
"""simple docstring"""

from __future__ import annotations

import math






def 							A		(       snake_case   :List[str]	)   ->			int:



 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5       ,      int(math.sqrt(lowerCAmelCase_	) + 1	)       ,      6	):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 							A		(       snake_case   :str	)   ->			Dict:
 __UpperCamelCase       						= str(lowerCAmelCase_	)
 __UpperCamelCase       						= [n]
 for i in range(1       ,      len(lowerCAmelCase_	)	):
  list_nums.append(int(str_num[i:]	)	)
  list_nums.append(int(str_num[:-i]	)	)
 return list_nums






def 							A		(       snake_case   :Optional[int]	)   ->			Tuple:



 if len(str(lowerCAmelCase_	)	) > 3:
  if not is_prime(int(str(lowerCAmelCase_	)[-3:]	)	) or not is_prime(int(str(lowerCAmelCase_	)[:3]	)	):
   return False
 return True






def 							A		(       snake_case   :Union[str, Any] = 1_1	)   ->			Tuple:
 __UpperCamelCase       						= []
 __UpperCamelCase       						= 1_3
 while len(lowerCAmelCase_	) != count:
  if validate(lowerCAmelCase_	):
   __UpperCamelCase       						= list_truncated_nums(lowerCAmelCase_	)
   if all(is_prime(lowerCAmelCase_	) for i in list_nums	):
    list_truncated_primes.append(lowerCAmelCase_	)
  num += 2
 return list_truncated_primes






def 							A		(       )   ->			Tuple:
 return sum(compute_truncated_primes(1_1	)	)


if __name__ == "__main__":
 print(f'''{sum(compute_truncated_primes(1_1)) = }''')





"""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
UpperCamelCase			:       Tuple									=				"."

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






def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[Any]	)   ->			Any:
 __UpperCamelCase       						= SavedModel()
 __UpperCamelCase       						= []

 with open(os.path.join(snake_case       ,      'utils'       ,      'tf_ops'       ,      'onnx.json'	)	) as f:
  __UpperCamelCase       						= 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       						= 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       						= sorted(snake_case	)
 __UpperCamelCase       						= []

 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			:       List[str]									=				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)"
      )
      UpperCamelCase			:       str									=				parser.parse_args()

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



