




from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)

class     __a(	_a  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=0    )					->						Optional[Any]:
         UpperCAmelCase_     :				str  =     1.0 if scale is None else scale
         UpperCAmelCase_     :				Any  =     0.0 if loc is None else loc

         super().__init__(_SCREAMING_SNAKE_CASE  ,[AffineTransform(loc=self.loc  ,scale=self.scale  ,event_dim=_SCREAMING_SNAKE_CASE    )]    )





    @property
    def 				a__ ( self    )					->						Any:
         return self.base_dist.mean * self.scale + self.loc





    @property
    def 				a__ ( self    )					->						Optional[Any]:
         return self.base_dist.variance * self.scale**2





    @property
    def 				a__ ( self    )					->						int:
         return self.variance.sqrt()


class     __a(	nn.Module  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						None:
         super().__init__(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[Any]  =     args_dim
         UpperCAmelCase_     :				int  =     nn.ModuleList([nn.Linear(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) for dim in args_dim.values()]    )
         UpperCAmelCase_     :				Optional[int]  =     domain_map





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Tuple[torch.Tensor]:
         UpperCAmelCase_     :				Optional[int]  =     [proj(_SCREAMING_SNAKE_CASE    ) for proj in self.proj]

         return self.domain_map(*_SCREAMING_SNAKE_CASE    )


class     __a(	nn.Module  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE    )					->						Union[str, Any]:
         super().__init__()
         UpperCAmelCase_     :				Tuple  =     function





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,*_SCREAMING_SNAKE_CASE    )					->						Tuple:
         return self.function(_SCREAMING_SNAKE_CASE  ,*_SCREAMING_SNAKE_CASE    )


class     __a:






    """simple docstring"""





    lowerCAmelCase         = 42
    lowerCAmelCase         = 42
    lowerCAmelCase         = 42





    def __init__( self  ,_SCREAMING_SNAKE_CASE = 1    )					->						None:
         UpperCAmelCase_     :				int  =     dim
         UpperCAmelCase_     :				Union[str, Any]  =     {k: dim * self.args_dim[k] for k in self.args_dim}





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         if self.dim == 1:
              return self.distribution_class(*_SCREAMING_SNAKE_CASE    )
         else:
              return Independent(self.distribution_class(*_SCREAMING_SNAKE_CASE    )  ,1    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,)					->						Distribution:
         UpperCAmelCase_     :				str  =     self._base_distribution(_SCREAMING_SNAKE_CASE    )
         if loc is None and scale is None:
              return distr
         else:
              return AffineTransformed(_SCREAMING_SNAKE_CASE  ,loc=_SCREAMING_SNAKE_CASE  ,scale=_SCREAMING_SNAKE_CASE  ,event_dim=self.event_dim    )





    @property
    def 				a__ ( self    )					->						Tuple:
         return () if self.dim == 1 else (self.dim,)





    @property
    def 				a__ ( self    )					->						int:
         return len(self.event_shape    )





    @property
    def 				a__ ( self    )					->						float:
         return 0.0





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						nn.Module:
         return ParameterProjection(
             in_features=_SCREAMING_SNAKE_CASE  ,args_dim=self.args_dim  ,domain_map=LambdaLayer(self.domain_map    )  ,)





    def 				a__ ( self  ,*_SCREAMING_SNAKE_CASE    )					->						Any:
         raise NotImplementedError()





    @staticmethod
    def 				a__ ( _SCREAMING_SNAKE_CASE    )					->						torch.Tensor:
         return (x + torch.sqrt(torch.square(_SCREAMING_SNAKE_CASE    ) + 4.0    )) / 2.0


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = {"df": 1, "loc": 1, "scale": 1}
    lowerCAmelCase         = StudentT





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         UpperCAmelCase_     :				Optional[Any]  =     cls.squareplus(_SCREAMING_SNAKE_CASE    ).clamp_min(torch.finfo(scale.dtype    ).eps    )
         UpperCAmelCase_     :				Union[str, Any]  =     2.0 + cls.squareplus(_SCREAMING_SNAKE_CASE    )
         return df.squeeze(-1    ), loc.squeeze(-1    ), scale.squeeze(-1    )


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = {"loc": 1, "scale": 1}
    lowerCAmelCase         = Normal





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     cls.squareplus(_SCREAMING_SNAKE_CASE    ).clamp_min(torch.finfo(scale.dtype    ).eps    )
         return loc.squeeze(-1    ), scale.squeeze(-1    )


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = {"total_count": 1, "logits": 1}
    lowerCAmelCase         = NegativeBinomial





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         UpperCAmelCase_     :				Union[str, Any]  =     cls.squareplus(_SCREAMING_SNAKE_CASE    )
         return total_count.squeeze(-1    ), logits.squeeze(-1    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Distribution:
         UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     distr_args
         if self.dim == 1:
              return self.distribution_class(total_count=_SCREAMING_SNAKE_CASE  ,logits=_SCREAMING_SNAKE_CASE    )
         else:
              return Independent(self.distribution_class(total_count=_SCREAMING_SNAKE_CASE  ,logits=_SCREAMING_SNAKE_CASE    )  ,1    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None    )					->						Distribution:
         UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     distr_args

         if scale is not None:
              # See scaling property of Gamma.
              logits += scale.log()

         return self._base_distribution((total_count, logits)    )





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = (
        '''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
        '''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
        '''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
    )
    lowerCAmelCase         = '''CIDAS/clipseg-rd64-refined'''
    lowerCAmelCase         = '''image_segmenter'''
    lowerCAmelCase         = CLIPSegForImageSegmentation

    lowerCAmelCase         = ['''image''', '''text''']
    lowerCAmelCase         = ['''image''']





    def __init__( self  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						Dict:
         requires_backends(self  ,['''vision''']    )
         super().__init__(*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Tuple:
         return self.pre_processor(text=[label]  ,images=[image]  ,padding=_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         with torch.no_grad():
              UpperCAmelCase_     :				Dict  =     self.model(**_SCREAMING_SNAKE_CASE    ).logits
         return logits





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict:
         UpperCAmelCase_     :				Dict  =     outputs.cpu().detach().numpy()
         UpperCAmelCase_     :				Any  =     0
         UpperCAmelCase_     :				List[Any]  =     1
         return Image.fromarray((array * 255).astype(np.uinta    )    )