




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     : int      =logging.get_logger(__name__)


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






@add_end_docstrings(_a		)
class       A      (		_a		):
     """simple docstring"""

     def __init__(     self	: Optional[int]						,	*A__	: int						,	**A__	: Tuple      )					->							Optional[int]:
           super().__init__(*A__						,	**A__      )

           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	: Tuple						,	**A__	: Any      )					->							Optional[Any]:
           lowerCamelCase__   	=					{}
           lowerCamelCase__   	=					{}
           if "subtask" in kwargs:
                 lowerCamelCase__   	=					kwargs['subtask']
                 lowerCamelCase__   	=					kwargs['subtask']
           if "threshold" in kwargs:
                 lowerCamelCase__   	=					kwargs['threshold']
           if "mask_threshold" in kwargs:
                 lowerCamelCase__   	=					kwargs['mask_threshold']
           if "overlap_mask_area_threshold" in kwargs:
                 lowerCamelCase__   	=					kwargs['overlap_mask_area_threshold']

           return preprocess_kwargs, {}, postprocess_kwargs




     def __call__(     self	: str						,	A__	: Dict						,	**A__	: str      )					->							List[str]:
           return super().__call__(A__						,	**A__      )




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




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: Union[str, Any]      )					->							int:
           lowerCamelCase__   	=					model_inputs.pop('target_size'      )
           lowerCamelCase__   	=					self.model(**A__      )
           lowerCamelCase__   	=					target_size
           return model_outputs




     def 			__snake_case (     self	: Tuple						,	A__	: Any						,	A__	: List[str]=None						,	A__	: List[str]=0.9						,	A__	: List[str]=0.5						,	A__	: str=0.5      )					->							List[str]:
           lowerCamelCase__   	=					None
           if subtask in {"panoptic", None} and hasattr(self.image_processor						,	'post_process_panoptic_segmentation'      ):
                 lowerCamelCase__   	=					self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor						,	'post_process_instance_segmentation'      ):
                 lowerCamelCase__   	=					self.image_processor.post_process_instance_segmentation

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

                 lowerCamelCase__   	=					[]
                 lowerCamelCase__   	=					outputs['segmentation']

                 for segment in outputs["segments_info"]:
                       lowerCamelCase__   	=					(segmentation == segment['id']) * 2_5_5
                       lowerCamelCase__   	=					Image.fromarray(mask.numpy().astype(np.uinta      )						,	mode='L'      )
                       lowerCamelCase__   	=					self.model.config.idalabel[segment['label_id']]
                       lowerCamelCase__   	=					segment['score']
                       annotation.append({'score': score, 'label': label, 'mask': mask}      )

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

                 lowerCamelCase__   	=					[]
                 lowerCamelCase__   	=					outputs.numpy()
                 lowerCamelCase__   	=					np.unique(A__      )

                 for label in labels:
                       lowerCamelCase__   	=					(segmentation == label) * 2_5_5
                       lowerCamelCase__   	=					Image.fromarray(mask.astype(np.uinta      )						,	mode='L'      )
                       lowerCamelCase__   	=					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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase				    =      {
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BartForCausalLM""",
          """BartForConditionalGeneration""",
          """BartForQuestionAnswering""",
          """BartForSequenceClassification""",
          """BartModel""",
          """BartPretrainedModel""",
          """PretrainedBartModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """TFBartForConditionalGeneration""",
          """TFBartForSequenceClassification""",
          """TFBartModel""",
          """TFBartPretrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase				    =      [
          """FlaxBartDecoderPreTrainedModel""",
          """FlaxBartForCausalLM""",
          """FlaxBartForConditionalGeneration""",
          """FlaxBartForQuestionAnswering""",
          """FlaxBartForSequenceClassification""",
          """FlaxBartModel""",
          """FlaxBartPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
      from .tokenization_bart import BartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bart_fast import BartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bart import (
                BART_PRETRAINED_MODEL_ARCHIVE_LIST,
                BartForCausalLM,
                BartForConditionalGeneration,
                BartForQuestionAnswering,
                BartForSequenceClassification,
                BartModel,
                BartPretrainedModel,
                PretrainedBartModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bart import (
                TFBartForConditionalGeneration,
                TFBartForSequenceClassification,
                TFBartModel,
                TFBartPretrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bart import (
                FlaxBartDecoderPreTrainedModel,
                FlaxBartForCausalLM,
                FlaxBartForConditionalGeneration,
                FlaxBartForQuestionAnswering,
                FlaxBartForSequenceClassification,
                FlaxBartModel,
                FlaxBartPreTrainedModel,
            )

else:
      import sys

      __lowercase				    =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
