



import os

import numpy
import onnx



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
		_a					:				Optional[int]				=							a.name
		_a					:				Dict				=							b.name

		_a					:				Optional[int]				=							""""""
		_a					:				int				=							""""""

		_a					:				Any				=							a == b

		_a					:				List[Any]				=							name_a
		_a					:				List[str]				=							name_b

		return res



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:


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

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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:


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



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Tuple:
		_a					:				Dict				=							list(model.graph.initializer  )
		_a					:				List[Any]				=							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					:				Tuple				=							inits[i].name
				_a					:				Tuple				=							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						,      _lowerCamelCase						,      _lowerCamelCase  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				List[str]:
		_a					:				str				=							os.path.dirname(_lowerCamelCase  )
		_a					:				Dict				=							os.path.basename(_lowerCamelCase  )

		_a					:				Union[str, Any]				=							onnx.load(os.path.join(_lowerCamelCase						,      _lowerCamelCase  )  )

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

		_a					:				Optional[int]				=							set()
		_a					:				Optional[Any]				=							{}
		_a					:				Tuple				=							[]

		_a					:				List[Any]				=							0

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

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

								_a					:				List[Any]				=							inits[j].data_type
								_a					:				List[str]				=							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: '						,      _lowerCamelCase  )
								total_reduced_size += mem_size

								_a					:				Any				=							inits[i].name
								_a					:				Any				=							inits[j].name

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

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

		_a					:				int				=							sorted(_lowerCamelCase  )
		_remove_dup_initializers_from_model(_lowerCamelCase						,      _lowerCamelCase						,      _lowerCamelCase  )

		_a					:				str				=							"""optimized_""" + model_file_name
		_a					:				Optional[int]				=							os.path.join(_lowerCamelCase						,      _lowerCamelCase  )
		onnx.save(_lowerCamelCase						,      _lowerCamelCase  )

		return new_model









'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Optional[int]           = 'distilbert'
							lowerCAmelCase	:   Optional[int]           = {
							    'hidden_size': 'dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							}







							def __init__(							self :	str     ,_UpperCAmelCase :	Optional[Any]=30522     ,_UpperCAmelCase :	int=512     ,_UpperCAmelCase :	int=False     ,_UpperCAmelCase :	str=6     ,_UpperCAmelCase :	int=12     ,_UpperCAmelCase :	Union[str, Any]=768     ,_UpperCAmelCase :	Optional[Any]=4 * 768     ,_UpperCAmelCase :	int=0.1     ,_UpperCAmelCase :	Union[str, Any]=0.1     ,_UpperCAmelCase :	Tuple="gelu"     ,_UpperCAmelCase :	Any=0.02     ,_UpperCAmelCase :	List[Any]=0.1     ,_UpperCAmelCase :	Dict=0.2     ,_UpperCAmelCase :	Union[str, Any]=0     ,**_UpperCAmelCase :	List[Any]     ,):
									_a					:				Optional[Any]				=							vocab_size
									_a					:				Dict				=							max_position_embeddings
									_a					:				Optional[int]				=							sinusoidal_pos_embds
									_a					:				List[Any]				=							n_layers
									_a					:				Any				=							n_heads
									_a					:				int				=							dim
									_a					:				Optional[Any]				=							hidden_dim
									_a					:				str				=							dropout
									_a					:				Optional[Any]				=							attention_dropout
									_a					:				Any				=							activation
									_a					:				Tuple				=							initializer_range
									_a					:				Union[str, Any]				=							qa_dropout
									_a					:				List[Any]				=							seq_classif_dropout
									super().__init__(**_UpperCAmelCase     ,pad_token_id=_UpperCAmelCase		)

class      __magic_name__						(   _UpperCamelCase       ):







							@property
							def 							__lowercase      (							self :	Union[str, Any]		):
									if self.task == "multiple-choice":
											_a					:				Union[str, Any]				=							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a					:				List[str]				=							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)




