





"""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
_snake_case     :		Optional[int]       						= '.'

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






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  SavedModel()
							A		=  []

							with open(os.path.join(UpperCamelCase					,  "utils"					,  "tf_ops"					,  "onnx.json"       )       ) as f:
														A		=  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()       )

							A		=  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		=  sorted(UpperCamelCase       )
							A		=  []

							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__":
		_snake_case     :		Dict       						= 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)'
		)
		_snake_case     :		Tuple       						= parser.parse_args()

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









"""simple docstring"""
def 					A__				(			UpperCamelCase       ):
							A		=  generate_pascal_triangle(UpperCamelCase       )
							for row_idx in range(UpperCamelCase       ):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1       ):
																					print(end=" "       )
														# Print row values
														for col_idx in range(row_idx + 1       ):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]					,  end=" "       )
																					else:
																												print(triangle[row_idx][col_idx]					,  end=""       )
														print()






def 					A__				(			UpperCamelCase       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														raise TypeError("The input value of 'num_rows' should be 'int'"       )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    "The input value of 'num_rows' should be greater than or equal to 0"       )

							A		=  []
							for current_row_idx in range(UpperCamelCase       ):
														A		=  populate_current_row(UpperCamelCase					,  UpperCamelCase       )
														triangle.append(UpperCamelCase       )
							return triangle






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							A,	A		=  1, 1
							for current_col_idx in range(1					,  UpperCamelCase       ):
														calculate_current_element(
														    UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )
							return current_row






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  ):
							A		=  triangle[current_row_idx - 1][current_col_idx - 1]
							A		=  triangle[current_row_idx - 1][current_col_idx]
							A		=  above_to_left_elt + above_to_right_elt






def 					A__				(			UpperCamelCase       ):

							if not isinstance(UpperCamelCase					,  UpperCamelCase       ):
														raise TypeError("The input value of 'num_rows' should be 'int'"       )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    "The input value of 'num_rows' should be greater than or equal to 0"       )

							A		=  [[1]]

							for row_index in range(1					,  UpperCamelCase       ):
														A		=  [0] + result[-1] + [0]
														A		=  row_index + 1
														# Calculate the number of distinct elements in a row
														A		=  sum(divmod(UpperCamelCase					,  2       )       )
														A		=  [
														    temp_row[i - 1] + temp_row[i] for i in range(1					,  distinct_elements + 1       )
														]
														A		=  row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														A		=  row_first_half + row_second_half
														result.append(UpperCamelCase       )

							return result






def 					A__				(			):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(UpperCamelCase					,  UpperCamelCase       ) -> None:
														A		=  F"{func.__name__}({value})"
														A		=  timeit(F"__main__.{call}"					,  setup="import __main__"       )
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F"{call:38} -- {timing:.4f} seconds"       )

							for value in range(15       ):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(UpperCamelCase					,  UpperCamelCase       )
														print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()


