             =		model(__a					,      attention_mask=__a					,      token_type_ids=__a					,      labels=__a     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptModel(config=__a     )
           model.to(__a     )
           model.eval()

           # create attention mask
           lowerCamelCase             =		torch.ones(input_ids.shape					,      dtype=torch.long					,      device=__a     )
           lowerCamelCase             =		self.seq_length // 2
           lowerCamelCase             =		0

           # first forward pass
           lowerCamelCase      ,				lowerCamelCase             =		model(__a					,      attention_mask=__a     ).to_tuple()

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

           # change a random masked slice from input_ids
           lowerCamelCase             =		ids_tensor((1,)					,      __a     ).item() + 1
           lowerCamelCase             =		ids_tensor((self.batch_size, 1)					,      config.vocab_size     ).squeeze(-1     )
           lowerCamelCase             =		random_other_next_tokens

           # append to next input_ids and attn_mask
           lowerCamelCase             =		torch.cat([input_ids, next_tokens]					,      dim=-1     )
           lowerCamelCase             =		torch.cat(
               [attn_mask, torch.ones((attn_mask.shape[0], 1)					,      dtype=torch.long					,      device=__a     )]					,      dim=1					,      )

           # get two different outputs
           lowerCamelCase             =		model(__a					,      attention_mask=__a     )["last_hidden_state"]
           lowerCamelCase             =		model(__a					,      past_key_values=__a					,      attention_mask=__a     )["last_hidden_state"]

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

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







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptModel(config=__a     ).to(__a     ).eval()

           lowerCamelCase             =		torch.ones(input_ids.shape					,      dtype=torch.long					,      device=__a     )

           # first forward pass
           lowerCamelCase             =		model(__a					,      attention_mask=__a					,      use_cache=__a     )

           lowerCamelCase      ,				lowerCamelCase             =		outputs.to_tuple()

           # create hypothetical multiple next token and extent to next_input_ids
           lowerCamelCase             =		ids_tensor((self.batch_size, 3)					,      config.vocab_size     )
           lowerCamelCase             =		ids_tensor((self.batch_size, 3)					,      2     )

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

           lowerCamelCase             =		model(__a					,      attention_mask=__a     )["last_hidden_state"]
           lowerCamelCase             =		model(__a					,      attention_mask=__a					,      past_key_values=__a     )[
               "last_hidden_state"
           ]

           # select random slice
           lowerCamelCase             =		ids_tensor((1,)					,      output_from_past.shape[-1]     ).item()
           lowerCamelCase             =		output_from_no_past[:, -3:, random_slice_idx].detach()
           lowerCamelCase             =		output_from_past[:, :, random_slice_idx].detach()

           self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

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







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a					,      *__a					,      __a=False     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptForCausalLM(__a     )
           model.to(__a     )
           if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

           lowerCamelCase             =		model(__a					,      labels=__a     )
           self.parent.assertEqual(result.loss.shape					,      ()     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)     )
           result.loss.backward()







     def     _a   (self					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptModel(__a     )
           lowerCamelCase             =		model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers     )
           for key in model.state_dict().keys():
                 if "c_proj" in key and "weight" in key:
                       self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]     ) - model_std     )					,      0.001     )
                       self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]     ) - 0.0     )					,      0.01     )







     def     _a   (self					,      __a					,      __a					,      __a					,      __a					,      __a					,      *__a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.num_labels
           lowerCamelCase             =		BioGptForTokenClassification(__a     )
           model.to(__a     )
           model.eval()
           lowerCamelCase             =		model(__a					,      attention_mask=__a					,      token_type_ids=__a     )
           self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.prepare_config_and_inputs()
           (
               (
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				(
               lowerCamelCase
           )      ,				
           )             =		config_and_inputs
           lowerCamelCase             =		{"input_ids": input_ids, "attention_mask": input_mask}
           return config, inputs_dict





@require_torch
class  lowerCamelCase__	(    UpperCAmelCase_							,     UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      (
         (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
         if is_torch_available()
         else ()
     )
     _A      =      (BioGptForCausalLM,) if is_torch_available() else ()
     _A      =      (
         {
             'feature-extraction': BioGptModel,
             'text-classification': BioGptForSequenceClassification,
             'text-generation': BioGptForCausalLM,
             'token-classification': BioGptForTokenClassification,
             'zero-shot': BioGptForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _A      =      False







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptModelTester(self     )
           lowerCamelCase             =		ConfigTester(self					,      config_class=__a					,      hidden_size=37     )







     def     _a   (self     ):






           '''simple docstring'''



           self.config_tester.run_common_tests()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowerCamelCase             =		type
                 self.model_tester.create_and_check_model(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_attention_mask_past(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_forward_and_backwards(*__a					,      gradient_checkpointing=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_model_past_large_inputs(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_weight_initialization(*__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_biogpt_for_token_classification(*__a     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptForCausalLM.from_pretrained("microsoft/biogpt"     )
           model.to(__a     )
           lowerCamelCase             =		BioGptTokenizer.from_pretrained("microsoft/biogpt"     )

           lowerCamelCase             =		"left"

           # Define PAD Token = EOS Token = 50256
           lowerCamelCase             =		tokenizer.eos_token
           lowerCamelCase             =		model.config.eos_token_id

           # use different length sentences to test batching
           lowerCamelCase             =		[
               "Hello, my dog is a little",
               "Today, I",
           ]

           lowerCamelCase             =		tokenizer(__a					,      return_tensors="pt"					,      padding=__a     )
           lowerCamelCase             =		inputs["input_ids"].to(__a     )

           lowerCamelCase             =		model.generate(
               input_ids=__a					,      attention_mask=inputs["attention_mask"].to(__a     )					,      )

           lowerCamelCase             =		tokenizer(sentences[0]					,      return_tensors="pt"     ).input_ids.to(__a     )
           lowerCamelCase             =		model.generate(input_ids=__a     )

           lowerCamelCase             =		inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
           lowerCamelCase             =		tokenizer(sentences[1]					,      return_tensors="pt"     ).input_ids.to(__a     )
           lowerCamelCase             =		model.generate(input_ids=__a					,      max_length=model.config.max_length - num_paddings     )

           lowerCamelCase             =		tokenizer.batch_decode(__a					,      skip_special_tokens=__a     )
           lowerCamelCase             =		tokenizer.decode(output_non_padded[0]					,      skip_special_tokens=__a     )
           lowerCamelCase             =		tokenizer.decode(output_padded[0]					,      skip_special_tokens=__a     )

           lowerCamelCase             =		[
               "Hello, my dog is a little bit bigger than a little bit.",
               "Today, I have a good idea of how to use the information",
           ]
           self.assertListEqual(__a					,      __a     )
           self.assertListEqual(__a					,      [non_padded_sentence, padded_sentence]     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase             =		BioGptModel.from_pretrained(__a     )
                 self.assertIsNotNone(__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase             =		3
           lowerCamelCase             =		input_dict["input_ids"]
           lowerCamelCase             =		input_ids.ne(1     ).to(__a     )
           lowerCamelCase             =		ids_tensor([self.model_tester.batch_size]					,      self.model_tester.type_sequence_label_size     )
           lowerCamelCase             =		BioGptForSequenceClassification(__a     )
           model.to(__a     )
           model.eval()
           lowerCamelCase             =		model(__a					,      attention_mask=__a					,      labels=__a     )
           self.assertEqual(result.logits.shape					,      (self.model_tester.batch_size, self.model_tester.num_labels)     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase             =		3
           lowerCamelCase             =		"multi_label_classification"
           lowerCamelCase             =		input_dict["input_ids"]
           lowerCamelCase             =		input_ids.ne(1     ).to(__a     )
           lowerCamelCase             =		ids_tensor(
               [self.model_tester.batch_size, config.num_labels]					,      self.model_tester.type_sequence_label_size     ).to(torch.float     )
           lowerCamelCase             =		BioGptForSequenceClassification(__a     )
           model.to(__a     )
           model.eval()
           lowerCamelCase             =		model(__a					,      attention_mask=__a					,      labels=__a     )
           self.assertEqual(result.logits.shape					,      (self.model_tester.batch_size, self.model_tester.num_labels)     )





@require_torch
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptForCausalLM.from_pretrained("microsoft/biogpt"     )
           lowerCamelCase             =		torch.tensor([[2, 48_05, 9, 6_56, 21]]     )
           lowerCamelCase             =		model(__a     )[0]

           lowerCamelCase             =		4_23_84

           lowerCamelCase             =		torch.Size((1, 5, vocab_size)     )
           self.assertEqual(output.shape					,      __a     )

           lowerCamelCase             =		torch.tensor(
               [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]     )

           self.assertTrue(torch.allclose(output[:, :3, :3]					,      __a					,      atol=1E-4     )     )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		BioGptTokenizer.from_pretrained("microsoft/biogpt"     )
           lowerCamelCase             =		BioGptForCausalLM.from_pretrained("microsoft/biogpt"     )
           model.to(__a     )

           torch.manual_seed(0     )
           lowerCamelCase             =		tokenizer("COVID-19 is"					,      return_tensors="pt"     ).to(__a     )
           lowerCamelCase             =		model.generate(
               **__a					,      min_length=1_00					,      max_length=10_24					,      num_beams=5					,      early_stopping=__a					,      )
           lowerCamelCase             =		tokenizer.decode(output_ids[0]					,      skip_special_tokens=__a     )

           lowerCamelCase             =		(
               "COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
               " causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
               " territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
               " and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
               " more than 800,000 deaths."
           )
           self.assertEqual(__a					,      __a     )




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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



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





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a					,      __a=None					,      __a=None     ):






           '''simple docstring'''



           lowerCamelCase             =		file_names
           lowerCamelCase             =		image_transform
           lowerCamelCase             =		label_to_id







     def __len__(self     ):






           '''simple docstring'''



           return len(self.file_names     )







     def __getitem__(self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.file_names[idx]
           lowerCamelCase             =		PIL.Image.open(__a     )
           lowerCamelCase             =		raw_image.convert("RGB"     )
           if self.image_transform is not None:
                 lowerCamelCase             =		self.image_transform(__a     )
           lowerCamelCase             =		extract_label(__a     )
           if self.label_to_id is not None:
                 lowerCamelCase             =		self.label_to_id[label]
           return {"image": image, "label": label}



def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if args.with_tracking:
            lowerCamelCase             =		Accelerator(
                cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir			)
      else:
            lowerCamelCase             =		Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision			)

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

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

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

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

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

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

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

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

      # Instantiate dataloaders.
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , num_workers=4			)
      lowerCamelCase             =		DataLoader(UpperCAmelCase__ , shuffle=UpperCAmelCase__ , batch_size=UpperCAmelCase__ , num_workers=4			)

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

      # 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).
      lowerCamelCase             =		model.to(accelerator.device			)

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

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

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

      # Instantiate learning rate scheduler
      lowerCamelCase             =		OneCycleLR(optimizer=UpperCAmelCase__ , max_lr=UpperCAmelCase__ , epochs=UpperCAmelCase__ , steps_per_epoch=len(UpperCAmelCase__			)			)

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

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

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

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

      if args.with_tracking:
            accelerator.end_training()

def      __lowercase(						):


      """simple docstring"""



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


if __name__ == "__main__":
     main()