





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


_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    '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_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Union[str, Any]	 =		"""gpt_neo"""
				__snake_case   :       Optional[int]	 =		["""past_key_values"""]
				__snake_case   :       Optional[int]	 =		{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




				def __init__(     self  :Any						,    __lowercase  :Optional[Any]=5_0257						,    __lowercase  :Tuple=2048						,    __lowercase  :Optional[Any]=2048						,    __lowercase  :Any=24						,    __lowercase  :Optional[Any]=[[["global", "local"], 12]]						,    __lowercase  :List[Any]=16						,    __lowercase  :Optional[int]=None						,    __lowercase  :List[str]=256						,    __lowercase  :List[str]="gelu_new"						,    __lowercase  :Union[str, Any]=0.0						,    __lowercase  :str=0.0						,    __lowercase  :int=0.0						,    __lowercase  :List[str]=0.1						,    __lowercase  :Union[str, Any]=1e-5						,    __lowercase  :List[Any]=0.02						,    __lowercase  :Dict=True						,    __lowercase  :str=5_0256						,    __lowercase  :str=5_0256						,    **__lowercase  :Any						,    ):
											__lowerCamelCase      :      Dict         =vocab_size
											__lowerCamelCase      :      Tuple         =max_position_embeddings
											__lowerCamelCase      :      Dict         =hidden_size
											__lowerCamelCase      :      Optional[Any]         =num_layers
											__lowerCamelCase      :      str         =num_heads
											__lowerCamelCase      :      Dict         =intermediate_size
											__lowerCamelCase      :      List[Any]         =window_size
											__lowerCamelCase      :      int         =activation_function
											__lowerCamelCase      :      Any         =resid_dropout
											__lowerCamelCase      :      str         =embed_dropout
											__lowerCamelCase      :      Optional[Any]         =attention_dropout
											__lowerCamelCase      :      Optional[Any]         =classifier_dropout
											__lowerCamelCase      :      Optional[Any]         =layer_norm_epsilon
											__lowerCamelCase      :      Union[str, Any]         =initializer_range
											__lowerCamelCase      :      Dict         =use_cache

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

											__lowerCamelCase      :      str         =attention_types
											__lowerCamelCase      :      int         =self.expand_attention_types_params(__lowercase			)

											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=__lowercase						,    eos_token_id=__lowercase						,    **__lowercase			)




				@staticmethod
				def 					__lowercase  (     __lowercase  :Optional[int]			):
											__lowerCamelCase      :      List[Any]         =[]
											for item in attention_types:
																		for _ in range(item[1]			):
																									attentions.extend(item[0]			)
											return attentions



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							import torch

							__lowerCamelCase      :      Optional[Any]         =input.size()
							__lowerCamelCase      :      Tuple         =len(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      List[Any]         =shape[dimension]

							__lowerCamelCase      :      Optional[Any]         =torch.arange(0    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =torch.div(sizedim - size    ,						SCREAMING_SNAKE_CASE    ,						rounding_mode='''floor'''   ) + 1
							__lowerCamelCase      :      Tuple         =torch.arange(SCREAMING_SNAKE_CASE   ) + low_indices[:min_length][:, None]

							__lowerCamelCase      :      int         =[slice(SCREAMING_SNAKE_CASE   )] * rank
							__lowerCamelCase      :      List[Any]         =indices
							__lowerCamelCase      :      Dict         =input[s]

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

							return sliced.permute(SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							import torch

							__lowerCamelCase      :      Tuple         =torch.arange(1    ,						SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[int]         =torch.remainder(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Any         =remainders == 0
							__lowerCamelCase      :      Optional[int]         =candidates[divisor_indices]
							__lowerCamelCase      :      List[str]         =torch.max(SCREAMING_SNAKE_CASE   )
							return largest_divisor, torch.div(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						rounding_mode='''floor'''   )
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				@property
				def 					__lowercase  (     self  :List[Any]			):
											__lowerCamelCase      :      List[str]         =OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}			)
											if self.use_past:
																		self.fill_with_past_key_values_(__lowercase						,    direction='''inputs'''			)
																		__lowerCamelCase      :      str         ={0: '''batch''', 1: '''past_sequence + sequence'''}
											else:
																		__lowerCamelCase      :      Union[str, Any]         ={0: '''batch''', 1: '''sequence'''}

											return common_inputs




				@property
				def 					__lowercase  (     self  :List[Any]			):
											return self._config.num_heads




				def 					__lowercase  (     self  :Any						,    __lowercase  :PreTrainedTokenizer						,    __lowercase  :int = -1						,    __lowercase  :int = -1						,    __lowercase  :bool = False						,    __lowercase  :Optional[TensorType] = None						,    ):
											__lowerCamelCase      :      Dict         =super(__lowercase						,    self			).generate_dummy_inputs(
											    __lowercase						,    batch_size=__lowercase						,    seq_length=__lowercase						,    is_pair=__lowercase						,    framework=__lowercase			)

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

											__lowerCamelCase      :      Optional[Any]         =common_inputs['''attention_mask''']
											if self.use_past:
																		__lowerCamelCase      :      str         =ordered_inputs['''attention_mask'''].dtype
																		__lowerCamelCase      :      Any         =torch.cat(
																		    [ordered_inputs['''attention_mask'''], torch.ones(__lowercase						,    __lowercase						,    dtype=__lowercase			)]						,    dim=1			)

											return ordered_inputs




				@property
				def 					__lowercase  (     self  :Tuple			):
											return 13











"""simple docstring"""





def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =len(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =[[0] * n for i in range(SCREAMING_SNAKE_CASE   )]
							for i in range(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      Union[str, Any]         =y_points[i]

							for i in range(2    ,						SCREAMING_SNAKE_CASE   ):
														for j in range(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =(
																					    (xa - x_points[j - i + 1]) * q[j][i - 1]
																					    - (xa - x_points[j]) * q[j - 1][i - 1]
																					) / (x_points[j] - x_points[j - i + 1])

							return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




