


import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


SCREAMING_SNAKE_CASE							    =							importlib.util.find_spec('s3fs') is not None

if _has_safs:
  from .safilesystem import SaFileSystem  # noqa: F401

SCREAMING_SNAKE_CASE							    =							[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
  if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
    warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
  fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)




def  a   (lowerCAmelCase__   ):
   if "://" in dataset_path:
      __a					=  dataset_path.split("""://"""   )[1]
   return dataset_path




def  a   (lowerCAmelCase__   ):
   if fs is not None and fs.protocol != "file":
      return True
   else:
      return False




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  not is_remote_filesystem(lowerCAmelCase__   )
   if is_local:
      # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
      shutil.move(fs._strip_protocol(lowerCAmelCase__   )		,				fs._strip_protocol(lowerCAmelCase__   )   )
   else:
      fs.mv(lowerCAmelCase__		,				lowerCAmelCase__		,				recursive=lowerCAmelCase__   )




def  a   ():
   if hasattr(fsspec.asyn		,				"""reset_lock"""   ):
      # for future fsspec>2022.05.0
      fsspec.asyn.reset_lock()
   else:
      __a					=  None
      __a					=  None
      __a					=  threading.Lock()







# 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     __UpperCAmelCase							(       __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  ,       *__A  ,       **__A    ):
        requires_backends(self  ,       ["""vision"""]    )
        super().__init__(*__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        return self.pre_processor(text=[label]  ,       images=[image]  ,       padding=__A  ,       return_tensors="""pt"""    )


     def 	snake_case_  (	self  ,       __A    ):
        with torch.no_grad():
           __a					=  self.model(**__A    ).logits
        return logits


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  outputs.cpu().detach().numpy()
        __a					=  0
        __a					=  1
        return Image.fromarray((array * 255).astype(np.uinta    )    )



