

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 						a_			(				A__	):



      """simple docstring"""







      def __init__( self	:				List[Any]     ,*snake_case	:				Optional[int]     ,**snake_case	:				int							):
           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 			_lowerCAmelCase	( self	:				int     ,**snake_case	:				Optional[int]							):
           SCREAMING_SNAKE_CASE 		={}
           SCREAMING_SNAKE_CASE 		={}
           if "subtask" in kwargs:
                SCREAMING_SNAKE_CASE 		=kwargs['''subtask''']
                SCREAMING_SNAKE_CASE 		=kwargs['''subtask''']
           if "threshold" in kwargs:
                SCREAMING_SNAKE_CASE 		=kwargs['''threshold''']
           if "mask_threshold" in kwargs:
                SCREAMING_SNAKE_CASE 		=kwargs['''mask_threshold''']
           if "overlap_mask_area_threshold" in kwargs:
                SCREAMING_SNAKE_CASE 		=kwargs['''overlap_mask_area_threshold''']

           return preprocess_kwargs, {}, postprocess_kwargs
      def __call__( self	:				Tuple     ,snake_case	:				str     ,**snake_case	:				Optional[Any]							):
           return super().__call__(snake_case     ,**snake_case							)
      def 			_lowerCAmelCase	( self	:				Optional[int]     ,snake_case	:				Tuple     ,snake_case	:				Tuple=None							):
           SCREAMING_SNAKE_CASE 		=load_image(snake_case							)
           SCREAMING_SNAKE_CASE 		=[(image.height, image.width)]
           if self.model.config.__class__.__name__ == "OneFormerConfig":
                if subtask is None:
                     SCREAMING_SNAKE_CASE 		={}
                else:
                     SCREAMING_SNAKE_CASE 		={'''task_inputs''': [subtask]}
                SCREAMING_SNAKE_CASE 		=self.image_processor(images=[image]     ,return_tensors='pt'     ,**snake_case							)
                SCREAMING_SNAKE_CASE 		=self.tokenizer(
                    inputs['task_inputs']     ,padding='max_length'     ,max_length=self.model.config.task_seq_len     ,return_tensors=self.framework     ,)['''input_ids''']
           else:
                SCREAMING_SNAKE_CASE 		=self.image_processor(images=[image]     ,return_tensors='pt'							)
           SCREAMING_SNAKE_CASE 		=target_size
           return inputs
      def 			_lowerCAmelCase	( self	:				List[Any]     ,snake_case	:				Union[str, Any]							):
           SCREAMING_SNAKE_CASE 		=model_inputs.pop('target_size'							)
           SCREAMING_SNAKE_CASE 		=self.model(**snake_case							)
           SCREAMING_SNAKE_CASE 		=target_size
           return model_outputs


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

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

                SCREAMING_SNAKE_CASE 		=[]
                SCREAMING_SNAKE_CASE 		=outputs['''segmentation''']

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

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

                SCREAMING_SNAKE_CASE 		=[]
                SCREAMING_SNAKE_CASE 		=outputs.numpy()
                SCREAMING_SNAKE_CASE 		=np.unique(snake_case							)

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


