



'''simple docstring'''



from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCAmelCase_     :       str								=						logging.get_logger(__name__)


if is_torch_available():
							import torch
							from torch import nn
							from torch.utils.data import Dataset

							from transformers import Trainer
							class 	lowerCamelCase_							(					a__			):







													def __init__(  self   :      List[Any]				,    lowerCAmelCase__   :      Tuple = 1_01				):

																				"""simple docstring"""
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			length







													def __len__(  self   :      Tuple				):

																				"""simple docstring"""
																				return self.length







													def __getitem__(  self   :      List[str]				,    lowerCAmelCase__   :      Dict				):

																				"""simple docstring"""
																				return i


							class 	lowerCamelCase_							:







													def __call__(  self   :      str				,    lowerCAmelCase__   :      Dict				):

																				"""simple docstring"""
																				return {"input_ids": torch.tensor(lowerCAmelCase__				), "labels": torch.tensor(lowerCAmelCase__				)}


							class 	lowerCamelCase_							(					nn.Module			):







													def __init__(  self   :      str				):

																				"""simple docstring"""
																				super().__init__()
																				# Add some (unused) params otherwise DDP will complain.
																				SCREAMING_SNAKE_CASE					:      Any     					=			nn.Linear(1_20				,    80				)







													def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int=None				):

																				"""simple docstring"""
																				if labels is not None:
																											return torch.tensor(0.0				,    device=input_ids.device				), input_ids
																				else:
																											return input_ids
class 	lowerCamelCase_							(					a__			):







						@require_torch_neuroncore
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
													SCREAMING_SNAKE_CASE					:      str     					=			self.get_auto_remove_tmp_dir()
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			F"""--output_dir {output_dir}""".split()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			["torchrun"] + distributed_args + args
													execute_subprocess_async(lowerCAmelCase__				,    env=self.get_env()				)
													# successful return here == success - any errors would have caused an error in the sub-call


class 	lowerCamelCase_							(					a__			):







						@require_torch_multi_gpu
						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
													SCREAMING_SNAKE_CASE					:      int     					=			self.get_auto_remove_tmp_dir()
													SCREAMING_SNAKE_CASE					:      int     					=			F"""--output_dir {output_dir}""".split()
													SCREAMING_SNAKE_CASE					:      Any     					=			["torchrun"] + distributed_args + args
													execute_subprocess_async(lowerCAmelCase__				,    env=self.get_env()				)
													# successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
							# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
							#
							# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

							lowerCAmelCase_     :       List[str]								=						HfArgumentParser((TrainingArguments,))
							lowerCAmelCase_     :       Tuple								=						parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '
							    f'distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'
							)

							# Essentially, what we want to verify in the distributed case is that we get all samples back,
							# in the right order. (this is crucial for prediction for instance)
							for dataset_length in [101, 40, 7]:
														lowerCAmelCase_     :       List[Any]								=						DummyDataset(dataset_length)
														def 					lowerCAmelCase_   (						A  :				EvalPrediction				):
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			list(range(len(_lowercase				)				)				)
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					if not success and training_args.local_rank == 0:
																												logger.warning(
																												    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
																												    F"""{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"""				)
																					return {"success": success}

														lowerCAmelCase_     :       List[str]								=						Trainer(
														    model=DummyModel(),
														    args=training_args,
														    data_collator=DummyDataCollator(),
														    eval_dataset=dataset,
														    compute_metrics=compute_metrics,
														)
														lowerCAmelCase_     :       Tuple								=						trainer.evaluate()
														logger.info(metrics)
														if metrics["eval_success"] is not True:
																					logger.error(metrics)
																					exit(1)

														lowerCAmelCase_     :       int								=						trainer.predict(dataset)
														logger.info(p.metrics)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics)
																					exit(1)

														lowerCAmelCase_     :       Optional[Any]								=						2

														lowerCAmelCase_     :       Optional[int]								=						trainer.evaluate()
														logger.info(metrics)
														if metrics["eval_success"] is not True:
																					logger.error(metrics)
																					exit(1)

														lowerCAmelCase_     :       Optional[int]								=						trainer.predict(dataset)
														logger.info(p.metrics)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics)
																					exit(1)






														lowerCAmelCase_     :       List[Any]								=						None









'''simple docstring'''



import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				Optional[int]					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			1.5
							SCREAMING_SNAKE_CASE					:      str     					=			int(factor * num_class_images				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ClipClient(
							    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1				)

							os.makedirs(F"""{class_data_dir}/images"""					,		exist_ok=A				)
							if len(list(Path(F"""{class_data_dir}/images"""				).iterdir()				)				) >= num_class_images:
														return

							while True:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			client.query(text=A				)
														if len(A				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			int(factor * num_images				)
																					SCREAMING_SNAKE_CASE					:      str     					=			ClipClient(
																					    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1					,		)

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0
							SCREAMING_SNAKE_CASE					:      Tuple     					=			tqdm(desc='''downloading real regularization images'''					,		total=A				)

							with open(F"""{class_data_dir}/caption.txt"""					,		'''w'''				) as fa, open(F"""{class_data_dir}/urls.txt"""					,		'''w'''				) as fa, open(
							    F"""{class_data_dir}/images.txt"""					,		'''w'''				) as fa:
														while total < num_class_images:
																					SCREAMING_SNAKE_CASE					:      int     					=			class_images[count]
																					count += 1
																					try:
																												SCREAMING_SNAKE_CASE					:      int     					=			requests.get(images['''url''']				)
																												if img.status_code == 200:
																																			SCREAMING_SNAKE_CASE					:      List[str]     					=			Image.open(BytesIO(img.content				)				)
																																			with open(F"""{class_data_dir}/images/{total}.jpg"""					,		'''wb'''				) as f:
																																										f.write(img.content				)
																																			fa.write(images['''caption'''] + '''\n'''				)
																																			fa.write(images['''url'''] + '''\n'''				)
																																			fa.write(F"""{class_data_dir}/images/{total}.jpg""" + '''\n'''				)
																																			total += 1
																																			pbar.update(1				)
																												else:
																																			continue
																					except Exception:
																												continue
							return
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			argparse.ArgumentParser(''''''					,		add_help=A				)
							parser.add_argument('''--class_prompt'''					,		help='''text prompt to retrieve images'''					,		required=A					,		type=A				)
							parser.add_argument('''--class_data_dir'''					,		help='''path to save images'''					,		required=A					,		type=A				)
							parser.add_argument('''--num_class_images'''					,		help='''number of images to download'''					,		default=200					,		type=A				)
							return parser.parse_args()


if __name__ == "__main__":
							lowerCAmelCase_     :       List[str]								=						parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




