



import os

import numpy
import onnx


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     a.name
			A__	  =     b.name

			A__	  =     ''
			A__	  =     ''

			A__	  =     a == b

			A__	  =     name_a
			A__	  =     name_b

			return res


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			for n in graph_proto.node:
						_node_replace_input_with(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     list(model.graph.initializer       )
			A__	  =     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

						A__	  =     inits[i].name
						A__	  =     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 A    (							__UpperCamelCase       )    ->   Tuple:
			A__	  =     os.path.dirname(__UpperCamelCase       )
			A__	  =     os.path.basename(__UpperCamelCase       )

			A__	  =     onnx.load(os.path.join(__UpperCamelCase			,						__UpperCamelCase       )       )

			A__	  =     list(model.graph.initializer       )

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

			A__	  =     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       )

												A__	  =     inits[j].data_type
												A__	  =     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

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

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

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

			A__	  =     sorted(__UpperCamelCase       )
			_remove_dup_initializers_from_model(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )

			A__	  =     'optimized_' + model_file_name
			A__	  =     os.path.join(__UpperCamelCase			,						__UpperCamelCase       )
			onnx.save(__UpperCamelCase			,						__UpperCamelCase       )

			return new_model









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   			=   {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__   			=   [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							SCREAMING_SNAKE_CASE__   			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure)




