

'''simple docstring'''
import os

import numpy
import onnx




def 	UpperCamelCase   (       lowercase_						:    Any     ,					lowercase_						:    Union[str, Any]	)		->	List[Any]:




  '''simple docstring'''



  lowercase									=a.name
  lowercase									=b.name

  lowercase									=''''''
  lowercase									=''''''

  lowercase									=a == b

  lowercase									=name_a
  lowercase									=name_b

  return res




def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    Tuple     ,					lowercase_						:    int	)		->	Optional[int]:




  '''simple docstring'''



  for i, input_name in enumerate(node_proto.input	):
    if input_name == name:
      node_proto.input.insert(lowercase_     ,					lowercase_	)
      node_proto.input.pop(i + 1	)

  if node_proto.op_type == "If":
    _graph_replace_input_with(node_proto.attribute[0].g     ,					lowercase_     ,					lowercase_	)
    _graph_replace_input_with(node_proto.attribute[1].g     ,					lowercase_     ,					lowercase_	)
  if node_proto.op_type == "Loop":
    _graph_replace_input_with(node_proto.attribute[0].g     ,					lowercase_     ,					lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    Optional[Any]     ,					lowercase_						:    str     ,					lowercase_						:    Optional[Any]	)		->	Union[str, Any]:




  '''simple docstring'''



  for n in graph_proto.node:
    _node_replace_input_with(lowercase_     ,					lowercase_     ,					lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    Union[str, Any]     ,					lowercase_						:    Optional[int]     ,					lowercase_						:    int	)		->	Tuple:




  '''simple docstring'''



  lowercase									=list(model.graph.initializer	)
  lowercase									=list(model_without_ext.graph.initializer	)
  for i, ref_i in ind_to_replace:
    assert inits_with_data[i].name == inits[i].name
    assert inits_with_data[ref_i].name == inits[ref_i].name
    assert i > ref_i

    lowercase									=inits[i].name
    lowercase									=inits[ref_i].name

    model_without_ext.graph.initializer.remove(inits[i]	)

    # for n in model.graph.node:
    _graph_replace_input_with(model_without_ext.graph     ,					lowercase_     ,					lowercase_	)




def 	UpperCamelCase   (       lowercase_						:    Optional[int]	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=os.path.dirname(lowercase_	)
  lowercase									=os.path.basename(lowercase_	)

  lowercase									=onnx.load(os.path.join(lowercase_     ,					lowercase_	)	)

  lowercase									=list(model.graph.initializer	)

  lowercase									=set()
  lowercase									={}
  lowercase									=[]

  lowercase									=0

  for i in range(len(lowercase_	)	):
    if i in dup_set:
      continue

    for j in range(i + 1     ,					len(lowercase_	)	):
      if j in dup_set:
        continue
      if _is_equal_tensor_proto(inits[i]     ,					inits[j]	):
        dup_set.add(lowercase_	)
        dup_set.add(lowercase_	)

        lowercase									=inits[j].data_type
        lowercase									=numpy.prod(inits[j].dims	)
        if dtype == 1:
          mem_size *= 4
        elif dtype == 6:
          mem_size *= 4
        elif dtype == 7 or dtype == 1_1:
          mem_size *= 8
        else:
          print('''unexpected data type: '''     ,					lowercase_	)
        total_reduced_size += mem_size

        lowercase									=inits[i].name
        lowercase									=inits[j].name

        if name_i in dup_map:
          dup_map[name_i].append(lowercase_	)
        else:
          lowercase									=[name_j]
        ind_to_replace.append((j, i)	)

  print('''total reduced size: '''     ,					total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4     ,					'''GB'''	)

  lowercase									=sorted(lowercase_	)
  _remove_dup_initializers_from_model(lowercase_     ,					lowercase_     ,					lowercase_	)

  lowercase									='''optimized_''' + model_file_name
  lowercase									=os.path.join(lowercase_     ,					lowercase_	)
  onnx.save(lowercase_     ,					lowercase_	)

  return new_model







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
