from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def 					lowercase__				(					__snake_case   :							Tuple					,	__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							Union[str, Any]					,	):


					'''simple docstring'''


					UpperCAmelCase_   :				List[str]						=						coefficient_matrix.shape
					UpperCAmelCase_   :				List[str]						=						constant_matrix.shape

					if rowsa != colsa:
										UpperCAmelCase_   :				List[Any]						=						F"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
										raise ValueError(lowerCAmelCase_  )

					if colsa != 1:
										UpperCAmelCase_   :				Tuple						=						F"Constant matrix must be nx1 but received {rowsa}x{colsa}"
										raise ValueError(lowerCAmelCase_  )

					if rowsa != rowsa:
										UpperCAmelCase_   :				Dict						=						(
										    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
										    F"received {rowsa}x{colsa} and {rowsa}x{colsa}"
										)
										raise ValueError(lowerCAmelCase_  )

					if len(lowerCAmelCase_  ) != rowsa:
										UpperCAmelCase_   :				Any						=						(
										    'Number of initial values must be equal to number of rows in coefficient '
										    F"matrix but received {len(lowerCAmelCase_  )} and {rowsa}"
										)
										raise ValueError(lowerCAmelCase_  )

					if iterations <= 0:
										raise ValueError('Iterations must be at least 1'  )

					UpperCAmelCase_   :				NDArray[floataa]						=						np.concatenate(
					    (coefficient_matrix, constant_matrix)					,	axis=1  )

					UpperCAmelCase_   :				Dict						=						table.shape

					strictly_diagonally_dominant(lowerCAmelCase_  )

					# Iterates the whole matrix for given number of times
					for _ in range(lowerCAmelCase_  ):
										UpperCAmelCase_   :				List[Any]						=						[]
										for row in range(lowerCAmelCase_  ):
															UpperCAmelCase_   :				Optional[int]						=						0
															for col in range(lowerCAmelCase_  ):
																				if col == row:
																									UpperCAmelCase_   :				int						=						table[row][col]
																				elif col == cols - 1:
																									UpperCAmelCase_   :				Any						=						table[row][col]
																				else:
																									temp += (-1) * table[row][col] * init_val[col]
															UpperCAmelCase_   :				int						=						(temp + val) / denom
															new_val.append(lowerCAmelCase_  )
										UpperCAmelCase_   :				Any						=						new_val

					return [float(lowerCAmelCase_  ) for i in new_val]




def 					lowercase__				(					__snake_case   :							Tuple  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						table.shape

					UpperCAmelCase_   :				List[Any]						=						True

					for i in range(0					,	lowerCAmelCase_  ):
										UpperCAmelCase_   :				List[Any]						=						0
										for j in range(0					,	cols - 1  ):
															if i == j:
																				continue
															else:
																				total += table[i][j]

										if table[i][i] <= total:
															raise ValueError('Coefficient matrix is not strictly diagonally dominant'  )

					return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()














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
__UpperCAmelCase     							=						'.'

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




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						SavedModel()
					UpperCAmelCase_   :				Optional[Any]						=						[]

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

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

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

					UpperCAmelCase_   :				List[Any]						=						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
					UpperCAmelCase_   :				Optional[int]						=						sorted(__snake_case  )
					UpperCAmelCase_   :				int						=						[]

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

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


if __name__ == "__main__":
		__UpperCAmelCase     							=						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)'
		)
		__UpperCAmelCase     							=						parser.parse_args()

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






