
"""simple docstring"""

import os

import numpy
import onnx
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   a.name
		lowerCamelCase					:      Any          =   b.name

		lowerCamelCase					:      Optional[Any]          =   ''
		lowerCamelCase					:      List[str]          =   ''

		lowerCamelCase					:      int          =   a == b

		lowerCamelCase					:      Tuple          =   name_a
		lowerCamelCase					:      Tuple          =   name_b

		return res
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


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

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





		'''simple docstring'''


		for n in graph_proto.node:
				_node_replace_input_with(a_,						a_,						a_      )
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   list(model.graph.initializer      )
		lowerCamelCase					:      str          =   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

				lowerCamelCase					:      str          =   inits[i].name
				lowerCamelCase					:      List[Any]          =   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,						a_,						a_      )





def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   os.path.dirname(a_      )
		lowerCamelCase					:      Dict          =   os.path.basename(a_      )

		lowerCamelCase					:      Any          =   onnx.load(os.path.join(a_,						a_      )      )

		lowerCamelCase					:      Dict          =   list(model.graph.initializer      )

		lowerCamelCase					:      Tuple          =   set()
		lowerCamelCase					:      Union[str, Any]          =   {}
		lowerCamelCase					:      int          =   []

		lowerCamelCase					:      List[str]          =   0

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

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

								lowerCamelCase					:      Union[str, Any]          =   inits[j].data_type
								lowerCamelCase					:      List[Any]          =   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: ',						a_      )
								total_reduced_size += mem_size

								lowerCamelCase					:      Tuple          =   inits[i].name
								lowerCamelCase					:      Optional[Any]          =   inits[j].name

								if name_i in dup_map:
										dup_map[name_i].append(a_      )
								else:
										lowerCamelCase					:      Dict          =   [name_j]
								ind_to_replace.append((j, i)      )

		print('total reduced size: ',						total_reduced_size / 1024 / 1024 / 1024,						'GB'      )

		lowerCamelCase					:      Tuple          =   sorted(a_      )
		_remove_dup_initializers_from_model(a_,						a_,						a_      )

		lowerCamelCase					:      int          =   'optimized_' + model_file_name
		lowerCamelCase					:      Dict          =   os.path.join(a_,						a_      )
		onnx.save(a_,						a_      )

		return new_model

"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






