




"""simple docstring"""






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 lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""gpt_neo"""
			lowerCamelCase__      =			["""past_key_values"""]
			lowerCamelCase__      =			{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
			def __init__(  self  ,      lowercase=50257  ,      lowercase=2048  ,      lowercase=2048  ,      lowercase=24  ,      lowercase=[[["global", "local"], 12]]  ,      lowercase=16  ,      lowercase=None  ,      lowercase=256  ,      lowercase="gelu_new"  ,      lowercase=0.0  ,      lowercase=0.0  ,      lowercase=0.0  ,      lowercase=0.1  ,      lowercase=1E-5  ,      lowercase=0.02  ,      lowercase=True  ,      lowercase=50256  ,      lowercase=50256  ,      **lowercase  ,      ):
							_lowerCamelCase     :			str    =    vocab_size
							_lowerCamelCase     :			Union[str, Any]    =    max_position_embeddings
							_lowerCamelCase     :			Optional[Any]    =    hidden_size
							_lowerCamelCase     :			List[str]    =    num_layers
							_lowerCamelCase     :			Optional[Any]    =    num_heads
							_lowerCamelCase     :			Tuple    =    intermediate_size
							_lowerCamelCase     :			Optional[int]    =    window_size
							_lowerCamelCase     :			int    =    activation_function
							_lowerCamelCase     :			Optional[Any]    =    resid_dropout
							_lowerCamelCase     :			Any    =    embed_dropout
							_lowerCamelCase     :			Any    =    attention_dropout
							_lowerCamelCase     :			Optional[int]    =    classifier_dropout
							_lowerCamelCase     :			List[str]    =    layer_norm_epsilon
							_lowerCamelCase     :			Dict    =    initializer_range
							_lowerCamelCase     :			Optional[int]    =    use_cache

							_lowerCamelCase     :			List[str]    =    bos_token_id
							_lowerCamelCase     :			int    =    eos_token_id

							_lowerCamelCase     :			int    =    attention_types
							_lowerCamelCase     :			List[str]    =    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 					A_    (  lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    []
							for item in attention_types:
											for _ in range(item[1]  ):
															attentions.extend(item[0]  )
							return attentions







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				import torch

				_lowerCamelCase     :			Tuple    =    input.size()
				_lowerCamelCase     :			Tuple    =    len(lowercase__		)
				_lowerCamelCase     :			Tuple    =    shape[dimension]

				_lowerCamelCase     :			Any    =    torch.arange(0							,    lowercase__							,    lowercase__		)
				_lowerCamelCase     :			Optional[int]    =    torch.div(sizedim - size							,    lowercase__							,    rounding_mode='floor'		) + 1
				_lowerCamelCase     :			List[str]    =    torch.arange(lowercase__		) + low_indices[:min_length][:, None]

				_lowerCamelCase     :			Dict    =    [slice(lowercase__		)] * rank
				_lowerCamelCase     :			Dict    =    indices
				_lowerCamelCase     :			List[Any]    =    input[s]

				_lowerCamelCase     :			List[Any]    =    list(range(0							,    rank + 1		)		)
				perm.append(perm.pop(dimension + 1		)		)

				return sliced.permute(lowercase__		)







def      _snake_case			(				lowercase__							,    lowercase__		):
				import torch

				_lowerCamelCase     :			List[str]    =    torch.arange(1							,    lowercase__		)
				_lowerCamelCase     :			str    =    torch.remainder(lowercase__							,    lowercase__		)
				_lowerCamelCase     :			Tuple    =    remainders == 0
				_lowerCamelCase     :			Dict    =    candidates[divisor_indices]
				_lowerCamelCase     :			Union[str, Any]    =    torch.max(lowercase__		)
				return largest_divisor, torch.div(lowercase__							,    lowercase__							,    rounding_mode='floor'		)






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			@property
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
							if self.use_past:
											self.fill_with_past_key_values_(lowercase  ,      direction='inputs'  )
											_lowerCamelCase     :			Optional[int]    =    {0: 'batch', 1: 'past_sequence + sequence'}
							else:
											_lowerCamelCase     :			Dict    =    {0: 'batch', 1: 'sequence'}

							return common_inputs
			@property
			def 					A_    (  self  ):
							return self._config.num_heads
			def 					A_    (  self  ,      lowercase  ,      lowercase = -1  ,      lowercase = -1  ,      lowercase = False  ,      lowercase = None  ,      ):
							_lowerCamelCase     :			Any    =    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     :			Dict    =    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     :			List[str]    =    common_inputs['input_ids'].shape
															# Not using the same length for past_key_values
															_lowerCamelCase     :			Union[str, Any]    =    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     :			List[Any]    =    common_inputs['attention_mask']
							if self.use_past:
											_lowerCamelCase     :			int    =    ordered_inputs['attention_mask'].dtype
											_lowerCamelCase     :			List[str]    =    torch.cat(
											    [ordered_inputs['attention_mask'], torch.ones(lowercase  ,      lowercase  ,      dtype=lowercase  )]  ,      dim=1  )

							return ordered_inputs






			@property
			def 					A_    (  self  ):
							return 13





"""simple docstring"""






def      _snake_case			(				lowercase__		):
				if not isinstance(lowercase__							,    lowercase__		):
								raise ValueError('Input series is not valid, valid series - [2, 4, 6]'		)
				if len(lowercase__		) == 0:
								raise ValueError('Input list must be a non empty list'		)
				if len(lowercase__		) == 1:
								return True
				_lowerCamelCase     :			List[Any]    =    series[1] - series[0]
				for index in range(len(lowercase__		) - 1		):
								if series[index + 1] - series[index] != common_diff:
												return False
				return True







def      _snake_case			(				lowercase__		):
				if not isinstance(lowercase__							,    lowercase__		):
								raise ValueError('Input series is not valid, valid series - [2, 4, 6]'		)
				if len(lowercase__		) == 0:
								raise ValueError('Input list must be a non empty list'		)
				_lowerCamelCase     :			Optional[int]    =    0
				for val in series:
								answer += val
				return answer / len(lowercase__		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()