





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
lowercase				:	Tuple      						=  '''.'''

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



def A_     (		A__		,				A__		,				A__    )  ->		Any:
       a__						:     Optional[Any]			=    SavedModel()
       a__						:     Optional[int]			=    []

       with open(os.path.join(_lowercase		,				'utils'		,				'tf_ops'		,				'onnx.json'    )    ) as f:
              a__						:     int			=    json.load(_lowercase    )["opsets"]

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

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

       a__						:     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
       a__						:     str			=    sorted(_lowercase    )
       a__						:     str			=    []

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

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


if __name__ == "__main__":
    lowercase				:	Union[str, 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)"""
    )
    lowercase				:	Optional[Any]      						=  parser.parse_args()

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









from __future__ import annotations

from collections.abc import Callable



def A_     (		A__		,				A__		,				A__		,				A__ = 100		,				)  ->		float:
       a__						:     Dict			=    x_start
       a__						:     Any			=    fnc(A__    )
       a__						:     Optional[int]			=    0.0
       for _ in range(A__    ):
              # Approximates small segments of curve as linear and solve
              # for trapezoidal area
              a__						:     Union[str, Any]			=    (x_end - x_start) / steps + xa
              a__						:     str			=    fnc(A__    )
              area += abs(fxa + fxa    ) * (xa - xa) / 2
              # Increment step
              a__						:     Optional[Any]			=    xa
              a__						:     Optional[int]			=    fxa
       return area


if __name__ == "__main__":



    def A_     (		A__    )  ->		List[str]:
           return x**3 + x**2

    print("""f(x) = x^3 + x^2""")
    print("""The area between the curve, x = -5, x = 5 and the x axis is:""")
    lowercase				:	Union[str, Any]      						=  1_0
    while i <= 1_0_0_0_0_0:
        print(F"""with {i} steps: {trapezoidal_area(f, -5, 5, i)}""")
        i *= 1_0


