import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
    import torch_xla.core.xla_model as xm  # noqa: F401

    _lowerCAmelCase        =       True
except ImportError:
    _lowerCAmelCase        =       False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_lowerCAmelCase        =       torch.distributed.is_available()


def       a__      (					a							)		->	Optional[Any]:
       # Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
       A_							:       str	      =				importlib.util.find_spec(a							) is not None
       if package_exists:
              try:
                     A_							:       str	      =				importlib.metadata.metadata(a							)
                     return True
              except importlib.metadata.PackageNotFoundError:
                     return False


def       a__      (					)		->	bool:
       return _torch_distributed_available


def       a__      (					)		->	int:
       try:
              pass
       except ImportError:
              print(
                  '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
                  ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
                  ''' Bindings for PyTorch*.'''							)
       return (
           importlib.util.find_spec('''torch_ccl'''							) is not None
           or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''							) is not None
       )


def       a__      (					)		->	List[str]:
       return importlib.metadata.version('''oneccl_bind_pt'''							)


def       a__      (					)		->	List[str]:
       return _is_package_available('''transformer_engine'''							)


@lru_cache
def       a__      (					a=True							)		->	Union[str, Any]:
       # Due to bugs on the amp series GPUs, we disable torch-xla on them
       if torch.cuda.is_available():
              return False
       if _tpu_available and check_device:
              try:
                     # Will raise a RuntimeError if no XLA configuration is found
                     A_							:       Tuple	      =				xm.xla_device()
                     return True
              except RuntimeError:
                     return False
       return _tpu_available


def       a__      (					)		->	Dict:
       return _is_package_available('''deepspeed'''							)


def       a__      (					a=False							)		->	Optional[int]:
       if is_tpu_available():
              return not ignore_tpu
       if torch.cuda.is_available():
              return torch.cuda.is_bfaa_supported()
       if is_npu_available():
              return False
       return True


def       a__      (					)		->	Optional[Any]:
       A_							:       Optional[int]	      =				_is_package_available('''bitsandbytes'''							)
       if package_exists:
              A_							:       Any	      =				version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
              return compare_versions(a  ,   '''>='''  ,   '''0.39.0'''							)
       return False


def       a__      (					)		->	Any:
       A_							:       List[str]	      =				_is_package_available('''bitsandbytes'''							)
       if package_exists:
              A_							:       Tuple	      =				version.parse(importlib.metadata.version('''bitsandbytes'''							)							)
              return compare_versions(a  ,   '''>='''  ,   '''0.37.2'''							)
       return False


def       a__      (					)		->	List[str]:
       return _is_package_available('''bitsandbytes'''							)


def       a__      (					)		->	List[str]:
       if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''  ,   '''False'''							)							) == 1:
              A_							:       str	      =				importlib.util.find_spec('''megatron'''							) is not None
              if package_exists:
                     try:
                            A_							:       Dict	      =				parse(importlib.metadata.version('''megatron-lm'''							)							)
                            return compare_versions(a  ,   '''>='''  ,   '''2.2.0'''							)
                     except Exception as e:
                            warnings.warn(f"""Parse Megatron version failed. Exception:{e}"""							)
                            return False


def       a__      (					)		->	Tuple:
       return _is_package_available('''safetensors'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''transformers'''							)


def       a__      (					)		->	int:
       return _is_package_available('''datasets'''							)


def       a__      (					)		->	Optional[int]:
       return _is_package_available('''aim'''							)


def       a__      (					)		->	Optional[Any]:
       return _is_package_available('''tensorboard'''							) or _is_package_available('''tensorboardX'''							)


def       a__      (					)		->	Optional[int]:
       return _is_package_available('''wandb'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''comet_ml'''							)


def       a__      (					)		->	Tuple:
       return _is_package_available('''boto3'''							)


def       a__      (					)		->	Tuple:
       if _is_package_available('''rich'''							):
              if "ACCELERATE_DISABLE_RICH" in os.environ:
                     warnings.warn(
                         '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''							)
                     return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''  ,   a							)
              return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''  ,   a							)
       return False


def       a__      (					)		->	Any:
       return _is_package_available('''sagemaker'''							)


def       a__      (					)		->	Union[str, Any]:
       return _is_package_available('''tqdm'''							)


def       a__      (					)		->	List[str]:
       return _is_package_available('''mlflow'''							)


def       a__      (					)		->	Tuple:
       return is_torch_version('''>='''  ,   '''1.12'''							) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def       a__      (					)		->	Tuple:
       def get_major_and_minor_from_version(a							):
              return str(version.parse(a							).major							) + "." + str(version.parse(a							).minor							)

       A_							:       Tuple	      =				importlib.metadata.version('''torch'''							)
       if importlib.util.find_spec('''intel_extension_for_pytorch'''							) is None:
              return False
       A_							:       Optional[int]	      =				'''N/A'''
       try:
              A_							:       List[Any]	      =				importlib.metadata.version('''intel_extension_for_pytorch'''							)
       except importlib.metadata.PackageNotFoundError:
              return False
       A_							:       Tuple	      =				get_major_and_minor_from_version(_torch_version							)
       A_							:       int	      =				get_major_and_minor_from_version(_ipex_version							)
       if torch_major_and_minor != ipex_major_and_minor:
              warnings.warn(
                  f"""Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"""
                  f""" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."""							)
              return False
       return True


@lru_cache
def       a__      (					a=False							)		->	Optional[int]:
       if importlib.util.find_spec('''torch'''							) is None or importlib.util.find_spec('''torch_npu'''							) is None:
              return False

       import torch
       import torch_npu  # noqa: F401

       if check_device:
              try:
                     # Will raise a RuntimeError if no NPU is found
                     A_							:       Dict	      =				torch.npu.device_count()
                     return torch.npu.is_available()
              except RuntimeError:
                     return False
       return hasattr(a  ,   '''npu'''							) and torch.npu.is_available()


@lru_cache
def       a__      (					a=False							)		->	Optional[Any]:
       if not parse_flag_from_env('''ACCELERATE_USE_XPU'''  ,   default=a							):
              return False
       if is_ipex_available():
              import torch

              if is_torch_version('''<='''  ,   '''1.12'''							):
                     return False
       else:
              return False

       import intel_extension_for_pytorch  # noqa: F401

       if check_device:
              try:
                     # Will raise a RuntimeError if no XPU  is found
                     A_							:       Tuple	      =				torch.xpu.device_count()
                     return torch.xpu.is_available()
              except RuntimeError:
                     return False
       return hasattr(a  ,   '''xpu'''							) and torch.xpu.is_available()



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(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        super().__init__(*__magic_name__ ,       **__magic_name__      )

        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 ,       **__magic_name__      ):

        """simple docstring"""

        A_							:       str	      =				{}
        A_							:       List[Any]	      =				{}
        if "subtask" in kwargs:
               A_							:       Optional[int]	      =				kwargs['''subtask''']
               A_							:       Dict	      =				kwargs['''subtask''']
        if "threshold" in kwargs:
               A_							:       Tuple	      =				kwargs['''threshold''']
        if "mask_threshold" in kwargs:
               A_							:       int	      =				kwargs['''mask_threshold''']
        if "overlap_mask_area_threshold" in kwargs:
               A_							:       Optional[Any]	      =				kwargs['''overlap_mask_area_threshold''']

        return preprocess_kwargs, {}, postprocess_kwargs
 def __call__(    self ,       __magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        return super().__call__(__magic_name__ ,       **__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

        """simple docstring"""

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

        """simple docstring"""

        A_							:       Dict	      =				model_inputs.pop('''target_size'''      )
        A_							:       Union[str, Any]	      =				self.model(**__magic_name__      )
        A_							:       Any	      =				target_size
        return model_outputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=0.9 ,       __magic_name__=0.5 ,       __magic_name__=0.5      ):

        """simple docstring"""

        A_							:       Tuple	      =				None
        if subtask in {"panoptic", None} and hasattr(self.image_processor ,       '''post_process_panoptic_segmentation'''      ):
               A_							:       Dict	      =				self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor ,       '''post_process_instance_segmentation'''      ):
               A_							:       Tuple	      =				self.image_processor.post_process_instance_segmentation

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

               A_							:       Optional[Any]	      =				[]
               A_							:       Optional[int]	      =				outputs['''segmentation''']

               for segment in outputs["segments_info"]:
                      A_							:       Optional[int]	      =				(segmentation == segment['''id''']) * 255
                      A_							:       int	      =				Image.fromarray(mask.numpy().astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       Union[str, Any]	      =				self.model.config.idalabel[segment['''label_id''']]
                      A_							:       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'''      ):
               A_							:       int	      =				self.image_processor.post_process_semantic_segmentation(
                   __magic_name__ ,       target_sizes=model_outputs['''target_size''']      )[0]

               A_							:       Tuple	      =				[]
               A_							:       Any	      =				outputs.numpy()
               A_							:       Optional[Any]	      =				np.unique(__magic_name__      )

               for label in labels:
                      A_							:       Any	      =				(segmentation == label) * 255
                      A_							:       str	      =				Image.fromarray(mask.astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       List[str]	      =				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


