





'''simple docstring'''




from __future__ import annotations

from scipy.special import comb  # type: ignore


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			list_of_points
          # Degree determines the flexibility of the curve.
          # Degree = 1 will produce a straight line.
          __A		:					List[Any]						=			len(__lowerCamelCase) - 1


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          assert 0 <= t <= 1, "Time t must be between 0 and 1."
          __A		:					list[float]						=			[]
          for i in range(len(self.list_of_points)):
               # basis function for each i
               output_values.append(
                   comb(self.degree		,      __lowerCamelCase) * ((1 - t) ** (self.degree - i)) * (t**i))
          # the basis must sum up to 1 for it to produce a valid Bezier curve.
          assert round(sum(__lowerCamelCase)		,      5) == 1
          return output_values


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          assert 0 <= t <= 1, "Time t must be between 0 and 1."

          __A		:					Optional[Any]						=			self.basis_function(__lowerCamelCase)
          __A		:					str						=			0.0
          __A		:					List[str]						=			0.0
          for i in range(len(self.list_of_points)):
               # For all points, sum up the product of i-th basis function and i-th point.
               x += basis_function[i] * self.list_of_points[i][0]
               y += basis_function[i] * self.list_of_points[i][1]
          return (x, y)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase = 0.01):

          '''simple docstring'''






          from matplotlib import pyplot as plt  # type: ignore

          __A		:					list[float]						=			[]  # x coordinates of points to plot
          __A		:					list[float]						=			[]  # y coordinates of points to plot

          __A		:					Union[str, Any]						=			0.0
          while t <= 1:
               __A		:					Tuple						=			self.bezier_curve_function(__lowerCamelCase)
               to_plot_x.append(value[0])
               to_plot_y.append(value[1])
               t += step_size

          __A		:					Optional[int]						=			[i[0] for i in self.list_of_points]
          __A		:					Any						=			[i[1] for i in self.list_of_points]

          plt.plot(
              __lowerCamelCase		,      __lowerCamelCase		,      color='blue'		,      label='Curve of Degree ' + str(self.degree)		,      )
          plt.scatter(__lowerCamelCase		,      __lowerCamelCase		,      color='red'		,      label='Control Points')
          plt.legend()
          plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
  BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
  BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3





'''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
A_			      =		"."

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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      snake_case__       :   str	      =    SavedModel()
      snake_case__       :   Union[str, Any]	      =    []

      with open(os.path.join(__SCREAMING_SNAKE_CASE				,      'utils'				,      'tf_ops'				,      'onnx.json'		)		) as f:
            snake_case__       :   Optional[Any]	      =    json.load(__SCREAMING_SNAKE_CASE		)['opsets']

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

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

      snake_case__       :   List[str]	      =    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
      snake_case__       :   Optional[int]	      =    sorted(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[Any]	      =    []

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

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


if __name__ == "__main__":
      A_			      =		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)"
      )
      A_			      =		parser.parse_args()

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