






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,
	)


UpperCAmelCase_					:		Dict           = logging.get_logger(__name__)


UpperCAmelCase_					:		List[Any]           = Dict[str, Any]
UpperCAmelCase_					:		List[Any]           = List[Prediction]

@add_end_docstrings(_a )
class        _SCREAMING_SNAKE_CASE			(     _a ):





	def __init__(						self							:   Tuple					,    *__lowerCamelCase							:   Dict					,    **__lowerCamelCase							:   Union[str, Any]      ):
		super().__init__(*__lowerCamelCase					,    **__lowerCamelCase      )

		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							:   Union[str, Any]					,    **__lowerCamelCase							:   Dict      ):
		UpperCamelCase	:int		=      {}
		UpperCamelCase	:Union[str, Any]		=      {}
		if "subtask" in kwargs:
			UpperCamelCase	:Optional[Any]		=      kwargs["""subtask"""]
			UpperCamelCase	:Dict		=      kwargs["""subtask"""]
		if "threshold" in kwargs:
			UpperCamelCase	:int		=      kwargs["""threshold"""]
		if "mask_threshold" in kwargs:
			UpperCamelCase	:List[Any]		=      kwargs["""mask_threshold"""]
		if "overlap_mask_area_threshold" in kwargs:
			UpperCamelCase	:Optional[Any]		=      kwargs["""overlap_mask_area_threshold"""]

		return preprocess_kwargs, {}, postprocess_kwargs





	def __call__(						self							:   List[Any]					,    __lowerCamelCase							:   List[str]					,    **__lowerCamelCase							:   Dict      ):
		return super().__call__(__lowerCamelCase					,    **__lowerCamelCase      )





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   int=None      ):
		UpperCamelCase	:List[str]		=      load_image(__lowerCamelCase      )
		UpperCamelCase	:Dict		=      [(image.height, image.width)]
		if self.model.config.__class__.__name__ == "OneFormerConfig":
			if subtask is None:
				UpperCamelCase	:List[str]		=      {}
			else:
				UpperCamelCase	:Optional[Any]		=      {"""task_inputs""": [subtask]}
			UpperCamelCase	:Optional[Any]		=      self.image_processor(images=[image]					,    return_tensors="""pt"""					,    **__lowerCamelCase      )
			UpperCamelCase	:Tuple		=      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							:   Union[str, Any]					,    __lowerCamelCase							:   Tuple      ):
		UpperCamelCase	:Any		=      model_inputs.pop("""target_size"""      )
		UpperCamelCase	:Dict		=      self.model(**__lowerCamelCase      )
		UpperCamelCase	:int		=      target_size
		return model_outputs





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Optional[Any]=None					,    __lowerCamelCase							:   Tuple=0.9					,    __lowerCamelCase							:   Any=0.5					,    __lowerCamelCase							:   Union[str, Any]=0.5      ):
		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	:List[Any]		=      self.image_processor.post_process_instance_segmentation

		if fn is not None:
			UpperCamelCase	:List[Any]		=      fn(
			    __lowerCamelCase					,    threshold=__lowerCamelCase					,    mask_threshold=__lowerCamelCase					,    overlap_mask_area_threshold=__lowerCamelCase					,    target_sizes=model_outputs["""target_size"""]					,    )[0]

			UpperCamelCase	:Optional[int]		=      []
			UpperCamelCase	:int		=      outputs["""segmentation"""]

			for segment in outputs["segments_info"]:
				UpperCamelCase	:Any		=      (segmentation == segment["""id"""]) * 255
				UpperCamelCase	:List[str]		=      Image.fromarray(mask.numpy().astype(np.uinta      )					,    mode="""L"""      )
				UpperCamelCase	:Optional[Any]		=      self.model.config.idalabel[segment["""label_id"""]]
				UpperCamelCase	:Tuple		=      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(
			    __lowerCamelCase					,    target_sizes=model_outputs["""target_size"""]      )[0]

			UpperCamelCase	:Union[str, Any]		=      []
			UpperCamelCase	:int		=      outputs.numpy()
			UpperCamelCase	:List[str]		=      np.unique(__lowerCamelCase      )

			for label in labels:
				UpperCamelCase	:Optional[int]		=      (segmentation == label) * 255
				UpperCamelCase	:List[str]		=      Image.fromarray(mask.astype(np.uinta      )					,    mode="""L"""      )
				UpperCamelCase	:Optional[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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_					:		int           = {
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_					:		Tuple           = [
	    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''UniSpeechSatForAudioFrameClassification''',
	    '''UniSpeechSatForCTC''',
	    '''UniSpeechSatForPreTraining''',
	    '''UniSpeechSatForSequenceClassification''',
	    '''UniSpeechSatForXVector''',
	    '''UniSpeechSatModel''',
	    '''UniSpeechSatPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_unispeech_sat import (
		    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    UniSpeechSatForAudioFrameClassification,
		    UniSpeechSatForCTC,
		    UniSpeechSatForPreTraining,
		    UniSpeechSatForSequenceClassification,
		    UniSpeechSatForXVector,
		    UniSpeechSatModel,
		    UniSpeechSatPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase_					:		str           = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
