


import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


A      :    List[str]		=					logging.get_logger(__name__)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  os.getenv("SM_HP_MP_PARAMETERS"    ,					"{}"						)
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            lowerCamelCase__		:				List[str]    =  json.loads(_snake_case						)
            if "partitions" not in smp_options:
                  return False
      except json.JSONDecodeError:
            return False

      # Get the sagemaker specific framework parameters from mpi_options variable.
      lowerCamelCase__		:				int    =  os.getenv("SM_FRAMEWORK_PARAMS"    ,					"{}"						)
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            lowerCamelCase__		:				Union[str, Any]    =  json.loads(_snake_case						)
            if not mpi_options.get("sagemaker_mpi_enabled"    ,					_snake_case						):
                  return False
      except json.JSONDecodeError:
            return False
      # Lastly, check if the `smdistributed` module is present.
      return importlib.util.find_spec("smdistributed"						) is not None


if is_sagemaker_model_parallel_available():
    import smdistributed.modelparallel.torch as smp

    smp.init()







@dataclass
class 			_lowercase (							a__):


       """simple docstring"""




       A__											=			field(
           default=""							,  metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}							,  )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             super().__post_init__()
             warnings.warn(
                 "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
                 "`TrainingArguments` instead."					,      SCREAMING_SNAKE_CASE__					,      )





       @cached_property
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             logger.info("PyTorch: setting up devices"       )
             if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
                   logger.warning(
                       "torch.distributed process group is initialized, but local_rank == -1. "
                       "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"       )
             if self.no_cuda:
                   lowerCamelCase__		:				Optional[int]    =  torch.device("cpu"       )
                   lowerCamelCase__		:				int    =  0
             elif is_sagemaker_model_parallel_available():
                   lowerCamelCase__		:				Optional[int]    =  smp.local_rank()
                   lowerCamelCase__		:				Any    =  torch.device("cuda"					,      SCREAMING_SNAKE_CASE__       )
                   lowerCamelCase__		:				List[Any]    =  1
             elif is_sagemaker_dp_enabled():
                   import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

                   torch.distributed.init_process_group(backend="smddp"					,      timeout=self.ddp_timeout_delta       )
                   lowerCamelCase__		:				str    =  int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"       )       )
                   lowerCamelCase__		:				Dict    =  torch.device("cuda"					,      self.local_rank       )
                   lowerCamelCase__		:				Optional[int]    =  1
             elif self.local_rank == -1:
                   # if n_gpu is > 1 we'll use nn.DataParallel.
                   # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                   # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
                   # trigger an error that a device index is missing. Index 0 takes into account the
                   # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
                   # will use the first GPU in that env, i.e. GPU#1
                   lowerCamelCase__		:				List[str]    =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )
                   # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
                   # the default value.
                   lowerCamelCase__		:				List[str]    =  torch.cuda.device_count()
             else:
                   # Here, we'll use torch.distributed.
                   # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
                   if not torch.distributed.is_initialized():
                         torch.distributed.init_process_group(backend="nccl"					,      timeout=self.ddp_timeout_delta       )
                   lowerCamelCase__		:				List[Any]    =  torch.device("cuda"					,      self.local_rank       )
                   lowerCamelCase__		:				str    =  1

             if device.type == "cuda":
                   torch.cuda.set_device(SCREAMING_SNAKE_CASE__       )

             return device





       @property
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             if is_sagemaker_model_parallel_available():
                   return smp.dp_size()

             return super().world_size





       @property
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             return not is_sagemaker_model_parallel_available()





       @property
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             return False




"""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__      :    List[str]    							= '.'

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

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   SavedModel()
							SCREAMING_SNAKE_CASE__						:       Dict           =   []

							with open(os.path.join(_snake_case   ,"""utils"""   ,"""tf_ops"""   ,"""onnx.json"""						)						) as f:
														SCREAMING_SNAKE_CASE__						:       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()						)

							SCREAMING_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
							SCREAMING_SNAKE_CASE__						:       int           =   sorted(_snake_case						)
							SCREAMING_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(_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__      :    Optional[int]    							= 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__      :    Dict    							= parser.parse_args()

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




