


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


SCREAMING_SNAKE_CASE :   Tuple							      =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :   Dict							      =				{
    "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(		_a     ):
       lowercase_				:			Optional[int]									=    """gpt_neo"""
       lowercase_				:			List[str]									=    ["""past_key_values"""]
       lowercase_				:			int									=    {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
       def __init__(      self,   lowerCamelCase=5_02_57,   lowerCamelCase=20_48,   lowerCamelCase=20_48,   lowerCamelCase=24,   lowerCamelCase=[[["global", "local"], 12]],   lowerCamelCase=16,   lowerCamelCase=None,   lowerCamelCase=2_56,   lowerCamelCase="gelu_new",   lowerCamelCase=0.0,   lowerCamelCase=0.0,   lowerCamelCase=0.0,   lowerCamelCase=0.1,   lowerCamelCase=1E-5,   lowerCamelCase=0.0_2,   lowerCamelCase=True,   lowerCamelCase=5_02_56,   lowerCamelCase=5_02_56,   **lowerCamelCase,   ) ->     Tuple:




           """simple docstring"""

           _lowercase :							int   	=	vocab_size
           _lowercase :							str   	=	max_position_embeddings
           _lowercase :							str   	=	hidden_size
           _lowercase :							Optional[int]   	=	num_layers
           _lowercase :							List[Any]   	=	num_heads
           _lowercase :							str   	=	intermediate_size
           _lowercase :							Optional[Any]   	=	window_size
           _lowercase :							Any   	=	activation_function
           _lowercase :							Union[str, Any]   	=	resid_dropout
           _lowercase :							Union[str, Any]   	=	embed_dropout
           _lowercase :							Optional[int]   	=	attention_dropout
           _lowercase :							Any   	=	classifier_dropout
           _lowercase :							List[str]   	=	layer_norm_epsilon
           _lowercase :							List[Any]   	=	initializer_range
           _lowercase :							Any   	=	use_cache

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

           _lowercase :							int   	=	attention_types
           _lowercase :							Optional[Any]   	=	self.expand_attention_types_params(lowerCamelCase)

           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=lowerCamelCase,   eos_token_id=lowerCamelCase,   **lowerCamelCase)




       @staticmethod
       def     UpperCamelCase  (      lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							List[Any]   	=	[]
           for item in attention_types:
               for _ in range(item[1]):
                   attentions.extend(item[0])
           return attentions



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Tuple:
    import torch

    _lowercase :							List[str]   	=	input.size()
    _lowercase :							Any   	=	len(lowerCamelCase_				)
    _lowercase :							List[str]   	=	shape[dimension]

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

    _lowercase :							List[Any]   	=	[slice(lowerCamelCase_				)] * rank
    _lowercase :							Optional[int]   	=	indices
    _lowercase :							Dict   	=	input[s]

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

    return sliced.permute(lowerCamelCase_				)



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Union[str, Any]:
    import torch

    _lowercase :							str   	=	torch.arange(1							,				lowerCamelCase_				)
    _lowercase :							str   	=	torch.remainder(lowerCamelCase_							,				lowerCamelCase_				)
    _lowercase :							int   	=	remainders == 0
    _lowercase :							str   	=	candidates[divisor_indices]
    _lowercase :							Optional[int]   	=	torch.max(lowerCamelCase_				)
    return largest_divisor, torch.div(lowerCamelCase_							,				lowerCamelCase_							,				rounding_mode='floor'				)






class _lowerCamelCase(		_a     ):
       @property
       def     UpperCamelCase  (      self) ->     Mapping[str, Mapping[int, str]]:




           """simple docstring"""

           _lowercase :							Any   	=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}})
           if self.use_past:
               self.fill_with_past_key_values_(lowerCamelCase,   direction='inputs')
               _lowercase :							Tuple   	=	{0: 'batch', 1: 'past_sequence + sequence'}
           else:
               _lowercase :							str   	=	{0: 'batch', 1: 'sequence'}

           return common_inputs
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           return self._config.num_heads
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = -1,   lowerCamelCase = -1,   lowerCamelCase = False,   lowerCamelCase = None,   ) ->     Mapping[str, Any]:




           """simple docstring"""

           _lowercase :							Dict   	=	super(lowerCamelCase,   self).generate_dummy_inputs(
               lowerCamelCase,   batch_size=lowerCamelCase,   seq_length=lowerCamelCase,   is_pair=lowerCamelCase,   framework=lowerCamelCase)

           # We need to order the input in the way they appears in the forward()
           _lowercase :							Tuple   	=	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

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

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

           return ordered_inputs




       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           return 13






def        UpperCamelCase_(   lowerCamelCase_				)					->		int:
    assert (
        isinstance(lowerCamelCase_							,				lowerCamelCase_				) and number_of_steps > 0
    ), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
    if number_of_steps == 1:
        return 1
    _lowercase	,       _lowercase :							Dict   	=	1, 1
    for _ in range(number_of_steps - 1				):
        _lowercase	,       _lowercase :							Tuple   	=	current + previous, current
    return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()


