'''simple docstring'''


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

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


snake_case__					:      List[Any]				      =  logging.get_logger(__name__)

snake_case__					:      Any				      =  {
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:str						   =		'''gpt_neo'''
  lowerCamelCase_		:int						   =		['''past_key_values''']
  lowerCamelCase_		:Tuple						   =		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


  def __init__(			self				,		snake_case_=5_0_2_5_7				,		snake_case_=2_0_4_8				,		snake_case_=2_0_4_8				,		snake_case_=2_4				,		snake_case_=[[["global", "local"], 1_2]]				,		snake_case_=1_6				,		snake_case_=None				,		snake_case_=2_5_6				,		snake_case_="gelu_new"				,		snake_case_=0.0				,		snake_case_=0.0				,		snake_case_=0.0				,		snake_case_=0.1				,		snake_case_=1E-5				,		snake_case_=0.02				,		snake_case_=True				,		snake_case_=5_0_2_5_6				,		snake_case_=5_0_2_5_6				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				vocab_size
       UpperCAmelCase_							:       Optional[Any]       =				max_position_embeddings
       UpperCAmelCase_							:       Any       =				hidden_size
       UpperCAmelCase_							:       Optional[int]       =				num_layers
       UpperCAmelCase_							:       Union[str, Any]       =				num_heads
       UpperCAmelCase_							:       Union[str, Any]       =				intermediate_size
       UpperCAmelCase_							:       Tuple       =				window_size
       UpperCAmelCase_							:       Dict       =				activation_function
       UpperCAmelCase_							:       Dict       =				resid_dropout
       UpperCAmelCase_							:       Optional[Any]       =				embed_dropout
       UpperCAmelCase_							:       List[str]       =				attention_dropout
       UpperCAmelCase_							:       List[Any]       =				classifier_dropout
       UpperCAmelCase_							:       Dict       =				layer_norm_epsilon
       UpperCAmelCase_							:       Union[str, Any]       =				initializer_range
       UpperCAmelCase_							:       Optional[int]       =				use_cache

       UpperCAmelCase_							:       Any       =				bos_token_id
       UpperCAmelCase_							:       List[Any]       =				eos_token_id

       UpperCAmelCase_							:       Any       =				attention_types
       UpperCAmelCase_							:       List[Any]       =				self.expand_attention_types_params(snake_case_						)

       if len(self.attention_layers						) != self.num_layers:
            raise ValueError(
                'Configuration for convolutional module is incorrect. '
                'It is required that `len(config.attention_layers)` == `config.num_layers` '
                F'''but is `len(config.attention_layers) = {len(self.attention_layers						)}`, '''
                F'''`config.num_layers = {self.num_layers}`. '''
                '`config.attention_layers` is prepared using `config.attention_types`. '
                'Please verify the value of `config.attention_types` argument.'						)

       super().__init__(bos_token_id=snake_case_				,		eos_token_id=snake_case_				,		**snake_case_						)






  @staticmethod
  def _UpperCamelCase					(			snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				[]
       for item in attention_types:
            for _ in range(item[1]						):
                 attentions.extend(item[0]						)
       return attentions







def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							Union[str, Any]     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Tuple       ):



     """simple docstring"""





     import torch

     UpperCAmelCase_							:       int       =				input.size()
     UpperCAmelCase_							:       List[Any]       =				len(lowerCamelCase_       )
     UpperCAmelCase_							:       List[str]       =				shape[dimension]

     UpperCAmelCase_							:       Optional[int]       =				torch.arange(0     ,					lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       Union[str, Any]       =				torch.div(sizedim - size     ,					lowerCamelCase_     ,					rounding_mode='floor'       ) + 1
     UpperCAmelCase_							:       Dict       =				torch.arange(lowerCamelCase_       ) + low_indices[:min_length][:, None]

     UpperCAmelCase_							:       Tuple       =				[slice(lowerCamelCase_       )] * rank
     UpperCAmelCase_							:       int       =				indices
     UpperCAmelCase_							:       List[str]       =				input[s]

     UpperCAmelCase_							:       Tuple       =				list(range(0     ,					rank + 1       )       )
     perm.append(perm.pop(dimension + 1       )       )

     return sliced.permute(lowerCamelCase_       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							Dict       ):



     """simple docstring"""





     import torch

     UpperCAmelCase_							:       Optional[Any]       =				torch.arange(1     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       str       =				torch.remainder(lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       List[Any]       =				remainders == 0
     UpperCAmelCase_							:       Any       =				candidates[divisor_indices]
     UpperCAmelCase_							:       Optional[int]       =				torch.max(lowerCamelCase_       )
     return largest_divisor, torch.div(lowerCamelCase_     ,					lowerCamelCase_     ,					rounding_mode='floor'       )






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}						)
       if self.use_past:
            self.fill_with_past_key_values_(snake_case_				,		direction='inputs'						)
            UpperCAmelCase_							:       Dict       =				{0: 'batch', 1: 'past_sequence + sequence'}
       else:
            UpperCAmelCase_							:       Any       =				{0: 'batch', 1: 'sequence'}

       return common_inputs


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._config.num_heads


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = -1				,		snake_case_ = -1				,		snake_case_ = False				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				super(snake_case_				,		self						).generate_dummy_inputs(
           snake_case_				,		batch_size=snake_case_				,		seq_length=snake_case_				,		is_pair=snake_case_				,		framework=snake_case_						)

       # We need to order the input in the way they appears in the forward()
       UpperCAmelCase_							:       Any       =				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

                 UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				common_inputs['input_ids'].shape
                 # Not using the same length for past_key_values
                 UpperCAmelCase_							:       int       =				seqlen + 2
                 UpperCAmelCase_							:       Optional[int]       =				(
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 UpperCAmelCase_							:       List[str]       =				[
                     (torch.zeros(snake_case_						), torch.zeros(snake_case_						)) for _ in range(self.num_layers						)
                 ]

       UpperCAmelCase_							:       Dict       =				common_inputs['attention_mask']
       if self.use_past:
            UpperCAmelCase_							:       List[Any]       =				ordered_inputs['attention_mask'].dtype
            UpperCAmelCase_							:       List[Any]       =				torch.cat(
                [ordered_inputs['attention_mask'], torch.ones(snake_case_				,		snake_case_				,		dtype=snake_case_						)]				,		dim=1						)

       return ordered_inputs






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return 1_3







'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


snake_case__					:      Optional[int]				      =  '''1'''
snake_case__					:      str				      =  '''0'''
snake_case__					:      List[str]				      =  '''1'''

snake_case__					:      List[str]				      =  ort.SessionOptions()
snake_case__					:      str				      =  ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case__					:      Dict				      =  ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case__					:      Dict				      =  ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case__					:      str				      =  ort.RunOptions()

snake_case__					:      List[Any]				      =  128
snake_case__					:      Union[str, Any]				      =  1
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Union[str, Any]				      =  np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
snake_case__					:      Union[str, Any]				      =  time.time()
snake_case__					:      str				      =  2000
snake_case__					:      Tuple				      =  {}
for iter in range(max_iters):
 snake_case__					:      str				      =  sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))






