



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


lowercase_	       =			logging.get_logger(__name__)

lowercase_	       =			{
    '''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 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "gpt_neo"
	SCREAMING_SNAKE_CASE       =       ["past_key_values"]
	SCREAMING_SNAKE_CASE       =       {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



	def __init__(						self							:    Any				,	snake_case_							:    Optional[int]=5_02_57				,	snake_case_							:    str=20_48				,	snake_case_							:    Union[str, Any]=20_48				,	snake_case_							:    Optional[Any]=24				,	snake_case_							:    int=[[["global", "local"], 12]]				,	snake_case_							:    List[Any]=16				,	snake_case_							:    Optional[Any]=None				,	snake_case_							:    Tuple=2_56				,	snake_case_							:    List[Any]="gelu_new"				,	snake_case_							:    str=0.0				,	snake_case_							:    Dict=0.0				,	snake_case_							:    Optional[Any]=0.0				,	snake_case_							:    List[Any]=0.1				,	snake_case_							:    Union[str, Any]=1e-5				,	snake_case_							:    Any=0.0_2				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    Union[str, Any]=5_02_56				,	snake_case_							:    Optional[Any]=5_02_56				,	**snake_case_							:    int				,	)->							str:
						__lowerCAmelCase	   =vocab_size
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =hidden_size
						__lowerCAmelCase	   =num_layers
						__lowerCAmelCase	   =num_heads
						__lowerCAmelCase	   =intermediate_size
						__lowerCAmelCase	   =window_size
						__lowerCAmelCase	   =activation_function
						__lowerCAmelCase	   =resid_dropout
						__lowerCAmelCase	   =embed_dropout
						__lowerCAmelCase	   =attention_dropout
						__lowerCAmelCase	   =classifier_dropout
						__lowerCAmelCase	   =layer_norm_epsilon
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =use_cache

						__lowerCAmelCase	   =bos_token_id
						__lowerCAmelCase	   =eos_token_id

						__lowerCAmelCase	   =attention_types
						__lowerCAmelCase	   =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_							:    Optional[Any])->							Dict:
						__lowerCAmelCase	   =[]
						for item in attention_types:
											for _ in range(item[1]):
																attentions.extend(item[0])
						return attentions






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    Union[str, Any]	)  ->						Optional[Any]:
					import torch

					__lowerCAmelCase	   =input.size()
					__lowerCAmelCase	   =len(__lowerCamelCase	)
					__lowerCAmelCase	   =shape[dimension]

					__lowerCAmelCase	   =torch.arange(0				,   __lowerCamelCase				,   __lowerCamelCase	)
					__lowerCAmelCase	   =torch.div(sizedim - size				,   __lowerCamelCase				,   rounding_mode="""floor"""	) + 1
					__lowerCAmelCase	   =torch.arange(__lowerCamelCase	) + low_indices[:min_length][:, None]

					__lowerCAmelCase	   =[slice(__lowerCamelCase	)] * rank
					__lowerCAmelCase	   =indices
					__lowerCAmelCase	   =input[s]

					__lowerCAmelCase	   =list(range(0				,   rank + 1	)	)
					perm.append(perm.pop(dimension + 1	)	)

					return sliced.permute(__lowerCamelCase	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple				,   __lowerCamelCase	:    Union[str, Any]	)  ->						List[Any]:
					import torch

					__lowerCAmelCase	   =torch.arange(1				,   __lowerCamelCase	)
					__lowerCAmelCase	   =torch.remainder(__lowerCamelCase				,   __lowerCamelCase	)
					__lowerCAmelCase	   =remainders == 0
					__lowerCAmelCase	   =candidates[divisor_indices]
					__lowerCAmelCase	   =torch.max(__lowerCamelCase	)
					return largest_divisor, torch.div(__lowerCamelCase				,   __lowerCamelCase				,   rounding_mode="""floor"""	)






class 						__a (     SCREAMING_SNAKE_CASE				):



	@property
	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Mapping[str, Mapping[int, str]]:
						__lowerCAmelCase	   =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}})
						if self.use_past:
											self.fill_with_past_key_values_(snake_case_				,	direction="""inputs""")
											__lowerCAmelCase	   ={0: """batch""", 1: """past_sequence + sequence"""}
						else:
											__lowerCAmelCase	   ={0: """batch""", 1: """sequence"""}

						return common_inputs



	@property
	def 		UpperCamelCase							(						self							:    Dict)->							int:
						return self._config.num_heads



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

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

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

						return ordered_inputs



	@property
	def 		UpperCamelCase							(						self							:    Optional[int])->							int:
						return 13









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						None:
					__lowerCAmelCase	   =generate_pascal_triangle(__lowerCamelCase	)
					for row_idx in range(__lowerCamelCase	):
										# Print left spaces
										for _ in range(num_rows - row_idx - 1	):
															print(end=""" """	)
										# Print row values
										for col_idx in range(row_idx + 1	):
															if col_idx != row_idx:
																				print(triangle[row_idx][col_idx]				,   end=""" """	)
															else:
																				print(triangle[row_idx][col_idx]				,   end=""""""	)
										print()






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list[list[int]]:

					if not isinstance(__lowerCamelCase				,   __lowerCamelCase	):
										raise TypeError("""The input value of 'num_rows' should be 'int'"""	)

					if num_rows == 0:
										return []
					elif num_rows < 0:
										raise ValueError(
										    """The input value of 'num_rows' should be greater than or equal to 0"""	)

					__lowerCAmelCase	   =[]
					for current_row_idx in range(__lowerCamelCase	):
										__lowerCAmelCase	   =populate_current_row(__lowerCamelCase				,   __lowerCamelCase	)
										triangle.append(__lowerCamelCase	)
					return triangle






def 					__lowerCAmelCase		(  __lowerCamelCase	:    list[list[int]]				,   __lowerCamelCase	:    int	)  ->						list[int]:
					__lowerCAmelCase	   =[-1] * (current_row_idx + 1)
					# first and last elements of current row are equal to 1
					__lowerCAmelCase   ,	__lowerCAmelCase	   =1, 1
					for current_col_idx in range(1				,   __lowerCamelCase	):
										calculate_current_element(
										    __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)
					return current_row






def 					__lowerCAmelCase		(  __lowerCamelCase	:    list[list[int]]				,   __lowerCamelCase	:    list[int]				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    int				,   )  ->						None:
					__lowerCAmelCase	   =triangle[current_row_idx - 1][current_col_idx - 1]
					__lowerCAmelCase	   =triangle[current_row_idx - 1][current_col_idx]
					__lowerCAmelCase	   =above_to_left_elt + above_to_right_elt






def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list[list[int]]:

					if not isinstance(__lowerCamelCase				,   __lowerCamelCase	):
										raise TypeError("""The input value of 'num_rows' should be 'int'"""	)

					if num_rows == 0:
										return []
					elif num_rows < 0:
										raise ValueError(
										    """The input value of 'num_rows' should be greater than or equal to 0"""	)

					__lowerCAmelCase	   =[[1]]

					for row_index in range(1				,   __lowerCamelCase	):
										__lowerCAmelCase	   =[0] + result[-1] + [0]
										__lowerCAmelCase	   =row_index + 1
										# Calculate the number of distinct elements in a row
										__lowerCAmelCase	   =sum(divmod(__lowerCamelCase				,   2	)	)
										__lowerCAmelCase	   =[
										    temp_row[i - 1] + temp_row[i] for i in range(1				,   distinct_elements + 1	)
										]
										__lowerCAmelCase	   =row_first_half[: (row_index + 1) // 2]
										row_second_half.reverse()
										__lowerCAmelCase	   =row_first_half + row_second_half
										result.append(__lowerCamelCase	)

					return result






def 					__lowerCAmelCase		(  )  ->						None:
					from collections.abc import Callable
					from timeit import timeit

					def benchmark_a_function(__lowerCamelCase	:    Callable				,   __lowerCamelCase	:    int	) -> None:
										__lowerCAmelCase	   =f"""{func.__name__}({value})"""
										__lowerCAmelCase	   =timeit(f"""__main__.{call}"""				,   setup="""import __main__"""	)
										# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
										print(f"""{call:38} -- {timing:.4f} seconds"""	)

					for value in range(15	):  # (1, 7, 14):
										for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
															benchmark_a_function(__lowerCamelCase				,   __lowerCamelCase	)
										print()


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				benchmark()




