



from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_pt_objects import *  # noqa F403
else:
     from .scheduling_consistency_models import CMStochasticIterativeScheduler
     from .scheduling_ddim import DDIMScheduler
     from .scheduling_ddim_inverse import DDIMInverseScheduler
     from .scheduling_ddim_parallel import DDIMParallelScheduler
     from .scheduling_ddpm import DDPMScheduler
     from .scheduling_ddpm_parallel import DDPMParallelScheduler
     from .scheduling_deis_multistep import DEISMultistepScheduler
     from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
     from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
     from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
     from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
     from .scheduling_euler_discrete import EulerDiscreteScheduler
     from .scheduling_heun_discrete import HeunDiscreteScheduler
     from .scheduling_ipndm import IPNDMScheduler
     from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
     from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
     from .scheduling_karras_ve import KarrasVeScheduler
     from .scheduling_pndm import PNDMScheduler
     from .scheduling_repaint import RePaintScheduler
     from .scheduling_sde_ve import ScoreSdeVeScheduler
     from .scheduling_sde_vp import ScoreSdeVpScheduler
     from .scheduling_unclip import UnCLIPScheduler
     from .scheduling_unipc_multistep import UniPCMultistepScheduler
     from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
     from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_flax_objects import *  # noqa F403
else:
     from .scheduling_ddim_flax import FlaxDDIMScheduler
     from .scheduling_ddpm_flax import FlaxDDPMScheduler
     from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
     from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
     from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
     from .scheduling_pndm_flax import FlaxPNDMScheduler
     from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
     from .scheduling_utils_flax import (
         FlaxKarrasDiffusionSchedulers,
         FlaxSchedulerMixin,
         FlaxSchedulerOutput,
         broadcast_to_shape_from_left,
     )


try:
     if not (is_torch_available() and is_scipy_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
     from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
     if not (is_torch_available() and is_torchsde_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
     from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler










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_      :      Optional[int]    =     '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
a_      :      List[str]    =     [
    '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__":
     a_      :      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)'
     )
     a_      :      str    =     parser.parse_args()

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





