"""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     :				Optional[Any]    				=    "."

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = SavedModel()
		lowerCAmelCase__   = []

		with open(os.path.join(lowerCamelCase__      ,	"""utils"""      ,	"""tf_ops"""      ,	"""onnx.json"""  )  ) as f:
				lowerCAmelCase__   = json.load(lowerCamelCase__  )["""opsets"""]

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

		with open(lowerCamelCase__      ,	"""rb"""  ) as f:
				saved_model.ParseFromString(f.read()  )

		lowerCAmelCase__   = 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
		lowerCAmelCase__   = sorted(lowerCamelCase__  )
		lowerCAmelCase__   = []

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

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


if __name__ == "__main__":
			__lowerCAmelCase     :				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=12, 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     :				List[Any]    				=    parser.parse_args()

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





"""simple docstring"""




import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		if (ksize % 2) == 0:
				lowerCAmelCase__   = ksize + 1
		lowerCAmelCase__   = np.zeros((ksize, ksize)      ,	dtype=np.floataa  )

		# each value
		for y in range(lowerCamelCase__  ):
				for x in range(lowerCamelCase__  ):
						# distance from center
						lowerCAmelCase__   = x - ksize // 2
						lowerCAmelCase__   = y - ksize // 2

						# degree to radiant
						lowerCAmelCase__   = theta / 180 * np.pi
						lowerCAmelCase__   = np.cos(_theta  )
						lowerCAmelCase__   = np.sin(_theta  )

						# get kernel x
						lowerCAmelCase__   = cos_theta * px + sin_theta * py

						# get kernel y
						lowerCAmelCase__   = -sin_theta * px + cos_theta * py

						# fill kernel
						lowerCAmelCase__   = np.exp(
						    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)  ) * np.cos(2 * np.pi * _x / lambd + psi  )

		return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			__lowerCAmelCase     :				Tuple    				=    imread("../image_data/lena.jpg")
			# turn image in gray scale value
			__lowerCAmelCase     :				List[str]    				=    cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			__lowerCAmelCase     :				Union[str, Any]    				=    np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 1_20, 1_50]:
						__lowerCAmelCase     :				Union[str, Any]    				=    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			__lowerCAmelCase     :				Optional[int]    				=    out / out.max() * 2_55
			__lowerCAmelCase     :				Tuple    				=    out.astype(np.uinta)

			imshow("Original", gray)
			imshow("Gabor filter with 20x20 mask and 6 directions", out)

			waitKey(0)




