


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,
   )


UpperCamelCase__				       =			logging.get_logger(__name__)


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

@add_end_docstrings(A_		)
class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     def __init__( self       :       Dict	,      *UpperCamelCase       :       Any	,      **UpperCamelCase       :       Optional[Any]		):





            """simple docstring"""



            super().__init__(*UpperCamelCase	,      **UpperCamelCase		)

            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       :       Tuple	,      **UpperCamelCase       :       str		):





            """simple docstring"""



            _lowercase					:				Union[str, Any]           =			{}
            _lowercase					:				Dict           =			{}
            if "subtask" in kwargs:
                   _lowercase					:				Optional[Any]           =			kwargs['''subtask''']
                   _lowercase					:				Dict           =			kwargs['''subtask''']
            if "threshold" in kwargs:
                   _lowercase					:				Optional[Any]           =			kwargs['''threshold''']
            if "mask_threshold" in kwargs:
                   _lowercase					:				Dict           =			kwargs['''mask_threshold''']
            if "overlap_mask_area_threshold" in kwargs:
                   _lowercase					:				Any           =			kwargs['''overlap_mask_area_threshold''']

            return preprocess_kwargs, {}, postprocess_kwargs




     def __call__( self       :       Optional[int]	,      UpperCamelCase       :       Tuple	,      **UpperCamelCase       :       Union[str, Any]		):





            """simple docstring"""



            return super().__call__(UpperCamelCase	,      **UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       Tuple=None		):





            """simple docstring"""



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




     def   lowerCAmelCase_   ( self       :       Union[str, Any]	,      UpperCamelCase       :       Dict		):





            """simple docstring"""



            _lowercase					:				Any           =			model_inputs.pop('''target_size'''		)
            _lowercase					:				List[str]           =			self.model(**UpperCamelCase		)
            _lowercase					:				Union[str, Any]           =			target_size
            return model_outputs




     def   lowerCAmelCase_   ( self       :       List[Any]	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       str=None	,      UpperCamelCase       :       str=0.9	,      UpperCamelCase       :       Dict=0.5	,      UpperCamelCase       :       int=0.5		):





            """simple docstring"""



            _lowercase					:				str           =			None
            if subtask in {"panoptic", None} and hasattr(self.image_processor	,      '''post_process_panoptic_segmentation'''		):
                   _lowercase					:				Any           =			self.image_processor.post_process_panoptic_segmentation
            elif subtask in {"instance", None} and hasattr(self.image_processor	,      '''post_process_instance_segmentation'''		):
                   _lowercase					:				Optional[Any]           =			self.image_processor.post_process_instance_segmentation

            if fn is not None:
                   _lowercase					:				Optional[int]           =			fn(
                       UpperCamelCase	,      threshold=UpperCamelCase	,      mask_threshold=UpperCamelCase	,      overlap_mask_area_threshold=UpperCamelCase	,      target_sizes=model_outputs['''target_size''']	,      )[0]

                   _lowercase					:				str           =			[]
                   _lowercase					:				Union[str, Any]           =			outputs['''segmentation''']

                   for segment in outputs["segments_info"]:
                          _lowercase					:				List[str]           =			(segmentation == segment['''id''']) * 2_55
                          _lowercase					:				Optional[int]           =			Image.fromarray(mask.numpy().astype(np.uinta		)	,      mode='''L'''		)
                          _lowercase					:				int           =			self.model.config.idalabel[segment['''label_id''']]
                          _lowercase					:				str           =			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					:				int           =			self.image_processor.post_process_semantic_segmentation(
                       UpperCamelCase	,      target_sizes=model_outputs['''target_size''']		)[0]

                   _lowercase					:				int           =			[]
                   _lowercase					:				Tuple           =			outputs.numpy()
                   _lowercase					:				Optional[int]           =			np.unique(UpperCamelCase		)

                   for label in labels:
                          _lowercase					:				List[str]           =			(segmentation == label) * 2_55
                          _lowercase					:				Optional[Any]           =			Image.fromarray(mask.astype(np.uinta		)	,      mode='''L'''		)
                          _lowercase					:				Tuple           =			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 torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 						UpperCAmelCase__   (      A_      ,						A_      ,						A_		):



     '''simple docstring'''




     @register_to_config
     def __init__( self       :       Dict	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       float	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       bool = False	,      ):





            """simple docstring"""



            super().__init__()

            _lowercase					:				Any           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      bias=UpperCamelCase		)

            _lowercase					:				List[Any]           =			nn.Embedding(UpperCamelCase	,      UpperCamelCase		)
            _lowercase					:				Any           =			False

            _lowercase					:				Tuple           =			nn.Dropout(p=UpperCamelCase		)

            _lowercase					:				Any           =			TaConfig(
                d_model=UpperCamelCase	,      num_heads=UpperCamelCase	,      d_kv=UpperCamelCase	,      d_ff=UpperCamelCase	,      feed_forward_proj=UpperCamelCase	,      dropout_rate=UpperCamelCase	,      is_decoder=UpperCamelCase	,      is_encoder_decoder=UpperCamelCase	,      )
            _lowercase					:				Any           =			nn.ModuleList()
            for lyr_num in range(UpperCamelCase		):
                   _lowercase					:				Tuple           =			TaBlock(UpperCamelCase		)
                   self.encoders.append(UpperCamelCase		)

            _lowercase					:				List[Any]           =			TaLayerNorm(UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			nn.Dropout(p=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[Any]		):





            """simple docstring"""



            _lowercase					:				Optional[int]           =			self.input_proj(UpperCamelCase		)

            # terminal relative positional encodings
            _lowercase					:				Optional[int]           =			encoder_inputs.shape[1]
            _lowercase					:				Optional[int]           =			torch.arange(UpperCamelCase	,      device=encoder_inputs.device		)

            _lowercase					:				Any           =			encoder_inputs_mask.sum(-1		)
            _lowercase					:				int           =			torch.roll(input_positions.unsqueeze(0		)	,      tuple(seq_lens.tolist()		)	,      dims=0		)
            x += self.position_encoding(UpperCamelCase		)

            _lowercase					:				Optional[int]           =			self.dropout_pre(UpperCamelCase		)

            # inverted the attention mask
            _lowercase					:				Optional[int]           =			encoder_inputs.size()
            _lowercase					:				List[Any]           =			self.get_extended_attention_mask(UpperCamelCase	,      UpperCamelCase		)

            for lyr in self.encoders:
                   _lowercase					:				Optional[int]           =			lyr(UpperCamelCase	,      UpperCamelCase		)[0]
            _lowercase					:				List[str]           =			self.layer_norm(UpperCamelCase		)

            return self.dropout_post(UpperCamelCase		), encoder_inputs_mask