


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


lowerCAmelCase           =		{
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}





class       A    (   A_ ):
							UpperCamelCase_   :							List[Any]       ='''albert'''


							def __init__(self      ,				lowerCAmelCase=3_0_0_0_0      ,				lowerCAmelCase=1_2_8      ,				lowerCAmelCase=4_0_9_6      ,				lowerCAmelCase=1_2      ,				lowerCAmelCase=1      ,				lowerCAmelCase=6_4      ,				lowerCAmelCase=1_6_3_8_4      ,				lowerCAmelCase=1      ,				lowerCAmelCase="gelu_new"      ,				lowerCAmelCase=0      ,				lowerCAmelCase=0      ,				lowerCAmelCase=5_1_2      ,				lowerCAmelCase=2      ,				lowerCAmelCase=0.02      ,				lowerCAmelCase=1E-12      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase="absolute"      ,				lowerCAmelCase=0      ,				lowerCAmelCase=2      ,				lowerCAmelCase=3      ,				**lowerCAmelCase      ,				):
									super().__init__(pad_token_id=lowerCAmelCase      ,				bos_token_id=lowerCAmelCase      ,				eos_token_id=lowerCAmelCase      ,				**lowerCAmelCase					)

									__lowercase=			vocab_size
									__lowercase=			embedding_size
									__lowercase=			hidden_size
									__lowercase=			num_hidden_layers
									__lowercase=			num_hidden_groups
									__lowercase=			num_attention_heads
									__lowercase=			inner_group_num
									__lowercase=			hidden_act
									__lowercase=			intermediate_size
									__lowercase=			hidden_dropout_prob
									__lowercase=			attention_probs_dropout_prob
									__lowercase=			max_position_embeddings
									__lowercase=			type_vocab_size
									__lowercase=			initializer_range
									__lowercase=			layer_norm_eps
									__lowercase=			classifier_dropout_prob
									__lowercase=			position_embedding_type





class       A    (   A_ ):


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










import os

import numpy
import onnx







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







		__lowercase=			a.name
		__lowercase=			b.name

		__lowercase=			''
		__lowercase=			''

		__lowercase=			a == b

		__lowercase=			name_a
		__lowercase=			name_b

		return res







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Union[str, Any]:

		'''simple docstring'''







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

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







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	str:

		'''simple docstring'''







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







def        _lowerCamelCase(	lowercase__   , lowercase__   , lowercase__	)						->	Any:

		'''simple docstring'''







		__lowercase=			list(model.graph.initializer	)
		__lowercase=			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

				__lowercase=			inits[i].name
				__lowercase=			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   , lowercase__   , lowercase__	)







def        _lowerCamelCase(	lowercase__	)						->	Dict:

		'''simple docstring'''







		__lowercase=			os.path.dirname(lowercase__	)
		__lowercase=			os.path.basename(lowercase__	)

		__lowercase=			onnx.load(os.path.join(lowercase__   , lowercase__	)	)

		__lowercase=			list(model.graph.initializer	)

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

		__lowercase=			0

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

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

								__lowercase=			inits[j].data_type
								__lowercase=			numpy.prod(inits[j].dims	)
								if dtype == 1:
										mem_size *= 4
								elif dtype == 6:
										mem_size *= 4
								elif dtype == 7 or dtype == 1_1:
										mem_size *= 8
								else:
										print('unexpected data type: '   , lowercase__	)
								total_reduced_size += mem_size

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

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

		print('total reduced size: '   , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4   , 'GB'	)

		__lowercase=			sorted(lowercase__	)
		_remove_dup_initializers_from_model(lowercase__   , lowercase__   , lowercase__	)

		__lowercase=			'optimized_' + model_file_name
		__lowercase=			os.path.join(lowercase__   , lowercase__	)
		onnx.save(lowercase__   , lowercase__	)

		return new_model






