


"""simple docstring"""
import os

import numpy
import onnx







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[Any]:
							snake_case_          =						a.name
							snake_case_          =						b.name

							snake_case_          =						""""""
							snake_case_          =						""""""

							snake_case_          =						a == b

							snake_case_          =						name_a
							snake_case_          =						name_b

							return res







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							for i, input_name in enumerate(node_proto.input			):
														if input_name == name:
																					node_proto.input.insert(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
																					node_proto.input.pop(i + 1			)

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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Dict:
							for n in graph_proto.node:
														_node_replace_input_with(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							snake_case_          =						list(model.graph.initializer			)
							snake_case_          =						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

														snake_case_          =						inits[i].name
														snake_case_          =						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			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							snake_case_          =						os.path.dirname(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						os.path.basename(_SCREAMING_SNAKE_CASE			)

							snake_case_          =						onnx.load(os.path.join(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)			)

							snake_case_          =						list(model.graph.initializer			)

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

							snake_case_          =						0

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

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

																												snake_case_          =						inits[j].data_type
																												snake_case_          =						numpy.prod(inits[j].dims			)
																												if dtype == 1:
																																			mem_size *= 4
																												elif dtype == 6:
																																			mem_size *= 4
																												elif dtype == 7 or dtype == 11:
																																			mem_size *= 8
																												else:
																																			print("""unexpected data type: """			,	_SCREAMING_SNAKE_CASE			)
																												total_reduced_size += mem_size

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

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

							print("""total reduced size: """			,	total_reduced_size / 1_024 / 1_024 / 1_024			,	"""GB"""			)

							snake_case_          =						sorted(_SCREAMING_SNAKE_CASE			)
							_remove_dup_initializers_from_model(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							snake_case_          =						"""optimized_""" + model_file_name
							snake_case_          =						os.path.join(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							onnx.save(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)

							return new_model







"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)



