



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,
		)


__magic_name__       =     logging.get_logger(__name__)


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


@add_end_docstrings(_snake_case       )
class 	a__       (       _snake_case       ):


					"""simple docstring"""



					def __init__(		self     :Tuple      ,  *lowercase__     :Any      ,  **lowercase__     :Optional[int]    ):
										super().__init__(*lowercase__      ,  **lowercase__    )

										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 				__UpperCAmelCase		(		self     :Any      ,  **lowercase__     :List[Any]    ):
										lowercase						   =       {}
										lowercase						   =       {}
										if "subtask" in kwargs:
															lowercase						   =       kwargs['subtask']
															lowercase						   =       kwargs['subtask']
										if "threshold" in kwargs:
															lowercase						   =       kwargs['threshold']
										if "mask_threshold" in kwargs:
															lowercase						   =       kwargs['mask_threshold']
										if "overlap_mask_area_threshold" in kwargs:
															lowercase						   =       kwargs['overlap_mask_area_threshold']

										return preprocess_kwargs, {}, postprocess_kwargs

					def __call__(		self     :Union[str, Any]      ,  lowercase__     :int      ,  **lowercase__     :List[str]    ):
										return super().__call__(lowercase__      ,  **lowercase__    )

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

					def 				__UpperCAmelCase		(		self     :Optional[Any]      ,  lowercase__     :Tuple    ):
										lowercase						   =       model_inputs.pop('target_size'    )
										lowercase						   =       self.model(**lowercase__    )
										lowercase						   =       target_size
										return model_outputs



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

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

															lowercase						   =       []
															lowercase						   =       outputs['segmentation']

															for segment in outputs["segments_info"]:
																				lowercase						   =       (segmentation == segment['id']) * 255
																				lowercase						   =       Image.fromarray(mask.numpy().astype(np.uinta    )      ,  mode='L'    )
																				lowercase						   =       self.model.config.idalabel[segment['label_id']]
																				lowercase						   =       segment['score']
																				annotation.append({'score': score, 'label': label, 'mask': mask}    )

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

															lowercase						   =       []
															lowercase						   =       outputs.numpy()
															lowercase						   =       np.unique(lowercase__    )

															for label in labels:
																				lowercase						   =       (segmentation == label) * 255
																				lowercase						   =       Image.fromarray(mask.astype(np.uinta    )      ,  mode='L'    )
																				lowercase						   =       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 packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
		from .testing_utils import (
		    floats_tensor,
		    load_hf_numpy,
		    load_image,
		    load_numpy,
		    load_pt,
		    nightly,
		    parse_flag_from_env,
		    print_tensor_test,
		    require_torch_a,
		    require_torch_gpu,
		    skip_mps,
		    slow,
		    torch_all_close,
		    torch_device,
		)
		from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


__magic_name__       =     get_logger(__name__)





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if version.parse(_UpperCAmelCase				) < version.parse(_UpperCAmelCase				):
										if "dev" in min_version:
															lowercase						   =       (
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															lowercase						   =       f"""This example requires a minimum version of {min_version},"""
										error_message += f""" but the version found is {__version__}.\n"""
										raise ImportError(_UpperCAmelCase				)






