






import os

import numpy
import onnx





def        _snake_case   (_snake_case				:       List[Any]      ,    _snake_case				:       Dict)					->	Optional[Any]:
   _lowercase              =a.name
   _lowercase              =b.name

   _lowercase              =''
   _lowercase              =''

   _lowercase              =a == b

   _lowercase              =name_a
   _lowercase              =name_b

   return res





def        _snake_case   (_snake_case				:       str      ,    _snake_case				:       Dict      ,    _snake_case				:       int)					->	Tuple:
   for i, input_name in enumerate(node_proto.input):
      if input_name == name:
         node_proto.input.insert(_snake_case      ,    _snake_case)
         node_proto.input.pop(i + 1)

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





def        _snake_case   (_snake_case				:       List[str]      ,    _snake_case				:       Union[str, Any]      ,    _snake_case				:       str)					->	Any:
   for n in graph_proto.node:
      _node_replace_input_with(_snake_case      ,    _snake_case      ,    _snake_case)





def        _snake_case   (_snake_case				:       Any      ,    _snake_case				:       List[Any]      ,    _snake_case				:       int)					->	Union[str, Any]:
   _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      ,    _snake_case      ,    _snake_case)





def        _snake_case   (_snake_case				:       List[Any])					->	List[Any]:
   _lowercase              =os.path.dirname(_snake_case)
   _lowercase              =os.path.basename(_snake_case)

   _lowercase              =onnx.load(os.path.join(_snake_case      ,    _snake_case))

   _lowercase              =list(model.graph.initializer)

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

   _lowercase              =0

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

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

            _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 == 11:
               mem_size *= 8
            else:
               print('unexpected data type: '      ,    _snake_case)
            total_reduced_size += mem_size

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

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

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

   _lowercase              =sorted(_snake_case)
   _remove_dup_initializers_from_model(_snake_case      ,    _snake_case      ,    _snake_case)

   _lowercase              ='optimized_' + model_file_name
   _lowercase              =os.path.join(_snake_case      ,    _snake_case)
   onnx.save(_snake_case      ,    _snake_case)

   return new_model










import warnings

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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					='''mvp'''
       __lowerCAmelCase    :		List[Any]					=['''past_key_values''']
       __lowerCAmelCase    :		Optional[Any]					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__(				self	:Any,      snake_case	:Any=5_0267,      snake_case	:List[str]=1024,      snake_case	:List[str]=12,      snake_case	:List[Any]=4096,      snake_case	:List[Any]=16,      snake_case	:Optional[int]=12,      snake_case	:Optional[int]=4096,      snake_case	:List[str]=16,      snake_case	:Optional[Any]=0.0,      snake_case	:Tuple=0.0,      snake_case	:str="gelu",      snake_case	:int=1024,      snake_case	:Any=0.1,      snake_case	:Dict=0.0,      snake_case	:Optional[Any]=0.0,      snake_case	:int=0.0_2,      snake_case	:Union[str, Any]=0.0,      snake_case	:Tuple=False,      snake_case	:Optional[int]=True,      snake_case	:Any=1,      snake_case	:Union[str, Any]=0,      snake_case	:Optional[Any]=2,      snake_case	:Union[str, Any]=True,      snake_case	:Tuple=2,      snake_case	:Any=2,      snake_case	:List[Any]=False,      snake_case	:Optional[int]=100,      snake_case	:Union[str, Any]=800,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =d_model
          _lowercase              =encoder_ffn_dim
          _lowercase              =encoder_layers
          _lowercase              =encoder_attention_heads
          _lowercase              =decoder_ffn_dim
          _lowercase              =decoder_layers
          _lowercase              =decoder_attention_heads
          _lowercase              =dropout
          _lowercase              =attention_dropout
          _lowercase              =activation_dropout
          _lowercase              =activation_function
          _lowercase              =init_std
          _lowercase              =encoder_layerdrop
          _lowercase              =decoder_layerdrop
          _lowercase              =classifier_dropout
          _lowercase              =use_cache
          _lowercase              =encoder_layers
          _lowercase              =scale_embedding  # scale factor will be sqrt(d_model) if True
          _lowercase              =use_prompt
          _lowercase              =prompt_length
          _lowercase              =prompt_mid_dim

          super().__init__(
              pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      is_encoder_decoder=snake_case,      decoder_start_token_id=snake_case,      forced_eos_token_id=snake_case,      **snake_case,      )

          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',      snake_case):
             _lowercase              =self.bos_token_id
             warnings.warn(
                 f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                 'The config can simply be saved and uploaded again to be fixed.')


