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 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  List[Any]      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Optional[int]=99      ,   __lowerCamelCase       :  Any=13      ,   __lowerCamelCase       :  List[str]=7      ,   __lowerCamelCase       :  Union[str, Any]=9      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   __lowerCamelCase       :  List[Any]=True      ,   __lowerCamelCase       :  Dict=False      ,   __lowerCamelCase       :  List[Any]=32      ,   __lowerCamelCase       :  List[Any]=5      ,   __lowerCamelCase       :  List[Any]=4      ,   __lowerCamelCase       :  Any=37      ,   __lowerCamelCase       :  Any=8      ,   __lowerCamelCase       :  Union[str, Any]=0.1      ,   __lowerCamelCase       :  Optional[Any]=0.002      ,   __lowerCamelCase       :  List[str]=1      ,   __lowerCamelCase       :  Any=0      ,   __lowerCamelCase       :  Union[str, Any]=0      ,   __lowerCamelCase       :  Union[str, Any]=None      ,   __lowerCamelCase       :  List[str]=None      ,   )      ->  Union[str, Any]:
							a					=    parent
							a					=    batch_size
							a					=    encoder_seq_length
							a					=    decoder_seq_length
							# For common tests
							a					=    self.decoder_seq_length
							a					=    is_training
							a					=    use_attention_mask
							a					=    use_labels
							a					=    vocab_size
							a					=    hidden_size
							a					=    num_hidden_layers
							a					=    num_attention_heads
							a					=    d_ff
							a					=    relative_attention_num_buckets
							a					=    dropout_rate
							a					=    initializer_factor
							a					=    eos_token_id
							a					=    pad_token_id
							a					=    decoder_start_token_id
							a					=    None
							a					=    decoder_layers





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Union[str, Any]:
							return TaConfig.from_pretrained("google/umt5-base"							)





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  List[str]=None      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  List[str]=None      ,   __lowerCamelCase       :  Union[str, Any]=None      ,   __lowerCamelCase       :  List[Any]=None      ,   )      ->  Optional[Any]:
							if attention_mask is None:
										a					=    input_ids.ne(config.pad_token_id							)
							if decoder_attention_mask is None:
										a					=    decoder_input_ids.ne(config.pad_token_id							)
							if head_mask is None:
										a					=    torch.ones(config.num_hidden_layers      ,   config.num_attention_heads      ,   device=__lowerCamelCase							)
							if decoder_head_mask is None:
										a					=    torch.ones(config.num_decoder_layers      ,   config.num_attention_heads      ,   device=__lowerCamelCase							)
							if cross_attn_head_mask is None:
										a					=    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       :  Optional[int]							)      ->  List[str]:
							a					=    ids_tensor([self.batch_size, self.encoder_seq_length]      ,   self.vocab_size							)
							a					=    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
							a					=    input_ids.clamp(self.pad_token_id + 1							)
							a					=    decoder_input_ids.clamp(self.pad_token_id + 1							)

							a					=    self.get_config()
							a					=    config.num_attention_heads
							a					=    self.prepare_inputs_dict(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase							)
							return config, input_dict





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Optional[Any]:
							a					,	a					=    self.prepare_config_and_inputs()
							return config, inputs_dict





				def 							__UpperCAmelCase							(   self       :  str							)      ->  int:
							return TaConfig(
							    vocab_size=1_66      ,   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       :  Union[str, Any]							)      ->  Dict:
							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       :  Union[str, Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Dict      ,   )      ->  str:
							a					=    UMTaModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(
							    input_ids=__lowerCamelCase      ,   decoder_input_ids=__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   decoder_attention_mask=__lowerCamelCase      ,   )
							a					=    model(input_ids=__lowerCamelCase      ,   decoder_input_ids=__lowerCamelCase							)
							a					=    result.last_hidden_state
							a					=    result.past_key_values
							a					=    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       :  Any      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Tuple      ,   )      ->  List[Any]:
							a					=    UMTaModel(config=__lowerCamelCase							).get_decoder().to(__lowerCamelCase							).eval()
							# first forward pass
							a					=    model(__lowerCamelCase      ,   use_cache=__lowerCamelCase							)
							a					=    model(__lowerCamelCase							)
							a					=    model(__lowerCamelCase      ,   use_cache=__lowerCamelCase							)

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

							a					,	a					=    outputs.to_tuple()

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

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

							a					=    model(__lowerCamelCase							)["last_hidden_state"]
							a					=    model(__lowerCamelCase      ,   past_key_values=__lowerCamelCase							)["last_hidden_state"]

							# select random slice
							a					=    ids_tensor((1,)      ,   output_from_past.shape[-1]							).item()
							a					=    output_from_no_past[:, -1, random_slice_idx].detach()
							a					=    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       :  int      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  int      ,   )      ->  List[str]:
							a					=    UMTaModel(config=__lowerCamelCase							).to(__lowerCamelCase							).half().eval()
							a					=    model(**__lowerCamelCase							)["last_hidden_state"]
							self.parent.assertFalse(torch.isnan(__lowerCamelCase							).any().item()							)




@require_torch
class 					snake_case__							(_UpperCamelCase					, _UpperCamelCase					, _UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      (
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				SCREAMING_SNAKE_CASE_ :							int											=      (UMTaForConditionalGeneration,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_ :							str											=      (
				    {
				        """conversational""": UMTaForConditionalGeneration,
				        """feature-extraction""": UMTaModel,
				        """summarization""": UMTaForConditionalGeneration,
				        """text2text-generation""": UMTaForConditionalGeneration,
				        """translation""": UMTaForConditionalGeneration,
				        """question-answering""": UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      True
				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      False
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      False
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      True
				SCREAMING_SNAKE_CASE_ :							Union[str, Any]											=      True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				SCREAMING_SNAKE_CASE_ :							Tuple											=      [0.8, 0.9]





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  int:
							a					=    UMTaModelTester(self							)





				@unittest.skip("Test has a segmentation fault on torch 1.8.0"							)
				def 							__UpperCAmelCase							(   self       :  int							)      ->  int:
							a					=    self.model_tester.prepare_config_and_inputs()
							a					=    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       :  int							)      ->  Optional[int]:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model_fpaa_forward(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int							)      ->  Optional[int]:
							a					=    ["encoder_attentions", "decoder_attentions", "cross_attentions"]
							a					=    self.model_tester.prepare_config_and_inputs()
							a					=    config_and_inputs[0]
							a					=    UMTaForConditionalGeneration(__lowerCamelCase							).eval()
							model.to(__lowerCamelCase							)

							a					=    {
							    "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()							):
										a					=    {name: mask}
										# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
										if name == "head_mask":
													a					=    torch.ones(
													    config.num_decoder_layers      ,   config.num_heads      ,   device=__lowerCamelCase							)

										a					=    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
										a					=    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]							)      ->  int:
							pass




@require_torch
@require_sentencepiece
@require_tokenizers
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				@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       :  str							)      ->  str:
							a					=    UMTaForConditionalGeneration.from_pretrained("google/umt5-small"      ,   return_dict=__lowerCamelCase							).to(__lowerCamelCase							)
							a					=    AutoTokenizer.from_pretrained("google/umt5-small"      ,   use_fast=__lowerCamelCase      ,   legacy=__lowerCamelCase							)
							a					=    [
							    "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>.",
							]
							a					=    tokenizer(__lowerCamelCase      ,   return_tensors="pt"      ,   padding=__lowerCamelCase							).input_ids
							# fmt: off
							a					=    torch.tensor(
							    [
							        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
							        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
							        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
							    ]							)
							# fmt: on
							torch.testing.assert_allclose(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    model.generate(input_ids.to(__lowerCamelCase							)							)
							a					=    [
							    "<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>",
							]
							a					=    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     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







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




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  str      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict=None      ,   __lowerCamelCase       :  Union[str, Any]=None							)      ->  Tuple:
							a					=    file_names
							a					=    image_transform
							a					=    label_to_id





				def __len__(   self       :  Any							)      ->  Tuple:
							return len(self.file_names							)





				def __getitem__(   self       :  List[Any]      ,   __lowerCamelCase       :  List[Any]							)      ->  int:
							a					=    self.file_names[idx]
							a					=    PIL.Image.open(__lowerCamelCase							)
							a					=    raw_image.convert("RGB"							)
							if self.image_transform is not None:
										a					=    self.image_transform(__lowerCamelCase							)
							a					=    extract_label(__lowerCamelCase							)
							if self.label_to_id is not None:
										a					=    self.label_to_id[label]
							return {"image": image, "label": label}




def     __magic_name__       (				A	:      str,						A	:      int     ):






			'''simple docstring'''







			if args.with_tracking:
						a					=    Accelerator(
						    cpu=args.cpu,						mixed_precision=args.mixed_precision,						log_with="all",						project_dir=args.project_dir     )
			else:
						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(A,						(list, tuple)     ):
						a					=    (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":
									a					=    args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									a					=    int(args.checkpointing_steps     )
						else:
									raise ValueError(
									    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""     )
			else:
						a					=    None

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

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

			# Build the label correspondences
			a					=    [extract_label(A     ) for fname in file_names]
			a					=    list(set(A     )     )
			id_to_label.sort()
			a					=    {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
			a					=    np.random.permutation(len(A     )     )
			a					=    int(0.8 * len(A     )     )
			a					=    random_perm[:cut]
			a					=    random_perm[cut:]

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

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

			# Instantiate dataloaders.
			a					=    DataLoader(A,						shuffle=A,						batch_size=A,						num_workers=4     )
			a					=    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)
			a					=    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).
			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=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.
			a					,	a					,	a					,	a					,	a					=    accelerator.prepare(
			    A,						A,						A,						A,						A     )
			# We need to keep track of how many total steps we have iterated over
			a					=    0
			# We also need to keep track of the starting epoch so files are named properly
			a					=    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     )
									a					=    os.path.basename(args.resume_from_checkpoint     )
						else:
									# Get the most recent checkpoint
									a					=    [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
									dirs.sort(key=os.path.getctime     )
									a					=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						a					=    os.path.splitext(A     )[0]

						if "epoch" in training_difference:
									a					=    int(training_difference.replace("epoch_",						""     )     ) + 1
									a					=    None
						else:
									a					=    int(training_difference.replace("step_",						""     )     )
									a					=    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:
									a					=    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
									a					=    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
									a					=    train_dataloader
						for batch in active_dataloader:
									# 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(A     )
									a					=    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     ):
												a					=    F"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		a					=    os.path.join(args.output_dir,						A     )
															accelerator.save_state(A     )
						model.eval()
						a					=    0
						a					=    0
						for step, batch in enumerate(A     ):
									# 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(A     )
									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}"""     )
						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":
									a					=    F"""epoch_{epoch}"""
									if args.output_dir is not None:
												a					=    os.path.join(args.output_dir,						A     )
									accelerator.save_state(A     )

			if args.with_tracking:
						accelerator.end_training()




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    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",						)
			a					=    parser.parse_args()
			a					=    {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
			training_function(A,						A     )


if __name__ == "__main__":
		main()






