






'''simple docstring'''


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_				      =							logging.get_logger(__name__)


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




@add_end_docstrings(lowerCamelCase__   )
class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   def __init__( self   ,   *_A   ,   **_A			)    ->   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    __magic_name__    ( self   ,   **_A			)    ->   Tuple:
    __a    :	str									=						{}
    __a    :	str									=						{}
    if "subtask" in kwargs:
     __a    :	Tuple									=						kwargs['subtask']
     __a    :	List[str]									=						kwargs['subtask']
    if "threshold" in kwargs:
     __a    :	Tuple									=						kwargs['threshold']
    if "mask_threshold" in kwargs:
     __a    :	Any									=						kwargs['mask_threshold']
    if "overlap_mask_area_threshold" in kwargs:
     __a    :	Optional[int]									=						kwargs['overlap_mask_area_threshold']

    return preprocess_kwargs, {}, postprocess_kwargs



   def __call__( self   ,   _A   ,   **_A			)    ->   Union[Predictions, List[Prediction]]:
    return super().__call__(_A   ,   **_A			)



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



   def    __magic_name__    ( self   ,   _A			)    ->   int:
    __a    :	Any									=						model_inputs.pop('target_size'			)
    __a    :	List[Any]									=						self.model(**_A			)
    __a    :	str									=						target_size
    return model_outputs



   def    __magic_name__    ( self   ,   _A   ,   _A=None   ,   _A=0.9   ,   _A=0.5   ,   _A=0.5			)    ->   Dict:
    __a    :	List[Any]									=						None
    if subtask in {"panoptic", None} and hasattr(self.image_processor   ,   'post_process_panoptic_segmentation'			):
     __a    :	int									=						self.image_processor.post_process_panoptic_segmentation
    elif subtask in {"instance", None} and hasattr(self.image_processor   ,   'post_process_instance_segmentation'			):
     __a    :	Optional[int]									=						self.image_processor.post_process_instance_segmentation

    if fn is not None:
     __a    :	List[Any]									=						fn(
         _A   ,   threshold=_A   ,   mask_threshold=_A   ,   overlap_mask_area_threshold=_A   ,   target_sizes=model_outputs['target_size']   ,   )[0]

     __a    :	List[str]									=						[]
     __a    :	List[Any]									=						outputs['segmentation']

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

     __a    :	str									=						[]
     __a    :	Any									=						outputs.numpy()
     __a    :	Any									=						np.unique(_A			)

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












'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							{
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   _A						=   'squeezebert'



   def __init__( self   ,   _A=30522   ,   _A=768   ,   _A=12   ,   _A=12   ,   _A=3072   ,   _A="gelu"   ,   _A=0.1   ,   _A=0.1   ,   _A=512   ,   _A=2   ,   _A=0.02   ,   _A=1E-1_2   ,   _A=0   ,   _A=768   ,   _A=4   ,   _A=4   ,   _A=4   ,   _A=1   ,   _A=4   ,   _A=4   ,   **_A   ,   )    ->   List[Any]:
    super().__init__(pad_token_id=_A   ,   **_A			)

    __a    :	Optional[int]									=						vocab_size
    __a    :	Optional[int]									=						hidden_size
    __a    :	int									=						num_hidden_layers
    __a    :	int									=						num_attention_heads
    __a    :	Tuple									=						hidden_act
    __a    :	Optional[Any]									=						intermediate_size
    __a    :	Any									=						hidden_dropout_prob
    __a    :	List[Any]									=						attention_probs_dropout_prob
    __a    :	List[str]									=						max_position_embeddings
    __a    :	Tuple									=						type_vocab_size
    __a    :	Tuple									=						initializer_range
    __a    :	List[str]									=						layer_norm_eps
    __a    :	str									=						embedding_size
    __a    :	Any									=						q_groups
    __a    :	Any									=						k_groups
    __a    :	Union[str, Any]									=						v_groups
    __a    :	List[str]									=						post_attention_groups
    __a    :	Dict									=						intermediate_groups
    __a    :	Optional[Any]									=						output_groups




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
     __a    :	Tuple									=						{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
     __a    :	List[Any]									=						{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
            ('token_type_ids', dynamic_axis),
        ]			)




