





'''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


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {
    '''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 							__lowercase       (      __lowerCamelCase						):







	'''simple docstring'''

	a    :				Optional[Any]											=       '''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     ,_lowerCamelCase=50400     ,_lowerCamelCase=2048     ,_lowerCamelCase=4096     ,_lowerCamelCase=28     ,_lowerCamelCase=16     ,_lowerCamelCase=64     ,_lowerCamelCase=None     ,_lowerCamelCase="gelu_new"     ,_lowerCamelCase=0.0     ,_lowerCamelCase=0.0     ,_lowerCamelCase=0.0     ,_lowerCamelCase=1E-5     ,_lowerCamelCase=0.0_2     ,_lowerCamelCase=True     ,_lowerCamelCase=50256     ,_lowerCamelCase=50256     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							List[str]:







				'''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 							__lowercase       (      __lowerCamelCase						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase = "default"     ,_lowerCamelCase = None     ,_lowerCamelCase = False     ,)							->							str:







				'''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 						_UpperCAmelCase     (self )							->							Any:







				'''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 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				return self._config.n_layer


	@property
	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				return self._config.n_head


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = -1     ,_lowerCamelCase = -1     ,_lowerCamelCase = False     ,_lowerCamelCase = None     ,)							->							Optional[Any]:







				'''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										=							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 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				return 13












'''simple docstring'''



from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			__lowercase										=							(low + high) // 2
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_subarray(lowerCamelCase_		,       mid + 1		,       lowerCamelCase_				)
			__lowercase						,		__lowercase						,		__lowercase										=							max_cross_sum(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum




def    _lowerCAmelCase						(				lowerCamelCase_			:  Sequence[float]		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  int				):
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1
			__lowercase						,		__lowercase										=							float('''-inf'''				), -1

			__lowercase										=							0
			for i in range(lowerCamelCase_		,       low - 1		,       -1				):
						summ += arr[i]
						if summ > left_sum:
									__lowercase										=							summ
									__lowercase										=							i

			__lowercase										=							0
			for i in range(mid + 1		,       high + 1				):
						summ += arr[i]
						if summ > right_sum:
									__lowercase										=							summ
									__lowercase										=							i

			return max_left, max_right, (left_sum + right_sum)




def    _lowerCAmelCase						(				lowerCamelCase_			:  int				):
			__lowercase										=							[randint(1		,       lowerCamelCase_				) for _ in range(lowerCamelCase_				)]
			__lowercase										=							time.time()
			max_subarray(lowerCamelCase_		,       0		,       input_size - 1				)
			__lowercase										=							time.time()
			return end - start




def    _lowerCAmelCase						(				):
			__lowercase										=							[1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
			__lowercase										=							[time_max_subarray(lowerCamelCase_				) for input_size in input_sizes]
			print('''No of Inputs\t\tTime Taken'''				)
			for input_size, runtime in zip(lowerCamelCase_		,       lowerCamelCase_				):
						print(lowerCamelCase_		,       '''\t\t'''		,       lowerCamelCase_				)
			plt.plot(lowerCamelCase_		,       lowerCamelCase_				)
			plt.xlabel('''Number of Inputs'''				)
			plt.ylabel('''Time taken in seconds'''				)
			plt.show()


if __name__ == "__main__":
	from doctest import testmod

	testmod()





