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#
# 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    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		(
     """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."""
 )
 UpperCAmelCase__													=		"""CIDAS/clipseg-rd64-refined"""
 UpperCAmelCase__													=		"""image_segmenter"""
 UpperCAmelCase__													=		CLIPSegForImageSegmentation

 UpperCAmelCase__													=		["""image""", """text"""]
 UpperCAmelCase__													=		["""image"""]
 def __init__(      self  :      Optional[Any]			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Tuple	)			->	Union[str, Any]:
     requires_backends(self			,     ['vision']	)
     super().__init__(*UpperCAmelCase			,     **UpperCAmelCase	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      "Image"			,     UpperCAmelCase  :      str	)			->	str:
     return self.pre_processor(text=[label]			,     images=[image]			,     padding=UpperCAmelCase			,     return_tensors='pt'	)
 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      Dict	)			->	Union[str, Any]:
     with torch.no_grad():
         lowerCamelCase__    :      Dict					 =		self.model(**UpperCAmelCase	).logits
     return logits







 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      Union[str, Any]	)			->	Dict:
     lowerCamelCase__    :      List[Any]					 =		outputs.cpu().detach().numpy()
     lowerCamelCase__    :      Any					 =		0
     lowerCamelCase__    :      Dict					 =		1
     return Image.fromarray((array * 255).astype(np.uinta	)	)












import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""M-CLIP"""
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]=1024			,     UpperCAmelCase  :      Tuple=768			,     **UpperCAmelCase  :      Optional[int]	)			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		transformerDimSize
     lowerCamelCase__    :      Optional[Any]					 =		imageDimSize
     super().__init__(**UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		MCLIPConfig
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      Dict			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     super().__init__(UpperCAmelCase			,     *UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		XLMRobertaModel(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.nn.Linear(
         in_features=config.transformerDimensions			,     out_features=config.numDims	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Dict	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.transformer(input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase	)[0]
     lowerCamelCase__    :      int					 =		(embs * attention_mask.unsqueeze(2	)).sum(dim=1	) / attention_mask.sum(dim=1	)[:, None]
     return self.LinearTransformation(UpperCAmelCase	), embs





