





"""simple docstring"""




import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


lowerCamelCase_	:		Union[str, Any]         =   logging.get_logger(__name__)

if is_tf_available():
   import tensorflow as tf

@dataclass
class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					List[Any]             =  """tf"""
      lowercase_ :					Optional[str]             =  field(
          default=UpperCAmelCase__     ,  metadata={"""help""": """Name of TPU"""}     ,  )

      lowercase_ :					Optional[str]             =  field(
          default=UpperCAmelCase__     ,  metadata={"""help""": """Zone of TPU"""}     ,  )

      lowercase_ :					Optional[str]             =  field(
          default=UpperCAmelCase__     ,  metadata={"""help""": """Name of Cloud TPU-enabled project"""}     ,  )

      lowercase_ :					float             =  field(
          default=1.0     ,  metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}     ,  )

      lowercase_ :					bool             =  field(default=UpperCAmelCase__     ,  metadata={"""help""": """Whether to activate the XLA compilation or not"""}						)

      @cached_property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        requires_backends(self   , ['tf']    )
        logger.info('Tensorflow: setting up strategy'    )

        A_    :			Optional[Any]									=    tf.config.list_physical_devices('GPU'    )

        # Set to float16 at first
        if self.fpaa:
          tf.keras.mixed_precision.set_global_policy('mixed_float16'    )

        if self.no_cuda:
          A_    :			Union[str, Any]									=    tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
        else:
          try:
            if self.tpu_name:
              A_    :			int									=    tf.distribute.cluster_resolver.TPUClusterResolver(
                  self.tpu_name   , zone=self.tpu_zone   , project=self.gcp_project    )
            else:
              A_    :			Union[str, Any]									=    tf.distribute.cluster_resolver.TPUClusterResolver()
          except ValueError:
            if self.tpu_name:
              raise RuntimeError(F"""Couldn't connect to TPU {self.tpu_name}!"""    )
            else:
              A_    :			List[Any]									=    None

          if tpu:
            # Set to bfloat16 in case of TPU
            if self.fpaa:
              tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'    )

            tf.config.experimental_connect_to_cluster(snake_case_    )
            tf.tpu.experimental.initialize_tpu_system(snake_case_    )

            A_    :			int									=    tf.distribute.TPUStrategy(snake_case_    )

          elif len(snake_case_    ) == 0:
            A_    :			List[str]									=    tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
          elif len(snake_case_    ) == 1:
            A_    :			List[str]									=    tf.distribute.OneDeviceStrategy(device='/gpu:0'    )
          elif len(snake_case_    ) > 1:
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            A_    :			List[str]									=    tf.distribute.MirroredStrategy()
          else:
            raise ValueError('Cannot find the proper strategy, please check your environment properties.'    )

        return strategy

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        requires_backends(self   , ['tf']    )
        return self._setup_strategy

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        requires_backends(self   , ['tf']    )
        return self._setup_strategy.num_replicas_in_sync

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return False  # TF Logging is handled by Keras not the Trainer

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.per_gpu_train_batch_size:
          logger.warning(
              'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
              'version. Using `--per_device_train_batch_size` is preferred.'    )
        A_    :			Any									=    self.per_gpu_train_batch_size or self.per_device_train_batch_size
        return per_device_batch_size * self.n_replicas

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.per_gpu_eval_batch_size:
          logger.warning(
              'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
              'version. Using `--per_device_eval_batch_size` is preferred.'    )
        A_    :			List[str]									=    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
        return per_device_batch_size * self.n_replicas

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        requires_backends(self   , ['tf']    )
        warnings.warn(
            'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'   , snake_case_   , )
        return self._setup_strategy.num_replicas_in_sync
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


