


"""simple docstring"""

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class    lowerCamelCase__						(		_a    ):
      a :     Dict											=		"""gptj"""
      a :     Any											=		{
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self	:	List[Any]      ,       A_	:	Tuple=5_0_4_0_0      ,       A_	:	str=2_0_4_8      ,       A_	:	str=4_0_9_6      ,       A_	:	Dict=2_8      ,       A_	:	Union[str, Any]=1_6      ,       A_	:	List[str]=6_4      ,       A_	:	Optional[int]=None      ,       A_	:	Optional[Any]="gelu_new"      ,       A_	:	Optional[int]=0.0      ,       A_	:	str=0.0      ,       A_	:	str=0.0      ,       A_	:	Tuple=1e-5      ,       A_	:	List[str]=0.02      ,       A_	:	Any=True      ,       A_	:	int=5_0_2_5_6      ,       A_	:	Optional[Any]=5_0_2_5_6      ,       A_	:	Optional[int]=False      ,       **A_	:	List[Any]      ,       ):






       '''simple docstring'''


       __lowercase									=	vocab_size
       __lowercase									=	n_positions
       __lowercase									=	n_embd
       __lowercase									=	n_layer
       __lowercase									=	n_head
       __lowercase									=	n_inner
       __lowercase									=	rotary_dim
       __lowercase									=	activation_function
       __lowercase									=	resid_pdrop
       __lowercase									=	embd_pdrop
       __lowercase									=	attn_pdrop
       __lowercase									=	layer_norm_epsilon
       __lowercase									=	initializer_range
       __lowercase									=	use_cache

       __lowercase									=	bos_token_id
       __lowercase									=	eos_token_id

       super().__init__(
           bos_token_id=A_      ,       eos_token_id=A_      ,       tie_word_embeddings=A_      ,       **A_							)






class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Union[str, Any]      ,       A_	:	PretrainedConfig      ,       A_	:	str = "default"      ,       A_	:	List[PatchingSpec] = None      ,       A_	:	bool = False      ,       ):






       '''simple docstring'''


       super().__init__(A_      ,       task=A_      ,       patching_specs=A_      ,       use_past=A_							)
       if not getattr(self._config      ,       """pad_token_id"""      ,       A_							):
        # TODO: how to do that better?
        __lowercase									=	0
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}							)
       if self.use_past:
        self.fill_with_past_key_values_(A_      ,       direction="""inputs"""							)
        __lowercase									=	{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowercase									=	{0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       return self._config.n_layer
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       return self._config.n_head
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	PreTrainedTokenizer      ,       A_	:	int = -1      ,       A_	:	int = -1      ,       A_	:	bool = False      ,       A_	:	Optional[TensorType] = None      ,       ):






       '''simple docstring'''


       __lowercase									=	super(A_      ,       self							).generate_dummy_inputs(
           A_      ,       batch_size=A_      ,       seq_length=A_      ,       is_pair=A_      ,       framework=A_							)

       # We need to order the input in the way they appears in the forward()
       __lowercase									=	OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}							)

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
        else:
         import torch

         __lowercase							,		__lowercase									=	common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         __lowercase									=	seqlen + 2
         __lowercase									=	(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         __lowercase									=	[
             (torch.zeros(A_							), torch.zeros(A_							)) for _ in range(self.num_layers							)
         ]

       __lowercase									=	common_inputs["""attention_mask"""]
       if self.use_past:
        __lowercase									=	ordered_inputs["""attention_mask"""].dtype
        __lowercase									=	torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(A_      ,       A_      ,       dtype=A_							)]      ,       dim=1							)

       return ordered_inputs






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       return 1_3




"""simple docstring"""

import os

import numpy
import onnx


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	a.name
 __lowercase									=	b.name

 __lowercase									=	""""""
 __lowercase									=	""""""

 __lowercase									=	a == b

 __lowercase									=	name_a
 __lowercase									=	name_b

 return res


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Dict ):






 """simple docstring"""







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

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


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: str ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: str				,   UpperCamelCase__	: 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				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







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

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

 __lowercase									=	list(model.graph.initializer )

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

 __lowercase									=	0

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

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

    __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: """				,   UpperCamelCase__ )
    total_reduced_size += mem_size

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

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

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

 __lowercase									=	sorted(UpperCamelCase__ )
 _remove_dup_initializers_from_model(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )

 __lowercase									=	"""optimized_""" + model_file_name
 __lowercase									=	os.path.join(UpperCamelCase__				,   UpperCamelCase__ )
 onnx.save(UpperCamelCase__				,   UpperCamelCase__ )

 return new_model
