

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		:List[Any]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:List[Any]						=     {
    """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 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:str										=      """gpt_neo"""
     UpperCamelCase_	:str										=      ["""past_key_values"""]
     UpperCamelCase_	:str										=      {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






     def __init__(				self		,						_lowercase=50_257		,						_lowercase=2_048		,						_lowercase=2_048		,						_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=50_256		,						_lowercase=50_256		,						**_lowercase		,						)->     List[str]:
         UpperCamelCase_			  =						vocab_size
         UpperCamelCase_			  =						max_position_embeddings
         UpperCamelCase_			  =						hidden_size
         UpperCamelCase_			  =						num_layers
         UpperCamelCase_			  =						num_heads
         UpperCamelCase_			  =						intermediate_size
         UpperCamelCase_			  =						window_size
         UpperCamelCase_			  =						activation_function
         UpperCamelCase_			  =						resid_dropout
         UpperCamelCase_			  =						embed_dropout
         UpperCamelCase_			  =						attention_dropout
         UpperCamelCase_			  =						classifier_dropout
         UpperCamelCase_			  =						layer_norm_epsilon
         UpperCamelCase_			  =						initializer_range
         UpperCamelCase_			  =						use_cache

         UpperCamelCase_			  =						bos_token_id
         UpperCamelCase_			  =						eos_token_id

         UpperCamelCase_			  =						attention_types
         UpperCamelCase_			  =						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 	UpperCAmelCase_				(				_lowercase	)->     List[str]:
         UpperCamelCase_			  =						[]
         for item in attention_types:
             for _ in range(item[1]	):
                 attentions.extend(item[0]	)
         return attentions




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    import torch

    UpperCamelCase_			  =						input.size()
    UpperCamelCase_			  =						len(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						shape[dimension]

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

    UpperCamelCase_			  =						[slice(SCREAMING_SNAKE_CASE_			)] * rank
    UpperCamelCase_			  =						indices
    UpperCamelCase_			  =						input[s]

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

    return sliced.permute(SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    import torch

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






class 						__magic_name__					(							snake_case				):






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

         return common_inputs






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






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = -1		,						_lowercase = -1		,						_lowercase = False		,						_lowercase = None		,						)->     Mapping[str, Any]:
         UpperCamelCase_			  =						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()
         UpperCamelCase_			  =						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

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

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

         return ordered_inputs






     @property
     def 	UpperCAmelCase_				(				self	)->     int:
         return 13



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[i - 1 for i in range(limit + 1			)]

    for i in range(2			,						limit + 1			):
        if phi[i] == i - 1:
            for j in range(2 * i			,						limit + 1			,						SCREAMING_SNAKE_CASE_			):
                phi[j] -= phi[j] // i

    return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
 print(solution())
