

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,
							)


lowerCAmelCase__             =		logging.get_logger(__name__)


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







@add_end_docstrings(_lowercase				)
class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		def __init__(					self						,							*__snake_case						,							**__snake_case):
			super().__init__(*__snake_case						,							**__snake_case)

			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 		A__       (					self						,							**__snake_case):
			_UpperCamelCase :		Union[str, Any]								=    {}
			_UpperCamelCase :		Optional[Any]								=    {}
			if "subtask" in kwargs:
				_UpperCamelCase :		int								=    kwargs['subtask']
				_UpperCamelCase :		Any								=    kwargs['subtask']
			if "threshold" in kwargs:
				_UpperCamelCase :		List[Any]								=    kwargs['threshold']
			if "mask_threshold" in kwargs:
				_UpperCamelCase :		List[Any]								=    kwargs['mask_threshold']
			if "overlap_mask_area_threshold" in kwargs:
				_UpperCamelCase :		Tuple								=    kwargs['overlap_mask_area_threshold']

			return preprocess_kwargs, {}, postprocess_kwargs


		def __call__(					self						,							__snake_case						,							**__snake_case):
			return super().__call__(__snake_case						,							**__snake_case)


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


		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		Optional[int]								=    model_inputs.pop('target_size')
			_UpperCamelCase :		List[Any]								=    self.model(**__snake_case)
			_UpperCamelCase :		str								=    target_size
			return model_outputs






		def 		A__       (					self						,							__snake_case						,							__snake_case=None						,							__snake_case=0.9						,							__snake_case=0.5						,							__snake_case=0.5):
			_UpperCamelCase :		Any								=    None
			if subtask in {"panoptic", None} and hasattr(self.image_processor						,							'post_process_panoptic_segmentation'):
				_UpperCamelCase :		List[str]								=    self.image_processor.post_process_panoptic_segmentation
			elif subtask in {"instance", None} and hasattr(self.image_processor						,							'post_process_instance_segmentation'):
				_UpperCamelCase :		Optional[int]								=    self.image_processor.post_process_instance_segmentation

			if fn is not None:
				_UpperCamelCase :		Optional[Any]								=    fn(
				    __snake_case						,							threshold=__snake_case						,							mask_threshold=__snake_case						,							overlap_mask_area_threshold=__snake_case						,							target_sizes=model_outputs['target_size']						,							)[0]

				_UpperCamelCase :		int								=    []
				_UpperCamelCase :		Dict								=    outputs['segmentation']

				for segment in outputs["segments_info"]:
					_UpperCamelCase :		int								=    (segmentation == segment['id']) * 2_55
					_UpperCamelCase :		Dict								=    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 :		Optional[Any]								=    self.image_processor.post_process_semantic_segmentation(
				    __snake_case						,							target_sizes=model_outputs['target_size'])[0]

				_UpperCamelCase :		Dict								=    []
				_UpperCamelCase :		int								=    outputs.numpy()
				_UpperCamelCase :		int								=    np.unique(__snake_case)

				for label in labels:
					_UpperCamelCase :		Dict								=    (segmentation == label) * 2_55
					_UpperCamelCase :		List[str]								=    Image.fromarray(mask.astype(np.uinta)						,							mode='L')
					_UpperCamelCase :		int								=    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 os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase__             =		2 << 2_0  # 2mb
lowerCAmelCase__             =		get_logger(__name__)







class 						lowercase     (					_lowercase				):


		"""simple docstring"""




		def 		A__       (					self):
			_UpperCamelCase :		Optional[Any]								=    self._options.get_all_options().get('runner')
			return runner in [None, "DirectRunner", "PortableRunner"]






def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  str=False						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    FileSystems
	if fs.exists(UpperCAmelCase_						):
		if force_upload:
			logger.info(F'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''						)
		else:
			logger.info(F'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''						)
			return
	with fs.create(UpperCAmelCase_						) as remote_file:
		with open(UpperCAmelCase_				,							'rb'						) as local_file:
			_UpperCamelCase :		List[str]								=    local_file.read(UpperCAmelCase_						)
			while chunk:
				remote_file.write(UpperCAmelCase_						)
				_UpperCamelCase :		List[str]								=    local_file.read(UpperCAmelCase_						)







def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[int]=False						)    ->					Any:




	'''simple docstring'''




	_UpperCamelCase :		List[str]								=    FileSystems
	if os.path.exists(UpperCAmelCase_						):
		if force_download:
			logger.info(F'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''						)
		else:
			logger.info(F'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''						)
			return
	with fs.open(UpperCAmelCase_						) as remote_file:
		with open(UpperCAmelCase_				,							'wb'						) as local_file:
			_UpperCamelCase :		Union[str, Any]								=    remote_file.read(UpperCAmelCase_						)
			while chunk:
				local_file.write(UpperCAmelCase_						)
				_UpperCamelCase :		List[str]								=    remote_file.read(UpperCAmelCase_						)




