





import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed




class 					UpperCamelCase_    (       UpperCamelCase__      ):


							def __init__(							self      :Optional[int]							,			__A      :Tuple							,			__A      :int							,			__A      :int=False							,			__A      :str=1024							,			__A      :Dict=1024							,			__A      :Any=3.6							,			__A      :Tuple=False							,			)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= tokenizer
									SCREAMING_SNAKE_CASE__								= tokenizer.bos_token_id
									SCREAMING_SNAKE_CASE__								= dataset
									SCREAMING_SNAKE_CASE__								= seq_length
									SCREAMING_SNAKE_CASE__								= 0
									SCREAMING_SNAKE_CASE__								= infinite
									SCREAMING_SNAKE_CASE__								= 0
									SCREAMING_SNAKE_CASE__								= tokenized

									if self.tokenized:
											SCREAMING_SNAKE_CASE__								= seq_length * num_of_sequences
											SCREAMING_SNAKE_CASE__								= """input_ids"""
									else:
											SCREAMING_SNAKE_CASE__								= seq_length * chars_per_token * num_of_sequences
											SCREAMING_SNAKE_CASE__								= """content"""


							def __iter__(							self      :int						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= iter(self.dataset						)
									SCREAMING_SNAKE_CASE__								= True
									while more_examples:
											SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= [], 0
											while True:
													if buffer_len >= self.max_buffer_size:
															break
													try:
															buffer.append(next(__A						)[self.content_field]						)
															buffer_len += len(buffer[-1]						)
													except StopIteration:
															if self.infinite:
																	SCREAMING_SNAKE_CASE__								= iter(self.dataset						)
																	self.epoch += 1
																	logger.info(f'''Dataset epoch: {self.epoch}'''						)
															else:
																	SCREAMING_SNAKE_CASE__								= False
																	break
											if self.tokenized:
													SCREAMING_SNAKE_CASE__								= buffer
											else:
													SCREAMING_SNAKE_CASE__								= self.tokenizer(__A							,			truncation=__A						)["""input_ids"""]
											SCREAMING_SNAKE_CASE__								= []
											for tokenized_input in tokenized_inputs:
													all_token_ids.extend(tokenized_input + [self.concat_token_id]						)
											for i in range(0							,			len(__A						)							,			self.seq_length						):
													SCREAMING_SNAKE_CASE__								= all_token_ids[i : i + self.seq_length]
													if len(__A						) == self.seq_length:
															self.current_size += 1
															yield torch.tensor(__A						)


							def        _snake_case					(							self      :Tuple							,			__A      :List[Any]=1000						)				->				int:



									"""simple docstring"""



									return ShufflerIterDataPipe(self							,			buffer_size=__A						)






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Tuple      ):
		SCREAMING_SNAKE_CASE__								= args.model_ckpt.split("""/"""      )[-1]
		SCREAMING_SNAKE_CASE__								= logging.getLogger(__name__      )
		SCREAMING_SNAKE_CASE__								= Path(args.save_dir      ) / """log/"""
		log_dir.mkdir(exist_ok=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= f'''debug_{accelerator.process_index}.log'''
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,				datefmt="""%m/%d/%Y %H:%M:%S"""   ,				level=logging.INFO   ,				handlers=[logging.FileHandler(log_dir / filename      ), logging.StreamHandler()]   ,				)
		if accelerator.is_main_process:  # we only want to setup logging once
				accelerator.init_trackers(UpperCamelCase__   ,				vars(UpperCamelCase__      )      )
				SCREAMING_SNAKE_CASE__								= accelerator.trackers[0].run.name
				logger.setLevel(logging.INFO      )
				datasets.utils.logging.set_verbosity_info()
				transformers.utils.logging.set_verbosity_info()
		else:
				SCREAMING_SNAKE_CASE__								= """"""
				logger.setLevel(logging.ERROR      )
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()
		return logger, run_name






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							str      ):
		SCREAMING_SNAKE_CASE__								= {"""streaming""": True}
		SCREAMING_SNAKE_CASE__								= load_dataset(args.dataset_name_train   ,				split="""train"""   ,				**UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= train_data.shuffle(buffer_size=args.shuffle_buffer   ,				seed=args.seed      )
		SCREAMING_SNAKE_CASE__								= load_dataset(args.dataset_name_valid   ,				split="""train"""   ,				**UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ConstantLengthDataset(
		    UpperCamelCase__   ,				UpperCamelCase__   ,				infinite=UpperCamelCase__   ,				seq_length=args.seq_length   ,				tokenized=args.tokenized      )
		SCREAMING_SNAKE_CASE__								= ConstantLengthDataset(
		    UpperCamelCase__   ,				UpperCamelCase__   ,				infinite=UpperCamelCase__   ,				seq_length=args.seq_length   ,				tokenized=args.tokenized      )
		SCREAMING_SNAKE_CASE__								= train_dataset.shuffle(buffer_size=args.shuffle_buffer      )
		SCREAMING_SNAKE_CASE__								= DataLoader(UpperCamelCase__   ,				batch_size=args.train_batch_size   ,				shuffle=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= DataLoader(UpperCamelCase__   ,				batch_size=args.valid_batch_size      )
		return train_dataloader, eval_dataloader






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict   ,				UpperCamelCase__:							str   ,				UpperCamelCase__:							Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]      ):
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= [], []
		for n, p in model.named_parameters():
				if any(nd in n for nd in no_decay      ):
						params_without_wd.append(UpperCamelCase__      )
				else:
						params_with_wd.append(UpperCamelCase__      )
		return [
		    {"params": params_with_wd, "weight_decay": args.weight_decay},
		    {"params": params_without_wd, "weight_decay": 0.0},
		]






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Optional[Any]      ):
		logger.info(f'''Step {step}: {metrics}'''      )
		if accelerator.is_main_process:
				accelerator.log(UpperCamelCase__   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Tuple   ,				UpperCamelCase__:							Dict   ,				UpperCamelCase__:							Optional[Any]      ):
		# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
		SCREAMING_SNAKE_CASE__								= accelerator.unwrap_model(UpperCamelCase__      ).config
		SCREAMING_SNAKE_CASE__								= 4 if args.gradient_checkpointing else 3
		SCREAMING_SNAKE_CASE__								= args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
		SCREAMING_SNAKE_CASE__								= 24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
		SCREAMING_SNAKE_CASE__								= factor * (
		    1.0
		    + (args.seq_length / (6.0 * config_model.n_embd))
		    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
		)
		SCREAMING_SNAKE_CASE__								= flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
		return tflops






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]      ):
		model.eval()
		SCREAMING_SNAKE_CASE__								= []
		for step, batch in enumerate(UpperCamelCase__      ):
				with torch.no_grad():
						SCREAMING_SNAKE_CASE__								= model(UpperCamelCase__   ,				labels=UpperCamelCase__      )
				SCREAMING_SNAKE_CASE__								= outputs.loss.repeat(args.valid_batch_size      )
				losses.append(accelerator.gather(UpperCamelCase__      )      )
				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		SCREAMING_SNAKE_CASE__								= torch.cat(UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= losses[: eval_dataloader.dataset.current_size].mean()
		try:
				SCREAMING_SNAKE_CASE__								= torch.exp(UpperCamelCase__      )
		except OverflowError:
				SCREAMING_SNAKE_CASE__								= float("""inf"""      )
		return loss.item(), perplexity.item()


# Settings
_lowerCamelCase							=     HfArgumentParser(TrainingArguments)
_lowerCamelCase							=     parser.parse_args()

# Accelerator
_lowerCamelCase							=     ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
_lowerCamelCase							=     Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
_lowerCamelCase							=     {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

_lowerCamelCase							=     Namespace(**vars(args), **acc_state)
_lowerCamelCase							=     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
	_lowerCamelCase							=     Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
_lowerCamelCase			,    _lowerCamelCase							=     setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
	hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
_lowerCamelCase							=     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
	model.gradient_checkpointing_enable()
_lowerCamelCase							=     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_lowerCamelCase			,    _lowerCamelCase							=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_lowerCamelCase							=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_lowerCamelCase							=     get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)






def 			SCREAMING_SNAKE_CASE__							(    ):
		return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
_lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase			,    _lowerCamelCase							=     accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# 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(args.save_dir) if f.is_dir() and 'step' in str(f)]
		dirs.sort(key=os.path.getctime)
		_lowerCamelCase							=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
	# Extract the step of the checkpoint to continue from there
	_lowerCamelCase							=     os.path.splitext(path)[0]
	_lowerCamelCase							=     int(training_difference.replace('step_', ''))

# Train model
model.train()
_lowerCamelCase							=     0
_lowerCamelCase							=     time.time()
_lowerCamelCase							=     0
for step, batch in enumerate(train_dataloader, start=1):
	if args.resume_from_checkpoint and step < resume_step:
		continue  # we need to skip steps until we reach the resumed step
	_lowerCamelCase							=     model(batch, labels=batch, use_cache=False).loss
	_lowerCamelCase							=     accelerator.gather(loss.repeat(args.train_batch_size)).mean()
	loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
	log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
	_lowerCamelCase							=     loss / args.gradient_accumulation_steps
	if step % args.gradient_accumulation_steps != 0:
		# Prevent backward from doing gradient all_reduce in every step
		if accelerator.distributed_type == DistributedType.MULTI_GPU:
			with model.no_sync():
				accelerator.backward(loss)
		else:
			accelerator.backward(loss)
	else:
		_lowerCamelCase							=     get_lr()
		accelerator.backward(loss)
		accelerator.clip_grad_norm_(model.parameters(), 1.0)
		optimizer.step()
		lr_scheduler.step()
		optimizer.zero_grad()
		_lowerCamelCase							=     time.time() - t_start
		_lowerCamelCase							=     compute_tflops(elapsed_time, accelerator, args)
		log_metrics(
		    step,
		    {
		        'steps': completed_steps,
		        'loss/train': loss_tracking,
		        'lr': lr,
		        'tflops': tflops,
		        'time_per_iteration': elapsed_time,
		    },
		)
		_lowerCamelCase							=     time.time()
		_lowerCamelCase							=     0
		completed_steps += 1
	if step % args.save_checkpoint_steps == 0:
		logger.info('Evaluating and saving model checkpoint')
		_lowerCamelCase			,    _lowerCamelCase							=     evaluate(args)
		log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
		accelerator.wait_for_everyone()
		_lowerCamelCase							=     os.path.join(args.save_dir, F'''step_{step}''')
		accelerator.save_state(save_dir)
		if accelerator.is_main_process:
			hf_repo.push_to_hub(commit_message=F'''step {step}''')
		model.train()
	if completed_steps >= args.max_train_steps:
		break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_lowerCamelCase			,    _lowerCamelCase							=     evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
_lowerCamelCase							=     accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_lowerCamelCase							=     os.path.join(args.save_dir, F'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
	hf_repo.push_to_hub(commit_message='final model')






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:




	class 					UpperCamelCase_    :


								@staticmethod
								def        _snake_case					(							*__A      :Optional[Any]							,			**__A      :Any						)				->				str:



										"""simple docstring"""



										pass




@is_pipeline_test
@require_torch_or_tf
@require_vision
class 					UpperCamelCase_    (       unittest.TestCase      ):
							lowerCamelCase_          =   MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							lowerCamelCase_          =   TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


							def        _snake_case					(							self      :Union[str, Any]							,			__A      :str							,			__A      :Dict							,			__A      :List[str]						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= ImageClassificationPipeline(model=__A							,			image_processor=__A							,			top_k=2						)
									SCREAMING_SNAKE_CASE__								= [
									    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						),
									    """http://images.cocodataset.org/val2017/000000039769.jpg""",
									]
									return image_classifier, examples


							def        _snake_case					(							self      :List[Any]							,			__A      :Any							,			__A      :List[str]						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)

									self.assertEqual(
									    __A							,			[
									        {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        {"""score""": ANY(__A						), """label""": ANY(__A						)},
									    ]							,			)

									import datasets

									SCREAMING_SNAKE_CASE__								= datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""							,			"""image"""							,			split="""test"""						)

									# Accepts URL + PIL.Image + lists
									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						),
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        # RGBA
									        dataset[0]["""file"""],
									        # LA
									        dataset[1]["""file"""],
									        # L
									        dataset[2]["""file"""],
									    ]						)
									self.assertEqual(
									    __A							,			[
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									    ]							,			)


							@require_torch
							def        _snake_case					(							self      :str						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """hf-internal-testing/tiny-random-vit"""
									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model=__A						)

									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]							,			)

									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									    ]							,			top_k=2							,			)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									    ]							,			)


							@require_tf
							def        _snake_case					(							self      :Any						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """hf-internal-testing/tiny-random-vit"""
									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model=__A							,			framework="""tf"""						)

									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]							,			)

									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									    ]							,			top_k=2							,			)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									    ]							,			)


							def        _snake_case					(							self      :Dict						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= PreTrainedTokenizer()

									# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
									SCREAMING_SNAKE_CASE__								= pipeline(
									    """image-classification"""							,			model="""hf-internal-testing/tiny-random-vit"""							,			tokenizer=__A						)

									self.assertIs(image_classifier.tokenizer							,			__A						)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-conv"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.4_3_8_5, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.3_2_1, """label""": """tiger cat"""},
									        {"""score""": 0.0_5_0_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.0_1_3_7, """label""": """crib, cot"""},
									        {"""score""": 0.0_0_7, """label""": """radiator"""},
									    ]							,			)

									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-fourier"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.5_6_5_8, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.1_3_0_9, """label""": """tiger cat"""},
									        {"""score""": 0.0_7_2_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.0_7_0_7, """label""": """remote control, remote"""},
									        {"""score""": 0.0_0_8_2, """label""": """computer keyboard, keypad"""},
									    ]							,			)

									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-learned"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.3_0_2_2, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.2_3_6_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.1_8_5_6, """label""": """tiger cat"""},
									        {"""score""": 0.0_3_2_4, """label""": """remote control, remote"""},
									        {"""score""": 0.0_0_9_6, """label""": """quilt, comforter, comfort, puff"""},
									    ]							,			)