


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,
					)


A  :			Optional[Any]     				=				logging.get_logger(__name__)


A  :			int     				=				Dict[str, Any]
A  :			str     				=				List[Prediction]







@add_end_docstrings(A_    )
class     __A(				A_    ):




				def __init__(  self	,  *_snake_case	,  **_snake_case       )					->   Optional[int]:


								'''simple docstring'''




								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        SCREAMING_SNAKE_CASE_	(  self	,  **_snake_case       )					->   int:


								'''simple docstring'''




								__a    							=					{}
								__a    							=					{}
								if "subtask" in kwargs:
												__a    							=					kwargs['''subtask''']
												__a    							=					kwargs['''subtask''']
								if "threshold" in kwargs:
												__a    							=					kwargs['''threshold''']
								if "mask_threshold" in kwargs:
												__a    							=					kwargs['''mask_threshold''']
								if "overlap_mask_area_threshold" in kwargs:
												__a    							=					kwargs['''overlap_mask_area_threshold''']

								return preprocess_kwargs, {}, postprocess_kwargs




				def __call__(  self	,  _snake_case	,  **_snake_case       )					->   Any:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case=None       )					->   str:


								'''simple docstring'''




								__a    							=					load_image(_snake_case       )
								__a    							=					[(image.height, image.width)]
								if self.model.config.__class__.__name__ == "OneFormerConfig":
												if subtask is None:
																__a    							=					{}
												else:
																__a    							=					{'''task_inputs''': [subtask]}
												__a    							=					self.image_processor(images=[image]	,  return_tensors='''pt'''	,  **_snake_case       )
												__a    							=					self.tokenizer(
												    inputs['''task_inputs''']	,  padding='''max_length'''	,  max_length=self.model.config.task_seq_len	,  return_tensors=self.framework	,  )['''input_ids''']
								else:
												__a    							=					self.image_processor(images=[image]	,  return_tensors='''pt'''       )
								__a    							=					target_size
								return inputs




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   int:


								'''simple docstring'''




								__a    							=					model_inputs.pop('''target_size'''       )
								__a    							=					self.model(**_snake_case       )
								__a    							=					target_size
								return model_outputs




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case=None	,  _snake_case=0.9	,  _snake_case=0.5	,  _snake_case=0.5       )					->   str:


								'''simple docstring'''




								__a    							=					None
								if subtask in {"panoptic", None} and hasattr(self.image_processor	,  '''post_process_panoptic_segmentation'''       ):
												__a    							=					self.image_processor.post_process_panoptic_segmentation
								elif subtask in {"instance", None} and hasattr(self.image_processor	,  '''post_process_instance_segmentation'''       ):
												__a    							=					self.image_processor.post_process_instance_segmentation

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

												__a    							=					[]
												__a    							=					outputs['''segmentation''']

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

												__a    							=					[]
												__a    							=					outputs.numpy()
												__a    							=					np.unique(_snake_case       )

												for label in labels:
																__a    							=					(segmentation == label) * 255
																__a    							=					Image.fromarray(mask.astype(np.uinta       )	,  mode='''L'''       )
																__a    							=					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 math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


A  :			List[str]     				=				logging.get_logger(__name__)







class     __A(				nn.Module    ):




				def __init__(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								super().__init__()
								if version.parse(torch.__version__       ) < version.parse('''1.12.0'''       ):
												raise ImportError(
												    F"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
												    '''PytorchGELUTanh. Please upgrade torch.'''       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.gelu(_snake_case	,  approximate='''tanh'''       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_4715 * torch.pow(_snake_case	,  3.0       ))       ))







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case = False       )					->   Tuple:


								'''simple docstring'''




								super().__init__()
								if use_gelu_python:
												__a    							=					self._gelu_python
								else:
												__a    							=					nn.functional.gelu




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0       )       ))




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.sigmoid(1.702 * input       )







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case	,  _snake_case       )					->   Optional[int]:


								'''simple docstring'''




								if min > max:
												raise ValueError(F"""min should be < max (got min: {min}, max: {max})"""       )

								super().__init__()
								__a    							=					min
								__a    							=					max




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return torch.clip(gelu(_snake_case       )	,  self.min	,  self.max       )







class     __A(				nn.Module    ):




				def __init__(  self       )					->   str:


								'''simple docstring'''




								super().__init__()
								__a    							=					math.sqrt(2 / math.pi       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(_snake_case	,  3       ))       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.silu(_snake_case       )







class     __A(				nn.Module    ):




				def __init__(  self       )					->   List[Any]:


								'''simple docstring'''




								super().__init__()
								if version.parse(torch.__version__       ) < version.parse('''1.9.0'''       ):
												__a    							=					self._mish_python
								else:
												__a    							=					nn.functional.mish




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.tanh(nn.functional.softplus(_snake_case       )       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case=0.70_7107	,  _snake_case=0.28_2095       )					->   int:


								'''simple docstring'''




								__a    							=					(input - mu).div(sigma * math.sqrt(2.0       )       )
								return 0.5 * (1.0 + torch.erf(_snake_case       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					nn.functional.relu(_snake_case       )
								__a    							=					torch.square(_snake_case       )
								return squared







class     __A(				a    ):




				def __getitem__(  self	,  _snake_case       )					->   Dict:


								'''simple docstring'''




								__a    							=					super().__getitem__(_snake_case       )
								__a							,  __a    							=					content if isinstance(_snake_case	,  _snake_case       ) else (content, {})
								return cls(**_snake_case       )



A  :			str     				=				{
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -1_0, 'max': 1_0}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
A  :			Any     				=				ClassInstantier(ACTaCLS)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"""						)


# For backwards compatibility with: from activations import gelu_python
A  :			Optional[Any]     				=				get_activation('gelu_python')
A  :			str     				=				get_activation('gelu_new')
A  :			Any     				=				get_activation('gelu')
A  :			int     				=				get_activation('gelu_fast')
A  :			Any     				=				get_activation('quick_gelu')
A  :			int     				=				get_activation('silu')
A  :			List[str]     				=				get_activation('mish')






A  :			Dict     				=				get_activation('linear')