



from typing import Any, Dict, List, Union

import numpy as np

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


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import (
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				)


__A             =logging.get_logger(__name__)


__A             =Dict[str, Any]
__A             =List[Prediction]

@add_end_docstrings(__UpperCamelCase       )
class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			def __init__(       self							:			List[Any]	,	*a_							:			Union[str, Any]	,	**a_							:			Tuple			):
					'''simple docstring'''





					super().__init__(*a_	,	**a_			)

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

					requires_backends(self	,	'''vision'''			)
					self.check_model_type(
					    dict(
					        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
					        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
					        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
					        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)




			def 			snake_case__					(       self							:			str	,	**a_							:			List[str]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     {}
					__UpperCAmelCase   :					int													=     {}
					if "subtask" in kwargs:
							__UpperCAmelCase   :					str													=     kwargs['''subtask''']
							__UpperCAmelCase   :					Union[str, Any]													=     kwargs['''subtask''']
					if "threshold" in kwargs:
							__UpperCAmelCase   :					List[Any]													=     kwargs['''threshold''']
					if "mask_threshold" in kwargs:
							__UpperCAmelCase   :					int													=     kwargs['''mask_threshold''']
					if "overlap_mask_area_threshold" in kwargs:
							__UpperCAmelCase   :					List[str]													=     kwargs['''overlap_mask_area_threshold''']

					return preprocess_kwargs, {}, postprocess_kwargs




			def __call__(       self							:			List[Any]	,	a_							:			str	,	**a_							:			str			):
					'''simple docstring'''





					return super().__call__(a_	,	**a_			)




			def 			snake_case__					(       self							:			Dict	,	a_							:			Dict	,	a_							:			Tuple=None			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     load_image(a_			)
					__UpperCAmelCase   :					Dict													=     [(image.height, image.width)]
					if self.model.config.__class__.__name__ == "OneFormerConfig":
							if subtask is None:
									__UpperCAmelCase   :					Tuple													=     {}
							else:
									__UpperCAmelCase   :					Tuple													=     {'''task_inputs''': [subtask]}
							__UpperCAmelCase   :					Optional[int]													=     self.image_processor(images=[image]	,	return_tensors='''pt'''	,	**a_			)
							__UpperCAmelCase   :					int													=     self.tokenizer(
							    inputs['''task_inputs''']	,	padding='''max_length'''	,	max_length=self.model.config.task_seq_len	,	return_tensors=self.framework	,	)['''input_ids''']
					else:
							__UpperCAmelCase   :					Optional[Any]													=     self.image_processor(images=[image]	,	return_tensors='''pt'''			)
					__UpperCAmelCase   :					int													=     target_size
					return inputs




			def 			snake_case__					(       self							:			List[Any]	,	a_							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     model_inputs.pop('''target_size'''			)
					__UpperCAmelCase   :					Optional[int]													=     self.model(**a_			)
					__UpperCAmelCase   :					int													=     target_size
					return model_outputs




			def 			snake_case__					(       self							:			List[Any]	,	a_							:			Tuple	,	a_							:			Dict=None	,	a_							:			str=0.9	,	a_							:			Any=0.5	,	a_							:			Tuple=0.5			):
					'''simple docstring'''





					__UpperCAmelCase   :					Union[str, Any]													=     None
					if subtask in {"panoptic", None} and hasattr(self.image_processor	,	'''post_process_panoptic_segmentation'''			):
							__UpperCAmelCase   :					str													=     self.image_processor.post_process_panoptic_segmentation
					elif subtask in {"instance", None} and hasattr(self.image_processor	,	'''post_process_instance_segmentation'''			):
							__UpperCAmelCase   :					Dict													=     self.image_processor.post_process_instance_segmentation

					if fn is not None:
							__UpperCAmelCase   :					str													=     fn(
							    a_	,	threshold=a_	,	mask_threshold=a_	,	overlap_mask_area_threshold=a_	,	target_sizes=model_outputs['''target_size''']	,	)[0]

							__UpperCAmelCase   :					str													=     []
							__UpperCAmelCase   :					Tuple													=     outputs['''segmentation''']

							for segment in outputs["segments_info"]:
									__UpperCAmelCase   :					Tuple													=     (segmentation == segment['''id''']) * 2_55
									__UpperCAmelCase   :					List[Any]													=     Image.fromarray(mask.numpy().astype(np.uinta			)	,	mode='''L'''			)
									__UpperCAmelCase   :					Tuple													=     self.model.config.idalabel[segment['''label_id''']]
									__UpperCAmelCase   :					Optional[int]													=     segment['''score''']
									annotation.append({'''score''': score, '''label''': label, '''mask''': mask}			)

					elif subtask in {"semantic", None} and hasattr(self.image_processor	,	'''post_process_semantic_segmentation'''			):
							__UpperCAmelCase   :					Tuple													=     self.image_processor.post_process_semantic_segmentation(
							    a_	,	target_sizes=model_outputs['''target_size''']			)[0]

							__UpperCAmelCase   :					str													=     []
							__UpperCAmelCase   :					int													=     outputs.numpy()
							__UpperCAmelCase   :					Optional[int]													=     np.unique(a_			)

							for label in labels:
									__UpperCAmelCase   :					str													=     (segmentation == label) * 2_55
									__UpperCAmelCase   :					List[str]													=     Image.fromarray(mask.astype(np.uinta			)	,	mode='''L'''			)
									__UpperCAmelCase   :					Any													=     self.model.config.idalabel[label]
									annotation.append({'''score''': None, '''label''': label, '''mask''': mask}			)
					else:
							raise ValueError(F'Subtask {subtask} is not supported for model {type(self.model			)}'			)
					return annotation










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A             =logging.getLogger(__name__)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     ArgumentParser()
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')						):
						parser.add_argument(arg.split('''='''						)[0]						)

		return parser.parse_args()

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     parse_args()
		__UpperCAmelCase   :					int													=     88_88
		__UpperCAmelCase   :					Union[str, Any]													=     int(os.environ['''SM_NUM_GPUS''']						)
		__UpperCAmelCase   :					Any													=     json.loads(os.environ['''SM_HOSTS''']						)
		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase   :					Union[str, Any]													=     hosts.index(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase   :					int													=     f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		else:
				__UpperCAmelCase   :					Optional[Any]													=     f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]						)}'
		try:
				subprocess.run(_UpperCAmelCase			,       shell=_UpperCAmelCase						)
		except Exception as e:
				logger.info(_UpperCAmelCase						)


if __name__ == "__main__":
				main()





