
'''simple docstring'''


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,
	)


__SCREAMING_SNAKE_CASE						=      logging.get_logger(__name__)


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




@add_end_docstrings(_a	)
class     __snake_case			(    _a	):




		def __init__(  self					:str			,    *_SCREAMING_SNAKE_CASE					:int			,    **_SCREAMING_SNAKE_CASE					:Any)					->			Any:






								'''simple docstring'''






								super().__init__(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_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 	_snake_case	(  self					:Any			,    **_SCREAMING_SNAKE_CASE					:Dict)					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     {}
								UpperCamelCase_     :	Dict					     =     {}
								if "subtask" in kwargs:
														UpperCamelCase_     :	int					     =     kwargs["""subtask"""]
														UpperCamelCase_     :	int					     =     kwargs["""subtask"""]
								if "threshold" in kwargs:
														UpperCamelCase_     :	List[str]					     =     kwargs["""threshold"""]
								if "mask_threshold" in kwargs:
														UpperCamelCase_     :	int					     =     kwargs["""mask_threshold"""]
								if "overlap_mask_area_threshold" in kwargs:
														UpperCamelCase_     :	str					     =     kwargs["""overlap_mask_area_threshold"""]

								return preprocess_kwargs, {}, postprocess_kwargs




		def __call__(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Any			,    **_SCREAMING_SNAKE_CASE					:str)					->			Union[Predictions, List[Prediction]]:






								'''simple docstring'''






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




		def 	_snake_case	(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:Tuple			,    _SCREAMING_SNAKE_CASE					:str=None)					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     load_image(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Optional[Any]					     =     [(image.height, image.width)]
								if self.model.config.__class__.__name__ == "OneFormerConfig":
														if subtask is None:
																				UpperCamelCase_     :	str					     =     {}
														else:
																				UpperCamelCase_     :	Optional[Any]					     =     {"""task_inputs""": [subtask]}
														UpperCamelCase_     :	Dict					     =     self.image_processor(images=[image]			,    return_tensors="""pt"""			,    **_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Optional[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_     :	List[Any]					     =     self.image_processor(images=[image]			,    return_tensors="""pt""")
								UpperCamelCase_     :	Optional[Any]					     =     target_size
								return inputs




		def 	_snake_case	(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:List[str])					->			Union[str, Any]:






								'''simple docstring'''






								UpperCamelCase_     :	str					     =     model_inputs.pop("""target_size""")
								UpperCamelCase_     :	List[str]					     =     self.model(**_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	List[Any]					     =     target_size
								return model_outputs





		def 	_snake_case	(  self					:str			,    _SCREAMING_SNAKE_CASE					:List[Any]			,    _SCREAMING_SNAKE_CASE					:str=None			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=0.9			,    _SCREAMING_SNAKE_CASE					:str=0.5			,    _SCREAMING_SNAKE_CASE					:Optional[int]=0.5)					->			str:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     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_     :	Dict					     =     self.image_processor.post_process_instance_segmentation

								if fn is not None:
														UpperCamelCase_     :	int					     =     fn(
														    _SCREAMING_SNAKE_CASE			,    threshold=_SCREAMING_SNAKE_CASE			,    mask_threshold=_SCREAMING_SNAKE_CASE			,    overlap_mask_area_threshold=_SCREAMING_SNAKE_CASE			,    target_sizes=model_outputs["""target_size"""]			,    )[0]

														UpperCamelCase_     :	Union[str, Any]					     =     []
														UpperCamelCase_     :	Tuple					     =     outputs["""segmentation"""]

														for segment in outputs["segments_info"]:
																				UpperCamelCase_     :	Optional[Any]					     =     (segmentation == segment["""id"""]) * 2_5_5
																				UpperCamelCase_     :	Dict					     =     Image.fromarray(mask.numpy().astype(np.uinta)			,    mode="""L""")
																				UpperCamelCase_     :	Any					     =     self.model.config.idalabel[segment["""label_id"""]]
																				UpperCamelCase_     :	Optional[Any]					     =     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_     :	List[str]					     =     self.image_processor.post_process_semantic_segmentation(
														    _SCREAMING_SNAKE_CASE			,    target_sizes=model_outputs["""target_size"""])[0]

														UpperCamelCase_     :	Tuple					     =     []
														UpperCamelCase_     :	Optional[Any]					     =     outputs.numpy()
														UpperCamelCase_     :	int					     =     np.unique(_SCREAMING_SNAKE_CASE)

														for label in labels:
																				UpperCamelCase_     :	Union[str, Any]					     =     (segmentation == label) * 2_5_5
																				UpperCamelCase_     :	List[Any]					     =     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






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						=      {
    "configuration_markuplm": ["MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig"],
    "feature_extraction_markuplm": ["MarkupLMFeatureExtractor"],
    "processing_markuplm": ["MarkupLMProcessor"],
    "tokenization_markuplm": ["MarkupLMTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      ["MarkupLMTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__SCREAMING_SNAKE_CASE						=      [
	    "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "MarkupLMForQuestionAnswering",
	    "MarkupLMForSequenceClassification",
	    "MarkupLMForTokenClassification",
	    "MarkupLMModel",
	    "MarkupLMPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
	from .feature_extraction_markuplm import MarkupLMFeatureExtractor
	from .processing_markuplm import MarkupLMProcessor
	from .tokenization_markuplm import MarkupLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_markuplm_fast import MarkupLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_markuplm import (
		    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MarkupLMForQuestionAnswering,
		    MarkupLMForSequenceClassification,
		    MarkupLMForTokenClassification,
		    MarkupLMModel,
		    MarkupLMPreTrainedModel,
		)


else:
	import sys

	__SCREAMING_SNAKE_CASE						=      _LazyModule(__name__, globals()["__file__"], _import_structure)




