

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						_lowerCAmelCase      :    int						       =    "platform"

						import jax
						import jax.numpy as jnp

						from transformers import AutoTokenizer
						from transformers.models.mbart.modeling_flax_mbart import (
						    FlaxMBartForConditionalGeneration,
						    FlaxMBartForQuestionAnswering,
						    FlaxMBartForSequenceClassification,
						    FlaxMBartModel,
						    shift_tokens_right,
						)






def  UpperCamelCase_(							_snake_case     :						str ,  _snake_case     :						Union[str, Any] ,  _snake_case     :						List[str]=None ,  _snake_case     :						Optional[int]=None ,  _snake_case     :						str=None ,  _snake_case     :						List[str]=None ,  _snake_case     :						Union[str, Any]=None ,  _snake_case     :						Union[str, Any]=None ,  ):




			"""simple docstring"""






			if attention_mask is None:
						__a						   =np.where(input_ids != config.pad_token_id ,  1 ,  0			)
			if decoder_attention_mask is None:
						__a						   =np.where(decoder_input_ids != config.pad_token_id ,  1 ,  0			)
			if head_mask is None:
						__a						   =np.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						__a						   =np.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						__a						   =np.ones((config.decoder_layers, config.decoder_attention_heads)			)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}






class 			__magic_name__					(    unittest.TestCase   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case=13      ,						__snake_case=7      ,						__snake_case=True      ,						__snake_case=False      ,						__snake_case=99      ,						__snake_case=16      ,						__snake_case=2      ,						__snake_case=4      ,						__snake_case=4      ,						__snake_case="gelu"      ,						__snake_case=0.1      ,						__snake_case=0.1      ,						__snake_case=32      ,						__snake_case=2      ,						__snake_case=1      ,						__snake_case=0      ,						__snake_case=2      ,						__snake_case=0.02      ,						)     ->				List[str]:




						'''simple docstring'''
						__a						   =parent
						__a						   =batch_size
						__a						   =seq_length
						__a						   =is_training
						__a						   =use_labels
						__a						   =vocab_size
						__a						   =hidden_size
						__a						   =num_hidden_layers
						__a						   =num_attention_heads
						__a						   =intermediate_size
						__a						   =hidden_act
						__a						   =hidden_dropout_prob
						__a						   =attention_probs_dropout_prob
						__a						   =max_position_embeddings
						__a						   =eos_token_id
						__a						   =pad_token_id
						__a						   =bos_token_id
						__a						   =decoder_start_token_id
						__a						   =initializer_range





			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						__a						   =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,						self.vocab_size      )      ,						3      ,						self.vocab_size      )
						__a						   =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,						dtype=np.intaa      ))      ,						-1      )

						__a						   =shift_tokens_right(__snake_case      ,						1      )

						__a						   =MBartConfig(
						    vocab_size=self.vocab_size      ,						d_model=self.hidden_size      ,						encoder_layers=self.num_hidden_layers      ,						decoder_layers=self.num_hidden_layers      ,						encoder_attention_heads=self.num_attention_heads      ,						decoder_attention_heads=self.num_attention_heads      ,						encoder_ffn_dim=self.intermediate_size      ,						decoder_ffn_dim=self.intermediate_size      ,						dropout=self.hidden_dropout_prob      ,						attention_dropout=self.attention_probs_dropout_prob      ,						max_position_embeddings=self.max_position_embeddings      ,						eos_token_id=self.eos_token_id      ,						bos_token_id=self.bos_token_id      ,						pad_token_id=self.pad_token_id      ,						decoder_start_token_id=self.decoder_start_token_id      ,						initializer_range=self.initializer_range      ,						use_cache=__snake_case      ,						)
						__a						   =prepare_mbart_inputs_dict(__snake_case      ,						__snake_case      ,						__snake_case      )
						return config, inputs_dict





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a   ,					__a						   =self.prepare_config_and_inputs()
						return config, inputs_dict





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Optional[int]:




						'''simple docstring'''
						__a						   =20
						__a						   =model_class_name(__snake_case      )

						__a						   =model.encode(inputs_dict['input_ids']      )

						__a   ,					__a						   =(
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

						__a						   =model.init_cache(decoder_input_ids.shape[0]      ,						__snake_case      ,						__snake_case      )
						__a						   =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,						dtype='i4'      )

						__a						   =jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]      ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,						)
						__a						   =model.decode(
						    decoder_input_ids[:, :-1]      ,						__snake_case      ,						decoder_attention_mask=__snake_case      ,						past_key_values=__snake_case      ,						decoder_position_ids=__snake_case      ,						)

						__a						   =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,						dtype='i4'      )
						__a						   =model.decode(
						    decoder_input_ids[:, -1:]      ,						__snake_case      ,						decoder_attention_mask=__snake_case      ,						past_key_values=outputs_cache.past_key_values      ,						decoder_position_ids=__snake_case      ,						)

						__a						   =model.decode(__snake_case      ,						__snake_case      )

						__a						   =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
						self.parent.assertTrue(diff < 1e-3      ,						msg=f'Max diff is {diff}'      )





			def 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				str:




						'''simple docstring'''
						__a						   =20
						__a						   =model_class_name(__snake_case      )

						__a						   =model.encode(inputs_dict['input_ids']      )

						__a   ,					__a						   =(
						    inputs_dict['decoder_input_ids'],
						    inputs_dict['decoder_attention_mask'],
						)

						__a						   =jnp.concatenate(
						    [
						        decoder_attention_mask,
						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
						    ]      ,						axis=-1      ,						)

						__a						   =model.init_cache(decoder_input_ids.shape[0]      ,						__snake_case      ,						__snake_case      )
						__a						   =jnp.broadcast_to(
						    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]      ,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,						)

						__a						   =model.decode(
						    decoder_input_ids[:, :-1]      ,						__snake_case      ,						decoder_attention_mask=__snake_case      ,						past_key_values=__snake_case      ,						decoder_position_ids=__snake_case      ,						)
						__a						   =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,						dtype='i4'      )
						__a						   =model.decode(
						    decoder_input_ids[:, -1:]      ,						__snake_case      ,						past_key_values=outputs_cache.past_key_values      ,						decoder_attention_mask=__snake_case      ,						decoder_position_ids=__snake_case      ,						)

						__a						   =model.decode(__snake_case      ,						__snake_case      ,						decoder_attention_mask=__snake_case      )

						__a						   =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
						self.parent.assertTrue(diff < 1e-3      ,						msg=f'Max diff is {diff}'      )






@require_flax
class 			__magic_name__					(    unittest.TestCase   ):
			SCREAMING_SNAKE_CASE							     =		9_9





			def 	__magic_name__					(	self      )     ->				str:




						'''simple docstring'''
						__a						   =np.array(
						    [
						        [71, 82, 18, 33, 46, 91, 2],
						        [68, 34, 26, 58, 30, 82, 2],
						        [5, 97, 17, 39, 94, 40, 2],
						        [76, 83, 94, 25, 70, 78, 2],
						        [87, 59, 41, 35, 48, 66, 2],
						        [55, 13, 16, 58, 5, 2, 1],  # note padding
						        [64, 27, 31, 51, 12, 75, 2],
						        [52, 64, 86, 17, 83, 39, 2],
						        [48, 61, 9, 24, 71, 82, 2],
						        [26, 1, 60, 48, 22, 13, 2],
						        [21, 5, 62, 28, 14, 76, 2],
						        [45, 98, 37, 86, 59, 48, 2],
						        [70, 70, 50, 9, 28, 0, 2],
						    ]      ,						dtype=np.intaa      ,						)

						__a						   =input_ids.shape[0]
						__a						   =MBartConfig(
						    vocab_size=self.vocab_size      ,						d_model=24      ,						encoder_layers=2      ,						decoder_layers=2      ,						encoder_attention_heads=2      ,						decoder_attention_heads=2      ,						encoder_ffn_dim=32      ,						decoder_ffn_dim=32      ,						max_position_embeddings=48      ,						eos_token_id=2      ,						pad_token_id=1      ,						bos_token_id=0      ,						)
						return config, input_ids, batch_size





			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						__a   ,					__a   ,					__a						   =self._get_config_and_data()
						__a						   =FlaxMBartForSequenceClassification(__snake_case      )
						__a						   =model(input_ids=__snake_case      ,						decoder_input_ids=__snake_case      )
						__a						   =(batch_size, config.num_labels)
						self.assertEqual(outputs['logits'].shape      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a   ,					__a   ,					__a						   =self._get_config_and_data()
						__a						   =FlaxMBartForQuestionAnswering(__snake_case      )
						__a						   =model(input_ids=__snake_case      )

						self.assertEqual(outputs['start_logits'].shape      ,						input_ids.shape      )
						self.assertEqual(outputs['end_logits'].shape      ,						input_ids.shape      )





			def 	__magic_name__					(	self      )     ->				List[Any]:




						'''simple docstring'''
						__a   ,					__a   ,					__a						   =self._get_config_and_data()
						__a						   =FlaxMBartForConditionalGeneration(__snake_case      )
						__a						   =lm_model(input_ids=__snake_case      )
						__a						   =(batch_size, input_ids.shape[1], config.vocab_size)
						self.assertEqual(outputs['logits'].shape      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a						   =MBartConfig(
						    vocab_size=self.vocab_size      ,						d_model=14      ,						encoder_layers=2      ,						decoder_layers=2      ,						encoder_attention_heads=2      ,						decoder_attention_heads=2      ,						encoder_ffn_dim=8      ,						decoder_ffn_dim=8      ,						max_position_embeddings=48      ,						)
						__a						   =FlaxMBartForConditionalGeneration(__snake_case      )
						__a						   =np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,						dtype=np.intaa      )
						__a						   =np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,						dtype=np.intaa      )
						__a						   =lm_model(input_ids=__snake_case      ,						decoder_input_ids=__snake_case      )
						__a						   =(*summary.shape, config.vocab_size)
						self.assertEqual(outputs['logits'].shape      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				Any:




						'''simple docstring'''
						__a						   =np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,						dtype=np.intaa      )
						__a						   =shift_tokens_right(__snake_case      ,						1      )
						__a						   =np.equal(__snake_case      ,						1      ).astype(np.floataa      ).sum()
						__a						   =np.equal(__snake_case      ,						1      ).astype(np.floataa      ).sum()
						self.assertEqual(shifted.shape      ,						input_ids.shape      )
						self.assertEqual(__snake_case      ,						n_pad_before - 1      )
						self.assertTrue(np.equal(shifted[:, 0]      ,						2      ).all()      )






@require_flax
class 			__magic_name__					(    lowerCAmelCase_						,					unittest.TestCase						,					lowerCAmelCase_   ):
			SCREAMING_SNAKE_CASE							     =		True
			SCREAMING_SNAKE_CASE							     =		(
			    (
			        FlaxMBartModel,
			        FlaxMBartForConditionalGeneration,
			        FlaxMBartForSequenceClassification,
			        FlaxMBartForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE							     =		(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						__a						   =FlaxMBartModelTester(self      )





			def 	__magic_name__					(	self      )     ->				int:




						'''simple docstring'''
						__a   ,					__a						   =self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__snake_case      ,						__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				Dict:




						'''simple docstring'''
						__a   ,					__a						   =self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(__snake_case      ,						__snake_case      ,						__snake_case      )





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a   ,					__a						   =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__      ):
												__a						   =self._prepare_for_class(__snake_case      ,						__snake_case      )
												__a						   =model_class(__snake_case      )

												@jax.jit
												def encode_jitted(__snake_case      ,						__snake_case=None      ,						**__snake_case      ):
															return model.encode(input_ids=__snake_case      ,						attention_mask=__snake_case      )

												with self.subTest('JIT Enabled'      ):
															__a						   =encode_jitted(**__snake_case      ).to_tuple()

												with self.subTest('JIT Disabled'      ):
															with jax.disable_jit():
																		__a						   =encode_jitted(**__snake_case      ).to_tuple()

												self.assertEqual(len(__snake_case      )      ,						len(__snake_case      )      )
												for jitted_output, output in zip(__snake_case      ,						__snake_case      ):
															self.assertEqual(jitted_output.shape      ,						output.shape      )





			def 	__magic_name__					(	self      )     ->				Tuple:




						'''simple docstring'''
						__a   ,					__a						   =self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__      ):
												__a						   =model_class(__snake_case      )
												__a						   =model.encode(inputs_dict['input_ids']      ,						inputs_dict['attention_mask']      )

												__a						   ={
												    'decoder_input_ids': inputs_dict['decoder_input_ids'],
												    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
												    'encoder_outputs': encoder_outputs,
												}

												@jax.jit
												def decode_jitted(__snake_case      ,						__snake_case      ,						__snake_case      ):
															return model.decode(
															    decoder_input_ids=__snake_case      ,						decoder_attention_mask=__snake_case      ,						encoder_outputs=__snake_case      ,						)

												with self.subTest('JIT Enabled'      ):
															__a						   =decode_jitted(**__snake_case      ).to_tuple()

												with self.subTest('JIT Disabled'      ):
															with jax.disable_jit():
																		__a						   =decode_jitted(**__snake_case      ).to_tuple()

												self.assertEqual(len(__snake_case      )      ,						len(__snake_case      )      )
												for jitted_output, output in zip(__snake_case      ,						__snake_case      ):
															self.assertEqual(jitted_output.shape      ,						output.shape      )





			@slow
			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						for model_class_name in self.all_model_classes:
									__a						   =model_class_name.from_pretrained('facebook/mbart-large-cc25'      ,						from_pt=__snake_case      )
									# FlaxMBartForSequenceClassification expects eos token in input_ids
									__a						   =np.ones((1, 1)      ) * model.config.eos_token_id
									__a						   =model(__snake_case      )
									self.assertIsNotNone(__snake_case      )






@require_flax
@require_sentencepiece
@require_tokenizers
class 			__magic_name__					(    unittest.TestCase   ):
			SCREAMING_SNAKE_CASE							     =		[
			    ' UN Chief Says There Is No Military Solution in Syria',
			]
			SCREAMING_SNAKE_CASE							     =		[
			    'Şeful ONU declară că nu există o soluţie militară în Siria',
			]
			SCREAMING_SNAKE_CASE							     =		'facebook/mbart-large-en-ro'





			@cached_property
			def 	__magic_name__					(	self      )     ->				Optional[Any]:




						'''simple docstring'''
						return AutoTokenizer.from_pretrained(self.model_name      )





			@cached_property
			def 	__magic_name__					(	self      )     ->				List[str]:




						'''simple docstring'''
						__a						   =FlaxMBartForConditionalGeneration.from_pretrained(self.model_name      ,						from_pt=__snake_case      )
						return model





			def 	__magic_name__					(	self      ,						**__snake_case      )     ->				List[Any]:




						'''simple docstring'''
						__a						   =self.translate_src_text(**__snake_case      )
						self.assertListEqual(self.expected_text      ,						__snake_case      )





			def 	__magic_name__					(	self      ,						**__snake_case      )     ->				int:




						'''simple docstring'''
						__a						   =self.tokenizer(self.src_text      ,						**__snake_case      ,						return_tensors='np'      )
						__a						   =self.model.generate(
						    model_inputs.input_ids      ,						attention_mask=model_inputs.attention_mask      ,						decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']      ,						early_stopping=__snake_case      ,						num_beams=2      ,						).sequences
						__a						   =self.tokenizer.batch_decode(__snake_case      ,						skip_special_tokens=__snake_case      )
						return generated_words





			@slow
			def 	__magic_name__					(	self      )     ->				Optional[int]:




						'''simple docstring'''
						self._assert_generated_batch_equal_expected()









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  UpperCamelCase_(							_snake_case     :						Union[str, Any]			):




			"""simple docstring"""






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






class 			__magic_name__					(    lowerCAmelCase_   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case=None      ,						__snake_case=None      )     ->				Tuple:




						'''simple docstring'''
						__a						   =file_names
						__a						   =image_transform
						__a						   =label_to_id





			def __len__(	self      )     ->				int:




						'''simple docstring'''
						return len(self.file_names      )





			def __getitem__(	self      ,						__snake_case      )     ->				List[str]:




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






def  UpperCamelCase_(							_snake_case     :						Union[str, Any] ,  _snake_case     :						Optional[int]			):




			"""simple docstring"""






			__a						   =Accelerator(cpu=args.cpu ,  mixed_precision=args.mixed_precision			)

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

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

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

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

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

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

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

			# Instantiate dataloaders.
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)
			__a						   =DataLoader(_snake_case ,  shuffle=_snake_case ,  batch_size=_snake_case ,  num_workers=4			)

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

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

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

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

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

			# Instantiate learning rate scheduler
			__a						   =OneCycleLR(optimizer=_snake_case ,  max_lr=_snake_case ,  epochs=_snake_case ,  steps_per_epoch=len(_snake_case			)			)

			# 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.
			__a   ,					__a   ,					__a   ,					__a   ,					__a						   =accelerator.prepare(
			    _snake_case ,  _snake_case ,  _snake_case ,  _snake_case ,  _snake_case			)

			# Now we train the model
			for epoch in range(_snake_case			):
						model.train()
						for step, batch in enumerate(_snake_case			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									__a						   ={k: v.to(accelerator.device			) for k, v in batch.items()}
									__a						   =(batch['image'] - mean) / std
									__a						   =model(_snake_case			)
									__a						   =torch.nn.functional.cross_entropy(_snake_case ,  batch['label']			)
									accelerator.backward(_snake_case			)
									optimizer.step()
									lr_scheduler.step()
									optimizer.zero_grad()

						model.eval()
						__a						   =0
						__a						   =0
						for _, batch in enumerate(_snake_case			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									__a						   ={k: v.to(accelerator.device			) for k, v in batch.items()}
									__a						   =(batch['image'] - mean) / std
									with torch.no_grad():
												__a						   =model(_snake_case			)
									__a						   =outputs.argmax(dim=-1			)
									__a   ,					__a						   =accelerator.gather_for_metrics((predictions, batch['label'])			)
									__a						   =predictions == references
									num_elems += accurate_preds.shape[0]
									accurate += accurate_preds.long().sum()

						__a						   =accurate.item() / num_elems
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}: {100 * eval_metric:.2f}'			)






def  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   =argparse.ArgumentParser(description='Simple example of training script.'			)
			parser.add_argument('--data_dir' ,  required=_snake_case ,  help='The data folder on disk.'			)
			parser.add_argument(
			    '--mixed_precision' ,  type=_snake_case ,  default=_snake_case ,  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(
			    '--checkpointing_steps' ,  type=_snake_case ,  default=_snake_case ,  help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.' ,  )
			parser.add_argument('--cpu' ,  action='store_true' ,  help='If passed, will train on the CPU.'			)
			__a						   =parser.parse_args()
			__a						   ={'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
			training_function(_snake_case ,  _snake_case			)


if __name__ == "__main__":
						main()






