


import os

import numpy
import onnx






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					a.name
			UpperCamelCase         =					b.name

			UpperCamelCase         =					""""""
			UpperCamelCase         =					""""""

			UpperCamelCase         =					a == b

			UpperCamelCase         =					name_a
			UpperCamelCase         =					name_b

			return res






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




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

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




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

						UpperCamelCase         =					inits[i].name
						UpperCamelCase         =					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       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					os.path.dirname(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					os.path.basename(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					onnx.load(os.path.join(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)	)

			UpperCamelCase         =					list(model.graph.initializer	)

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

			UpperCamelCase         =					0

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

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

												UpperCamelCase         =					inits[j].data_type
												UpperCamelCase         =					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: """       ,  SCREAMING_SNAKE_CASE_	)
												total_reduced_size += mem_size

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

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

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

			UpperCamelCase         =					sorted(SCREAMING_SNAKE_CASE_	)
			_remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					"""optimized_""" + model_file_name
			UpperCamelCase         =					os.path.join(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
			onnx.save(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			return new_model








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


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """lilt"""




			def __init__(						self							:		Tuple       ,       __magic_name__							:		Tuple=3_0_5_2_2       ,       __magic_name__							:		List[str]=7_6_8       ,       __magic_name__							:		Optional[Any]=1_2       ,       __magic_name__							:		str=1_2       ,       __magic_name__							:		Tuple=3_0_7_2       ,       __magic_name__							:		List[Any]="gelu"       ,       __magic_name__							:		Dict=0.1       ,       __magic_name__							:		str=0.1       ,       __magic_name__							:		Dict=5_1_2       ,       __magic_name__							:		Any=2       ,       __magic_name__							:		Optional[int]=0.02       ,       __magic_name__							:		Optional[int]=1e-12       ,       __magic_name__							:		Optional[int]=0       ,       __magic_name__							:		List[str]="absolute"       ,       __magic_name__							:		List[Any]=None       ,       __magic_name__							:		str=4       ,       __magic_name__							:		Any=1_0_2_4       ,       **__magic_name__							:		List[str]       ,       ):







						"""simple docstring"""







						super().__init__(pad_token_id=__magic_name__       ,       **__magic_name__						)

						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					type_vocab_size
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					layer_norm_eps
						UpperCamelCase         =					position_embedding_type
						UpperCamelCase         =					classifier_dropout
						UpperCamelCase         =					channel_shrink_ratio
						UpperCamelCase         =					max_ad_position_embeddings




