





import os

import numpy
import onnx






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Dict       , UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   a.name
		SCREAMING_SNAKE_CASE__								=   b.name

		SCREAMING_SNAKE_CASE__								=   """"""
		SCREAMING_SNAKE_CASE__								=   """"""

		SCREAMING_SNAKE_CASE__								=   a == b

		SCREAMING_SNAKE_CASE__								=   name_a
		SCREAMING_SNAKE_CASE__								=   name_b

		return res






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]     ):
		for i, input_name in enumerate(node_proto.input     ):
				if input_name == name:
						node_proto.input.insert(UpperCamelCase__       , UpperCamelCase__     )
						node_proto.input.pop(i + 1     )

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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Tuple     ):
		for n in graph_proto.node:
				_node_replace_input_with(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]     ):
		SCREAMING_SNAKE_CASE__								=   list(model.graph.initializer     )
		SCREAMING_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

				SCREAMING_SNAKE_CASE__								=   inits[i].name
				SCREAMING_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       , UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]     ):
		SCREAMING_SNAKE_CASE__								=   os.path.dirname(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   os.path.basename(UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   onnx.load(os.path.join(UpperCamelCase__       , UpperCamelCase__     )     )

		SCREAMING_SNAKE_CASE__								=   list(model.graph.initializer     )

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

		SCREAMING_SNAKE_CASE__								=   0

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

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

								SCREAMING_SNAKE_CASE__								=   inits[j].data_type
								SCREAMING_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: """       , UpperCamelCase__     )
								total_reduced_size += mem_size

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

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

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

		SCREAMING_SNAKE_CASE__								=   sorted(UpperCamelCase__     )
		_remove_dup_initializers_from_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   """optimized_""" + model_file_name
		SCREAMING_SNAKE_CASE__								=   os.path.join(UpperCamelCase__       , UpperCamelCase__     )
		onnx.save(UpperCamelCase__       , UpperCamelCase__     )

		return new_model

"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowercase							=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





