




from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						import torch

						from transformers.modeling_outputs import BaseModelOutput

						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

_A     =			logging.get_logger(__name__)


@add_end_docstrings(__UpperCamelCase     )
class  A		(  __UpperCamelCase     ):



		def __init__(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								super().__init__(**a_   )

								if self.framework == "tf":
														raise ValueError(f"The {self.__class__} is only available in PyTorch."   )

								requires_backends(self,	'''vision'''   )
								self.check_model_type(a_   )



		def __call__(     self,	UpperCamelCase__,	UpperCamelCase__ = None,	**UpperCamelCase__,	):







								"""simple docstring"""



								if "text_queries" in kwargs:
														lowerCAmelCase_      			=  kwargs.pop('''text_queries'''   )

								if isinstance(a_,	(str, Image.Image)   ):
														lowerCAmelCase_      			=  {'''image''': image, '''candidate_labels''': candidate_labels}
								else:
														lowerCAmelCase_      			=  image
								lowerCAmelCase_      			=  super().__call__(a_,	**a_   )
								return results



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {}
								if "threshold" in kwargs:
														lowerCAmelCase_      			=  kwargs['''threshold''']
								if "top_k" in kwargs:
														lowerCAmelCase_      			=  kwargs['''top_k''']
								return {}, {}, postprocess_params



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  load_image(inputs['''image''']   )
								lowerCAmelCase_      			=  inputs['''candidate_labels''']
								if isinstance(a_,	a_   ):
														lowerCAmelCase_      			=  candidate_labels.split(''','''   )

								lowerCAmelCase_      			=  torch.tensor([[image.height, image.width]],	dtype=torch.intaa   )
								for i, candidate_label in enumerate(a_   ):
														lowerCAmelCase_      			=  self.tokenizer(a_,	return_tensors=self.framework   )
														lowerCAmelCase_      			=  self.image_processor(a_,	return_tensors=self.framework   )
														yield {
														    "is_last": i == len(a_   ) - 1,
														    "target_size": target_size,
														    "candidate_label": candidate_label,
														    **text_inputs,
														    **image_features,
														}



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  model_inputs.pop('''target_size'''   )
								lowerCAmelCase_      			=  model_inputs.pop('''candidate_label'''   )
								lowerCAmelCase_      			=  model_inputs.pop('''is_last'''   )

								lowerCAmelCase_      			=  self.model(**a_   )

								lowerCAmelCase_      			=  {'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
								return model_outputs



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__=0.1,	UpperCamelCase__=None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  []
								for model_output in model_outputs:
														lowerCAmelCase_      			=  model_output['''candidate_label''']
														lowerCAmelCase_      			=  BaseModelOutput(a_   )
														lowerCAmelCase_      			=  self.image_processor.post_process_object_detection(
														    outputs=a_,	threshold=a_,	target_sizes=model_output['''target_size''']   )[0]

														for index in outputs["scores"].nonzero():
																				lowerCAmelCase_      			=  outputs['''scores'''][index].item()
																				lowerCAmelCase_      			=  self._get_bounding_box(outputs['''boxes'''][index][0]   )

																				lowerCAmelCase_      			=  {'''score''': score, '''label''': label, '''box''': box}
																				results.append(a_   )

								lowerCAmelCase_      			=  sorted(a_,	key=lambda UpperCamelCase__   : x["score"],	reverse=a_   )
								if top_k:
														lowerCAmelCase_      			=  results[:top_k]

								return results







		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								if self.framework != "pt":
														raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''   )
								lowerCAmelCase_      			=  box.int().tolist()
								lowerCAmelCase_      			=  {
								    '''xmin''': xmin,
								    '''ymin''': ymin,
								    '''xmax''': xmax,
								    '''ymax''': ymax,
								}
								return bbox









import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
				# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
				# setup instructions, if using on-demand hardware

				# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
				# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
				# Throw an error if user passes both BYO and on-demand cluster args
				# Otherwise, use default values
				__A   			=argparse.ArgumentParser()
				parser.add_argument("--user", type=str, default="ubuntu")
				parser.add_argument("--host", type=str, default="localhost")
				parser.add_argument("--key_path", type=str, default=None)
				parser.add_argument("--instance", type=str, default="V100:1")
				parser.add_argument("--provider", type=str, default="cheapest")
				parser.add_argument("--use_spot", type=bool, default=False)
				parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
				__A			,			__A   			=parser.parse_known_args()
				if args.host != "localhost":
								if args.instance != "V100:1" or args.provider != "cheapest":
												raise ValueError("Cannot specify both BYO and on-demand cluster args")
								__A   			=rh.cluster(
								    name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
								)
				else:
								__A   			=rh.cluster(
								    name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
								)
				__A   			=args.example.rsplit("/", 1)[0]

				# Set up remote environment
				cluster.install_packages(["pip:./"])  # Installs transformers from local source
				# Note transformers is copied into the home directory on the remote machine, so we can install from there
				cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
				cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

				# Run example. You can bypass the CLI wrapper and paste your own code here.
				cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

				# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
				# from my_script... import train
				# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
				# launch_train_gpu = rh.function(fn=train,
				#                                system=gpu,
				#                                reqs=reqs,
				#                                name='train_bert_glue')
				#
				# We can pass in arguments just like we would to a function:
				# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
				#                  stream_logs=True)





