




'''simple docstring'''






import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
A_			      =		logging.getLogger(__name__)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   str	      =    git.Repo(search_parent_directories=__SCREAMING_SNAKE_CASE		)
      snake_case__       :   List[Any]	      =    {
          'repo_id': str(__SCREAMING_SNAKE_CASE		),
          'repo_sha': str(repo.head.object.hexsha		),
          'repo_branch': str(repo.active_branch		),
      }

      with open(os.path.join(__SCREAMING_SNAKE_CASE				,      'git_log.json'		)				,      'w'		) as f:
            json.dump(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      indent=4		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      if params.n_gpu <= 0:
            snake_case__       :   Optional[Any]	      =    0
            snake_case__       :   Optional[Any]	      =    -1
            snake_case__       :   Any	      =    True
            snake_case__       :   int	      =    False
            return

      assert torch.cuda.is_available()

      logger.info('Initializing GPUs'		)
      if params.n_gpu > 1:
            assert params.local_rank != -1

            snake_case__       :   Dict	      =    int(os.environ['WORLD_SIZE']		)
            snake_case__       :   List[str]	      =    int(os.environ['N_GPU_NODE']		)
            snake_case__       :   Optional[Any]	      =    int(os.environ['RANK']		)

            # number of nodes / node ID
            snake_case__       :   List[Any]	      =    params.world_size // params.n_gpu_per_node
            snake_case__       :   int	      =    params.global_rank // params.n_gpu_per_node
            snake_case__       :   int	      =    True

            assert params.n_nodes == int(os.environ['N_NODES']		)
            assert params.node_id == int(os.environ['NODE_RANK']		)

      # local job (single GPU)
      else:
            assert params.local_rank == -1

            snake_case__       :   List[Any]	      =    1
            snake_case__       :   Union[str, Any]	      =    0
            snake_case__       :   Optional[Any]	      =    0
            snake_case__       :   Any	      =    0
            snake_case__       :   Optional[int]	      =    1
            snake_case__       :   List[Any]	      =    1
            snake_case__       :   Optional[Any]	      =    False

      # sanity checks
      assert params.n_nodes >= 1
      assert 0 <= params.node_id < params.n_nodes
      assert 0 <= params.local_rank <= params.global_rank < params.world_size
      assert params.world_size == params.n_nodes * params.n_gpu_per_node

      # define whether this is the master process / if we are in multi-node distributed mode
      snake_case__       :   str	      =    params.node_id == 0 and params.local_rank == 0
      snake_case__       :   Union[str, Any]	      =    params.n_nodes > 1

      # summary
      snake_case__       :   List[str]	      =    f"--- Global rank: {params.global_rank} - "
      logger.info(PREFIX + 'Number of nodes: %i' % params.n_nodes		)
      logger.info(PREFIX + 'Node ID        : %i' % params.node_id		)
      logger.info(PREFIX + 'Local rank     : %i' % params.local_rank		)
      logger.info(PREFIX + 'World size     : %i' % params.world_size		)
      logger.info(PREFIX + 'GPUs per node  : %i' % params.n_gpu_per_node		)
      logger.info(PREFIX + 'Master         : %s' % str(params.is_master		)		)
      logger.info(PREFIX + 'Multi-node     : %s' % str(params.multi_node		)		)
      logger.info(PREFIX + 'Multi-GPU      : %s' % str(params.multi_gpu		)		)
      logger.info(PREFIX + 'Hostname       : %s' % socket.gethostname()		)

      # set GPU device
      torch.cuda.set_device(params.local_rank		)

      # initialize multi-GPU
      if params.multi_gpu:
            logger.info('Initializing PyTorch distributed'		)
            torch.distributed.init_process_group(
                init_method='env://'				,      backend='nccl'				,      )






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      np.random.seed(args.seed		)
      torch.manual_seed(args.seed		)
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed		)






'''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)
