


import os

import numpy
import onnx



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Any:
    _lowercase       :							Optional[Any]      		=		a.name
    _lowercase       :							Dict      		=		b.name

    _lowercase       :							List[str]      		=		''
    _lowercase       :							int      		=		''

    _lowercase       :							Optional[int]      		=		a == b

    _lowercase       :							str      		=		name_a
    _lowercase       :							Optional[int]      		=		name_b

    return res



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Union[str, Any]:
    for i, input_name in enumerate(node_proto.input ):
        if input_name == name:
            node_proto.input.insert(lowerCamelCase_							, lowerCamelCase_ )
            node_proto.input.pop(i + 1 )

    if node_proto.op_type == "If":
        _graph_replace_input_with(node_proto.attribute[0].g							, lowerCamelCase_							, lowerCamelCase_ )
        _graph_replace_input_with(node_proto.attribute[1].g							, lowerCamelCase_							, lowerCamelCase_ )
    if node_proto.op_type == "Loop":
        _graph_replace_input_with(node_proto.attribute[0].g							, lowerCamelCase_							, lowerCamelCase_ )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						int:
    for n in graph_proto.node:
        _node_replace_input_with(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    _lowercase       :							List[str]      		=		list(model.graph.initializer )
    _lowercase       :							str      		=		list(model_without_ext.graph.initializer )
    for i, ref_i in ind_to_replace:
        assert inits_with_data[i].name == inits[i].name
        assert inits_with_data[ref_i].name == inits[ref_i].name
        assert i > ref_i

        _lowercase       :							List[Any]      		=		inits[i].name
        _lowercase       :							Optional[int]      		=		inits[ref_i].name

        model_without_ext.graph.initializer.remove(inits[i] )

        # for n in model.graph.node:
        _graph_replace_input_with(model_without_ext.graph							, lowerCamelCase_							, lowerCamelCase_ )



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Optional[int]:
    _lowercase       :							str      		=		os.path.dirname(lowerCamelCase_ )
    _lowercase       :							Tuple      		=		os.path.basename(lowerCamelCase_ )

    _lowercase       :							Optional[int]      		=		onnx.load(os.path.join(lowerCamelCase_							, lowerCamelCase_ ) )

    _lowercase       :							Optional[int]      		=		list(model.graph.initializer )

    _lowercase       :							Dict      		=		set()
    _lowercase       :							Optional[int]      		=		{}
    _lowercase       :							Union[str, Any]      		=		[]

    _lowercase       :							Optional[int]      		=		0

    for i in range(len(lowerCamelCase_ ) ):
        if i in dup_set:
            continue

        for j in range(i + 1							, len(lowerCamelCase_ ) ):
            if j in dup_set:
                continue
            if _is_equal_tensor_proto(inits[i]							, inits[j] ):
                dup_set.add(lowerCamelCase_ )
                dup_set.add(lowerCamelCase_ )

                _lowercase       :							Optional[int]      		=		inits[j].data_type
                _lowercase       :							Optional[int]      		=		numpy.prod(inits[j].dims )
                if dtype == 1:
                    mem_size *= 4
                elif dtype == 6:
                    mem_size *= 4
                elif dtype == 7 or dtype == 11:
                    mem_size *= 8
                else:
                    print('unexpected data type: '							, lowerCamelCase_ )
                total_reduced_size += mem_size

                _lowercase       :							str      		=		inits[i].name
                _lowercase       :							List[str]      		=		inits[j].name

                if name_i in dup_map:
                    dup_map[name_i].append(lowerCamelCase_ )
                else:
                    _lowercase       :							Tuple      		=		[name_j]
                ind_to_replace.append((j, i) )

    print('total reduced size: '							, total_reduced_size / 1024 / 1024 / 1024							, 'GB' )

    _lowercase       :							int      		=		sorted(lowerCamelCase_ )
    _remove_dup_initializers_from_model(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )

    _lowercase       :							Tuple      		=		'optimized_' + model_file_name
    _lowercase       :							int      		=		os.path.join(lowerCamelCase_							, lowerCamelCase_ )
    onnx.save(lowerCamelCase_							, lowerCamelCase_ )

    return new_model







"""simple docstring"""







# 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__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (
          """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."""
      )
      _SCREAMING_SNAKE_CASE  				=     """CIDAS/clipseg-rd64-refined"""
      _SCREAMING_SNAKE_CASE  				=     """image_segmenter"""
      _SCREAMING_SNAKE_CASE  				=     CLIPSegForImageSegmentation

      _SCREAMING_SNAKE_CASE  				=     ["""image""", """text"""]
      _SCREAMING_SNAKE_CASE  				=     ["""image"""]



      def __init__(      self				:      Dict    ,			*SCREAMING_SNAKE_CASE_				:      Tuple    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]       ):
        requires_backends(self    ,			['vision']       )
        super().__init__(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      "Image"    ,			SCREAMING_SNAKE_CASE_				:      str       ):
        return self.pre_processor(text=[label]    ,			images=[image]    ,			padding=SCREAMING_SNAKE_CASE_    ,			return_tensors='pt'       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        with torch.no_grad():
          lowerCAmelCase_   :				List[str]								= self.model(**SCREAMING_SNAKE_CASE_       ).logits
        return logits





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Dict								= outputs.cpu().detach().numpy()
        lowerCAmelCase_   :				Optional[Any]								= 0
        lowerCAmelCase_   :				Optional[Any]								= 1
        return Image.fromarray((array * 2_5_5).astype(np.uinta       )       )





