


import os

import numpy
import onnx


def 				lowerCamelCase__	(      a	:   List[Any]	,  a	:   List[Any]				)    ->	str:





			"""simple docstring"""
			a__ :Tuple						  =    a.name
			a__ :Optional[int]						  =    b.name

			a__ :Any						  =    ""
			a__ :Union[str, Any]						  =    ""

			a__ :Union[str, Any]						  =    a == b

			a__ :Union[str, Any]						  =    name_a
			a__ :Union[str, Any]						  =    name_b

			return res


def 				lowerCamelCase__	(      a	:   List[str]	,  a	:   int	,  a	:   Dict				)    ->	Union[str, Any]:





			"""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 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   str	,  a	:   str				)    ->	Union[str, Any]:





			"""simple docstring"""
			for n in graph_proto.node:
						_node_replace_input_with(a	,  a	,  a				)


def 				lowerCamelCase__	(      a	:   int	,  a	:   Optional[int]	,  a	:   int				)    ->	str:





			"""simple docstring"""
			a__ :str						  =    list(model.graph.initializer				)
			a__ :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

						a__ :str						  =    inits[i].name
						a__ :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 				lowerCamelCase__	(      a	:   List[Any]				)    ->	List[str]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    os.path.dirname(a				)
			a__ :int						  =    os.path.basename(a				)

			a__ :str						  =    onnx.load(os.path.join(a	,  a				)				)

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

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

			a__ :Tuple						  =    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				)

												a__ :Any						  =    inits[j].data_type
												a__ :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

												a__ :int						  =    inits[i].name
												a__ :List[str]						  =    inits[j].name

												if name_i in dup_map:
															dup_map[name_i].append(a				)
												else:
															a__ :List[Any]						  =    [name_j]
												ind_to_replace.append((j, i)				)

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

			a__ :Any						  =    sorted(a				)
			_remove_dup_initializers_from_model(a	,  a	,  a				)

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

			return new_model






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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'pegasus'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Any   ,     __A						:   Dict=50265   ,     __A						:   List[Any]=1024   ,     __A						:   int=12   ,     __A						:   Optional[Any]=4096   ,     __A						:   Optional[int]=16   ,     __A						:   Dict=12   ,     __A						:   List[Any]=4096   ,     __A						:   List[str]=16   ,     __A						:   Optional[int]=0.0   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=True   ,     __A						:   Optional[int]=True   ,     __A						:   str="gelu"   ,     __A						:   Tuple=1024   ,     __A						:   Any=0.1   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=0.0   ,     __A						:   Tuple=0.02   ,     __A						:   Union[str, Any]=0   ,     __A						:   Union[str, Any]=False   ,     __A						:   Optional[Any]=0   ,     __A						:   Tuple=1   ,     __A						:   str=1   ,     **__A						:   Any   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Any						  =    vocab_size
									a__ :List[str]						  =    max_position_embeddings
									a__ :int						  =    d_model
									a__ :Union[str, Any]						  =    encoder_ffn_dim
									a__ :List[Any]						  =    encoder_layers
									a__ :Union[str, Any]						  =    encoder_attention_heads
									a__ :Tuple						  =    decoder_ffn_dim
									a__ :List[Any]						  =    decoder_layers
									a__ :Tuple						  =    decoder_attention_heads
									a__ :Optional[int]						  =    dropout
									a__ :str						  =    attention_dropout
									a__ :Optional[int]						  =    activation_dropout
									a__ :str						  =    activation_function
									a__ :Dict						  =    init_std
									a__ :Any						  =    encoder_layerdrop
									a__ :int						  =    decoder_layerdrop
									a__ :Union[str, Any]						  =    use_cache
									a__ :List[Any]						  =    encoder_layers
									a__ :Dict						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__A   ,     eos_token_id=__A   ,     is_encoder_decoder=__A   ,     decoder_start_token_id=__A   ,     forced_eos_token_id=__A   ,     **__A   ,     )




						@property
						def  _snake_case					(	self						:   Optional[Any]						)      ->int:






									"""simple docstring"""






									return self.encoder_attention_heads






						@property
						def  _snake_case					(	self						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									return self.d_model


