






'''simple docstring'''





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				(lowercase__				)		->				Tuple:

						'''simple docstring'''


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





class 		SCREAMING_SNAKE_CASE__   (     UpperCamelCase__    ):



		def __init__(    self:    Optional[int]				,   a:    Any				,   a:    Dict=None				,   a:    Optional[Any]=None)  ->str:






								'''simple docstring'''


								a_				     =   file_names
								a_				     =   image_transform
								a_				     =   label_to_id



		def __len__(    self:    List[Any])  ->Dict:






								'''simple docstring'''


								return len(self.file_names)




		def __getitem__(    self:    Optional[int]				,   a:    Any)  ->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				(lowercase__	,lowercase__				)		->				Union[str, Any]:

						'''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(__a	,(list, tuple)				):
												a_				     =   (image_size, image_size)

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

						# Now we train the model
						for epoch in range(__a				):
												model.train()
												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
																		a_				     =   model(__a				)
																		a_				     =   torch.nn.functional.cross_entropy(__a	,batch["label"]				)
																		accelerator.backward(__a				)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

												model.eval()
												a_				     =   0
												a_				     =   0
												for _, 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_				     =   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				()		->				Optional[Any]:

						'''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(
						    "--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(
						    "--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("--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(__a	,__a				)


if __name__ == "__main__":
							main()











'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
							from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

							from .test_feature_extraction_speechta import floats_list


a_      = get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')





@require_torch
class 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):



		def _lowerCAmelCase     (    self:    str)  ->Optional[Any]:






								'''simple docstring'''


								a_				     =   tempfile.mkdtemp()

								a_				     =   SpeechTaTokenizer(a)
								tokenizer.save_pretrained(self.tmpdirname)

								a_				     =   {
								    "feature_size": 1,
								    "padding_value": 0.0,
								    "sampling_rate": 1_60_00,
								    "do_normalize": False,
								    "num_mel_bins": 80,
								    "hop_length": 16,
								    "win_length": 64,
								    "win_function": "hann_window",
								    "fmin": 80,
								    "fmax": 76_00,
								    "mel_floor": 1e-10,
								    "reduction_factor": 2,
								    "return_attention_mask": True,
								}

								a_				     =   os.path.join(self.tmpdirname				,   a)
								with open(self.feature_extraction_file				,   "w"				,   encoding="utf-8") as fp:
														fp.write(json.dumps(a) + "\n")



		def _lowerCAmelCase     (    self:    Any				,   **a:    List[Any])  ->Optional[Any]:






								'''simple docstring'''


								return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,   **a)



		def _lowerCAmelCase     (    self:    Union[str, Any]				,   **a:    int)  ->str:






								'''simple docstring'''


								return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,   **a)



		def _lowerCAmelCase     (    self:    int)  ->Optional[Any]:






								'''simple docstring'''


								shutil.rmtree(self.tmpdirname)



		def _lowerCAmelCase     (    self:    Any)  ->str:






								'''simple docstring'''


								a_				     =   self.get_tokenizer()
								a_				     =   self.get_feature_extractor()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								processor.save_pretrained(self.tmpdirname)
								a_				     =   SpeechTaProcessor.from_pretrained(self.tmpdirname)

								self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer.get_vocab())
								self.assertIsInstance(processor.tokenizer				,   a)

								self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor.to_json_string())
								self.assertIsInstance(processor.feature_extractor				,   a)



		def _lowerCAmelCase     (    self:    List[Any])  ->Optional[int]:






								'''simple docstring'''


								a_				     =   SpeechTaProcessor(tokenizer=self.get_tokenizer()				,   feature_extractor=self.get_feature_extractor())
								processor.save_pretrained(self.tmpdirname)

								a_				     =   self.get_tokenizer(bos_token="(BOS)"				,   eos_token="(EOS)")
								a_				     =   self.get_feature_extractor(do_normalize=a				,   padding_value=1.0)

								a_				     =   SpeechTaProcessor.from_pretrained(
								    self.tmpdirname				,   bos_token="(BOS)"				,   eos_token="(EOS)"				,   do_normalize=a				,   padding_value=1.0)

								self.assertEqual(processor.tokenizer.get_vocab()				,   tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.tokenizer				,   a)

								self.assertEqual(processor.feature_extractor.to_json_string()				,   feature_extractor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.feature_extractor				,   a)



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






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   floats_list((3, 10_00))

								a_				     =   feature_extractor(audio=a				,   return_tensors="np")
								a_				     =   processor(audio=a				,   return_tensors="np")

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()				,   input_processor[key].sum()				,   delta=1e-2)



		def _lowerCAmelCase     (    self:    Tuple)  ->Union[str, Any]:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   floats_list((3, 10_00))

								a_				     =   feature_extractor(audio_target=a				,   return_tensors="np")
								a_				     =   processor(audio_target=a				,   return_tensors="np")

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()				,   input_processor[key].sum()				,   delta=1e-2)



		def _lowerCAmelCase     (    self:    str)  ->Dict:






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   "This is a test string"

								a_				     =   processor(text=a)
								a_				     =   tokenizer(a)

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]				,   encoded_processor[key])



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






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   "This is a test string"

								a_				     =   processor(text_target=a)
								a_				     =   tokenizer(a)

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]				,   encoded_processor[key])



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






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								a_				     =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								a_				     =   processor.batch_decode(a)
								a_				     =   tokenizer.batch_decode(a)

								self.assertListEqual(a				,   a)




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






								'''simple docstring'''


								a_				     =   self.get_feature_extractor()
								a_				     =   self.get_tokenizer()

								a_				     =   SpeechTaProcessor(tokenizer=a				,   feature_extractor=a)

								self.assertListEqual(
								    processor.model_input_names				,   feature_extractor.model_input_names				,   msg="`processor` and `feature_extractor` model input names do not match"				,   )



