






# 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						( UpperCAmelCase_ ):
  lowercase_						:			Dict						=					(
      '''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.'''
  )
  lowercase_						:			List[str]						=					'''CIDAS/clipseg-rd64-refined'''
  lowercase_						:			List[Any]						=					'''image_segmenter'''
  lowercase_						:			Optional[Any]						=					CLIPSegForImageSegmentation

  lowercase_						:			str						=					['''image''', '''text''']
  lowercase_						:			Dict						=					['''image''']

  def __init__(    self						:	Optional[int]   ,				*lowerCamelCase__						:	Optional[Any]   ,				**lowerCamelCase__						:	Any			):
     """simple docstring"""

     requires_backends(self   ,				["""vision"""]			)
     super().__init__(*lowerCamelCase__   ,				**lowerCamelCase__			)

  def    a					(    self						:	int   ,				lowerCamelCase__						:	"Image"   ,				lowerCamelCase__						:	str			):
     """simple docstring"""

     return self.pre_processor(text=[label]   ,				images=[image]   ,				padding=lowerCamelCase__   ,				return_tensors="""pt"""			)

  def    a					(    self						:	Any   ,				lowerCamelCase__						:	Tuple			):
     """simple docstring"""

     with torch.no_grad():
        __UpperCamelCase	:			List[str]					=					self.model(**lowerCamelCase__			).logits
     return logits

  def    a					(    self						:	Dict   ,				lowerCamelCase__						:	Any			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					outputs.cpu().detach().numpy()
     __UpperCamelCase	:			Union[str, Any]					=					0
     __UpperCamelCase	:			Tuple					=					1
     return Image.fromarray((array * 2_55).astype(np.uinta			)			)











import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase    							=      '\nimport os\n'

UpperCamelCase    							=      '\ndef foo():\n    import os\n    return False\n'

UpperCamelCase    							=      '\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

UpperCamelCase    							=      '\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

UpperCamelCase    							=      [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]


@pytest.mark.parametrize("""case"""					,							__lowerCAmelCase							)
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[Any]							)     ->  str:
   __UpperCamelCase	:			str					=					os.path.join(__lowerCAmelCase					,							"""test_file.py"""							)
   with open(__lowerCAmelCase					,							"""w"""							) as _tmp_file:
      _tmp_file.write(__lowerCAmelCase							)

   __UpperCamelCase	:			Optional[int]					=					get_imports(__lowerCAmelCase							)
   assert parsed_imports == ["os"]



