

'''simple docstring'''
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCamelCase :      Any					 =     "."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
lowerCamelCase :      Optional[Any]					 =     [
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]


def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     int							, _UpperCamelCase						:     int				)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =SavedModel()
     _SCREAMING_SNAKE_CASE							    =[]

     with open(os.path.join(_UpperCamelCase							, 'utils'							, 'tf_ops'							, 'onnx.json'				)				) as f:
          _SCREAMING_SNAKE_CASE							    =json.load(_UpperCamelCase				)['opsets']

     for i in range(1							, opset + 1				):
          onnx_ops.extend(onnx_opsets[str(_UpperCamelCase				)]				)

     with open(_UpperCamelCase							, 'rb'				) as f:
          saved_model.ParseFromString(f.read()				)

     _SCREAMING_SNAKE_CASE							    =set()

     # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
     for meta_graph in saved_model.meta_graphs:
          # Add operations in the graph definition
          model_op_names.update(node.op for node in meta_graph.graph_def.node				)

          # Go through the functions in the graph definition
          for func in meta_graph.graph_def.library.function:
               # Add operations in each function
               model_op_names.update(node.op for node in func.node_def				)

    # Convert to list, sorted if you want
     _SCREAMING_SNAKE_CASE							    =sorted(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =[]

     for op in model_op_names:
          if op not in onnx_ops and op not in INTERNAL_OPS:
               incompatible_ops.append(_UpperCamelCase				)

     if strict and len(_UpperCamelCase				) > 0:
          raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops				)
     elif len(_UpperCamelCase				) > 0:
          print(f"Found the following incompatible ops for the opset {opset}:"				)
          print(*_UpperCamelCase							, sep='\n'				)
     else:
          print(f"The saved model {saved_model_path} can properly be converted with ONNX."				)


if __name__ == "__main__":
     lowerCamelCase :      Optional[Any]					 =     argparse.ArgumentParser()
     parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
     parser.add_argument(
         "--opset", default=1_2, type=int, help="The ONNX opset against which the model has to be tested."
     )
     parser.add_argument(
         "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
     )
     parser.add_argument(
         "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
     )
     lowerCamelCase :      Union[str, Any]					 =     parser.parse_args()

     if args.framework == "onnx":
          onnx_compliancy(args.saved_model_path, args.strict, args.opset)



'''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 							A__    (       A__  ):
  A__		 =				(
      '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.'
  )
  A__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






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





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
