
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


lowercase__							:							List[str]									=			logging.get_logger(__name__)

lowercase__							:							List[str]									=			{
    "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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'gptj'
							_snake_case     		=  {
							    'max_position_embeddings': 'n_positions',
							    'hidden_size': 'n_embd',
							    'num_attention_heads': 'n_head',
							    'num_hidden_layers': 'n_layer',
							}



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=50400     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=4096     ,	SCREAMING_SNAKE_CASE_=28     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=64     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="gelu_new"     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=50256     ,	SCREAMING_SNAKE_CASE_=50256     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Optional[int]:





													'''simple docstring'''






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

													__UpperCamelCase       =			bos_token_id
													__UpperCamelCase       =			eos_token_id

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = "default"     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	)->   int:





													'''simple docstring'''






													super().__init__(SCREAMING_SNAKE_CASE_     ,	task=SCREAMING_SNAKE_CASE_     ,	patching_specs=SCREAMING_SNAKE_CASE_     ,	use_past=SCREAMING_SNAKE_CASE_			)
													if not getattr(self._config     ,	'''pad_token_id'''     ,	SCREAMING_SNAKE_CASE_			):
																			# TODO: how to do that better?
																			__UpperCamelCase       =			0



							@property
							def     A__		(  self			)->   Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






													__UpperCamelCase       =			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
													if self.use_past:
																			self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_     ,	direction='''inputs'''			)
																			__UpperCamelCase       =			{0: '''batch''', 1: '''past_sequence + sequence'''}
													else:
																			__UpperCamelCase       =			{0: '''batch''', 1: '''sequence'''}

													return common_inputs



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self._config.n_layer



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self._config.n_head



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)->   Mapping[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			super(SCREAMING_SNAKE_CASE_     ,	self			).generate_dummy_inputs(
													    SCREAMING_SNAKE_CASE_     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	seq_length=SCREAMING_SNAKE_CASE_     ,	is_pair=SCREAMING_SNAKE_CASE_     ,	framework=SCREAMING_SNAKE_CASE_			)

													# We need to order the input in the way they appears in the forward()
													__UpperCamelCase       =			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       =			common_inputs['''input_ids'''].shape
																									# Not using the same length for past_key_values
																									__UpperCamelCase       =			seqlen + 2
																									__UpperCamelCase       =			(
																									    batch,
																									    self.num_attention_heads,
																									    past_key_values_length,
																									    self._config.hidden_size // self.num_attention_heads,
																									)
																									__UpperCamelCase       =			[
																									    (torch.zeros(SCREAMING_SNAKE_CASE_			), torch.zeros(SCREAMING_SNAKE_CASE_			)) for _ in range(self.num_layers			)
																									]

													__UpperCamelCase       =			common_inputs['''attention_mask''']
													if self.use_past:
																			__UpperCamelCase       =			ordered_inputs['''attention_mask'''].dtype
																			__UpperCamelCase       =			torch.cat(
																			    [ordered_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	dtype=SCREAMING_SNAKE_CASE_			)]     ,	dim=1			)

													return ordered_inputs



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 13






def 	A_ (							snake_case							:   int = 10 ,      snake_case							:   int = 1000 ,      snake_case							:   bool = True )      ->    int:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						    and isinstance(snake_case ,      snake_case )
						), "Invalid type of value(s) specified to function!"

						if min_val > max_val:
												raise ValueError('''Invalid value for min_val or max_val (min_value < max_value)''' )
						return min_val if option else max_val




def 	A_ (							snake_case							:   int ,      snake_case							:   int )      ->    int:



						'''simple docstring'''

						return int((number_a + number_a) / 2 )




def 	A_ (							snake_case							:   int ,      snake_case							:   int ,      snake_case							:   int )      ->    None:



						'''simple docstring'''

						assert (
						    isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case ) and isinstance(snake_case ,      snake_case )
						), 'argument values must be type of "int"'

						if lower > higher:
												raise ValueError('''argument value for lower and higher must be(lower > higher)''' )

						if not lower < to_guess < higher:
												raise ValueError(
												    '''guess value must be within the range of lower and higher value''' )

						def answer(snake_case							:   int ) -> str:
												if number > to_guess:
																		return "high"
												elif number < to_guess:
																		return "low"
												else:
																		return "same"

						print('''started...''' )

						__UpperCamelCase       =			lower
						__UpperCamelCase       =			higher

						__UpperCamelCase       =			[]

						while True:
												__UpperCamelCase       =			get_avg(snake_case ,      snake_case )
												last_numbers.append(snake_case )

												if answer(snake_case ) == "low":
																		__UpperCamelCase       =			number
												elif answer(snake_case ) == "high":
																		__UpperCamelCase       =			number
												else:
																		break

						print(f"guess the number : {last_numbers[-1]}" )
						print(f"details : {last_numbers!s}" )




def 	A_ (							)      ->    None:



						'''simple docstring'''

						__UpperCamelCase       =			int(input('''Enter lower value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter high value : ''' ).strip() )
						__UpperCamelCase       =			int(input('''Enter value to guess : ''' ).strip() )
						guess_the_number(snake_case ,      snake_case ,      snake_case )


if __name__ == "__main__":
				main()




