





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   						=		{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE_    (       __a       ):
						__magic_name__:      str			   =							"gpt2"
						__magic_name__:      Any			   =							["past_key_values"]
						__magic_name__:      Optional[int]			   =							{
						    "hidden_size": "n_embd",
						    "max_position_embeddings": "n_positions",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}






						def __init__(   self   :   Optional[Any]		, _A   :   int=50257		, _A   :   List[Any]=1024		, _A   :   str=768		, _A   :   Tuple=12		, _A   :   List[Any]=12		, _A   :   Tuple=None		, _A   :   List[Any]="gelu_new"		, _A   :   List[str]=0.1		, _A   :   Union[str, Any]=0.1		, _A   :   Optional[int]=0.1		, _A   :   Dict=1E-5		, _A   :   Tuple=0.0_2		, _A   :   Tuple="cls_index"		, _A   :   Tuple=True		, _A   :   int=None		, _A   :   Tuple=True		, _A   :   List[Any]=0.1		, _A   :   int=True		, _A   :   Any=True		, _A   :   Tuple=50256		, _A   :   List[Any]=50256		, _A   :   Optional[int]=False		, _A   :   Dict=False		, **_A   :   Optional[int]		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      Union[str, Any]           =						n_positions
							snake_case_		:      List[Any]           =						n_embd
							snake_case_		:      int           =						n_layer
							snake_case_		:      str           =						n_head
							snake_case_		:      Any           =						n_inner
							snake_case_		:      Tuple           =						activation_function
							snake_case_		:      str           =						resid_pdrop
							snake_case_		:      Optional[Any]           =						embd_pdrop
							snake_case_		:      Dict           =						attn_pdrop
							snake_case_		:      Dict           =						layer_norm_epsilon
							snake_case_		:      Dict           =						initializer_range
							snake_case_		:      Any           =						summary_type
							snake_case_		:      List[Any]           =						summary_use_proj
							snake_case_		:      List[str]           =						summary_activation
							snake_case_		:      Any           =						summary_first_dropout
							snake_case_		:      Any           =						summary_proj_to_labels
							snake_case_		:      str           =						scale_attn_weights
							snake_case_		:      Tuple           =						use_cache
							snake_case_		:      Union[str, Any]           =						scale_attn_by_inverse_layer_idx
							snake_case_		:      Optional[Any]           =						reorder_and_upcast_attn

							snake_case_		:      Tuple           =						bos_token_id
							snake_case_		:      Optional[Any]           =						eos_token_id

							super().__init__(bos_token_id=UpperCamelCase__		, eos_token_id=UpperCamelCase__		, **UpperCamelCase__						)
class SCREAMING_SNAKE_CASE_    (       __a       ):






						def __init__(   self   :   int		, _A   :   PretrainedConfig		, _A   :   str = "default"		, _A   :   List[PatchingSpec] = None		, _A   :   bool = False		, )				->     str:







							"""simple docstring"""


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






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







							"""simple docstring"""


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

							return common_inputs






						@property
						def UpperCAmelCase_					(   self   :   Any						)				->     int:







							"""simple docstring"""


							return self._config.n_layer






						@property
						def UpperCAmelCase_					(   self   :   int						)				->     int:







							"""simple docstring"""


							return self._config.n_head






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   PreTrainedTokenizer		, _A   :   int = -1		, _A   :   int = -1		, _A   :   bool = False		, _A   :   Optional[TensorType] = None		, )				->     Mapping[str, Any]:







							"""simple docstring"""


							snake_case_		:      Any           =						super(UpperCamelCase__		, self						).generate_dummy_inputs(
							    UpperCamelCase__		, batch_size=UpperCamelCase__		, seq_length=UpperCamelCase__		, is_pair=UpperCamelCase__		, framework=UpperCamelCase__						)

							# We need to order the input in the way they appears in the forward()
							snake_case_		:      Union[str, 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

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

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

							return ordered_inputs






						@property
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     int:







							"""simple docstring"""


							return 13


"""simple docstring"""

def    lowerCAmelCase			(__UpperCamelCase							:    list							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							for index, item in enumerate(__UpperCamelCase			):
														if item == target:
																					return index
							return -1


def    lowerCAmelCase			(__UpperCamelCase							:    list							,					__UpperCamelCase							:    int							,					__UpperCamelCase							:    int							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							if not (0 <= high < len(__UpperCamelCase			) and 0 <= low < len(__UpperCamelCase			)):
														raise Exception('''Invalid upper or lower bound!'''			)
							if high < low:
														return -1
							if sequence[low] == target:
														return low
							if sequence[high] == target:
														return high
							return rec_linear_search(__UpperCamelCase							,					low + 1							,					high - 1							,					__UpperCamelCase			)


if __name__ == "__main__":
				__lowercase    =				input('''Enter numbers separated by comma:\n''').strip()
				__lowercase    =				[int(item.strip()) for item in user_input.split(''',''')]

				__lowercase    =				int(input('''Enter a single number to be found in the list:\n''').strip())
				__lowercase    =				linear_search(sequence, target)
				if result != -1:
								print(f'''linear_search({sequence}, {target}) = {result}''')
				else:
								print(f'''{target} was not found in {sequence}''')



