
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload





class 							A__		:
      def __init__(      self    ,  __magic_name__ = None      ):
       if components is None:
        lowerCamelCase		:	Optional[Any]							=      []
       lowerCamelCase		:	List[str]							=      list(__magic_name__      )
      def __len__(      self      ):
       return len(self.__components      )
      def __str__(      self      ):
       return "(" + ",".join(map(__magic_name__    ,  self.__components      )      ) + ")"
      def __add__(      self    ,  __magic_name__      ):
       lowerCamelCase		:	Any							=      len(self      )
       if size == len(__magic_name__      ):
        lowerCamelCase		:	Any							=      [self.__components[i] + other.component(__magic_name__      ) for i in range(__magic_name__      )]
        return Vector(__magic_name__      )
       else:
        raise Exception("""must have the same size"""      )
      def __sub__(      self    ,  __magic_name__      ):
       lowerCamelCase		:	List[str]							=      len(self      )
       if size == len(__magic_name__      ):
        lowerCamelCase		:	str							=      [self.__components[i] - other.component(__magic_name__      ) for i in range(__magic_name__      )]
        return Vector(__magic_name__      )
       else:  # error case
        raise Exception("""must have the same size"""      )
      @overload
      def __mul__(      self    ,  __magic_name__      ):
       ...
      @overload
      def __mul__(      self    ,  __magic_name__      ):
       ...
      def __mul__(      self    ,  __magic_name__      ):
       if isinstance(__magic_name__    ,  (float, int)      ):
        lowerCamelCase		:	Any							=      [c * other for c in self.__components]
        return Vector(__magic_name__      )
       elif isinstance(__magic_name__    ,  __magic_name__      ) and len(self      ) == len(__magic_name__      ):
        lowerCamelCase		:	List[Any]							=      len(self      )
        lowerCamelCase		:	Optional[int]							=      [self.__components[i] * other.component(__magic_name__      ) for i in range(__magic_name__      )]
        return sum(__magic_name__      )
       else:  # error case
        raise Exception("""invalid operand!"""      )
      def 						UpperCamelCase__							(      self      ):
       return Vector(self.__components      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if isinstance(__magic_name__    ,  __magic_name__      ) and -len(self.__components      ) <= i < len(self.__components      ):
        return self.__components[i]
       else:
        raise Exception("""index out of range"""      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       assert -len(self.__components      ) <= pos < len(self.__components      )
       lowerCamelCase		:	Union[str, Any]							=      value
      def 						UpperCamelCase__							(      self      ):
       if len(self.__components      ) == 0:
        raise Exception("""Vector is empty"""      )
       lowerCamelCase		:	Union[str, Any]							=      [c**2 for c in self.__components]
       return math.sqrt(sum(__magic_name__      )      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = False      ):
       lowerCamelCase		:	Tuple							=      self * other
       lowerCamelCase		:	List[Any]							=      self.euclidean_length() * other.euclidean_length()
       if deg:
        return math.degrees(math.acos(num / den      )      )
       else:
        return math.acos(num / den      )





def     _a					(						lowerCamelCase							):
 assert isinstance(lowerCamelCase,	lowerCamelCase							)
 return Vector([0] * dimension							)





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 assert isinstance(lowerCamelCase,	lowerCamelCase							) and (isinstance(lowerCamelCase,	lowerCamelCase							))
 lowerCamelCase		:	Tuple							=      [0] * dimension
 lowerCamelCase		:	Tuple							=      1
 return Vector(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 assert (
     isinstance(lowerCamelCase,	lowerCamelCase							)
     and isinstance(lowerCamelCase,	lowerCamelCase							)
     and (isinstance(lowerCamelCase,	(int, float)							))
 )
 return x * scalar + y





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 random.seed(lowerCamelCase							)
 lowerCamelCase		:	str							=      [random.randint(lowerCamelCase,	lowerCamelCase							) for _ in range(lowerCamelCase							)]
 return Vector(lowerCamelCase							)





class 							A__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Union[str, Any]							=      matrix
       lowerCamelCase		:	Optional[Any]							=      w
       lowerCamelCase		:	List[Any]							=      h
      def __str__(      self      ):
       lowerCamelCase		:	Dict							=      """"""
       for i in range(self.__height      ):
        ans += "|"
        for j in range(self.__width      ):
         if j < self.__width - 1:
          ans += str(self.__matrix[i][j]      ) + ","
         else:
          ans += str(self.__matrix[i][j]      ) + "|\n"
       return ans
      def __add__(      self    ,  __magic_name__      ):
       if self.__width == other.width() and self.__height == other.height():
        lowerCamelCase		:	Dict							=      []
        for i in range(self.__height      ):
         lowerCamelCase		:	List[Any]							=      [
             self.__matrix[i][j] + other.component(__magic_name__    ,  __magic_name__      )
             for j in range(self.__width      )
         ]
         matrix.append(__magic_name__      )
        return Matrix(__magic_name__    ,  self.__width    ,  self.__height      )
       else:
        raise Exception("""matrix must have the same dimension!"""      )
      def __sub__(      self    ,  __magic_name__      ):
       if self.__width == other.width() and self.__height == other.height():
        lowerCamelCase		:	Optional[int]							=      []
        for i in range(self.__height      ):
         lowerCamelCase		:	Optional[Any]							=      [
             self.__matrix[i][j] - other.component(__magic_name__    ,  __magic_name__      )
             for j in range(self.__width      )
         ]
         matrix.append(__magic_name__      )
        return Matrix(__magic_name__    ,  self.__width    ,  self.__height      )
       else:
        raise Exception("""matrices must have the same dimension!"""      )
      @overload
      def __mul__(      self    ,  __magic_name__      ):
       ...
      @overload
      def __mul__(      self    ,  __magic_name__      ):
       ...
      def __mul__(      self    ,  __magic_name__      ):
       if isinstance(__magic_name__    ,  __magic_name__      ):  # matrix-vector
        if len(__magic_name__      ) == self.__width:
         lowerCamelCase		:	Tuple							=      zero_vector(self.__height      )
         for i in range(self.__height      ):
          lowerCamelCase		:	Union[str, Any]							=      [
              self.__matrix[i][j] * other.component(__magic_name__      )
              for j in range(self.__width      )
          ]
          ans.change_component(__magic_name__    ,  sum(__magic_name__      )      )
         return ans
        else:
         raise Exception(
             """vector must have the same size as the """
             """number of columns of the matrix!"""      )
       elif isinstance(__magic_name__    ,  (int, float)      ):  # matrix-scalar
        lowerCamelCase		:	Union[str, Any]							=      [
            [self.__matrix[i][j] * other for j in range(self.__width      )]
            for i in range(self.__height      )
        ]
        return Matrix(__magic_name__    ,  self.__width    ,  self.__height      )
       return None
      def 						UpperCamelCase__							(      self      ):
       return self.__height
      def 						UpperCamelCase__							(      self      ):
       return self.__width
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       if 0 <= x < self.__height and 0 <= y < self.__width:
        return self.__matrix[x][y]
       else:
        raise Exception("""change_component: indices out of bounds"""      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       if 0 <= x < self.__height and 0 <= y < self.__width:
        lowerCamelCase		:	Tuple							=      value
       else:
        raise Exception("""change_component: indices out of bounds"""      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       if self.__height != self.__width:
        raise Exception("""Matrix is not square"""      )
       lowerCamelCase		:	str							=      self.__matrix[:x] + self.__matrix[x + 1 :]
       for i in range(len(__magic_name__      )      ):
        lowerCamelCase		:	List[str]							=      minor[i][:y] + minor[i][y + 1 :]
       return Matrix(__magic_name__    ,  self.__width - 1    ,  self.__height - 1      ).determinant()
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       if self.__height != self.__width:
        raise Exception("""Matrix is not square"""      )
       if 0 <= x < self.__height and 0 <= y < self.__width:
        return (-1) ** (x + y) * self.minor(__magic_name__    ,  __magic_name__      )
       else:
        raise Exception("""Indices out of bounds"""      )






      def 						UpperCamelCase__							(      self      ):
       if self.__height != self.__width:
        raise Exception("""Matrix is not square"""      )
       if self.__height < 1:
        raise Exception("""Matrix has no element"""      )
       elif self.__height == 1:
        return self.__matrix[0][0]
       elif self.__height == 2:
        return (
            self.__matrix[0][0] * self.__matrix[1][1]
            - self.__matrix[0][1] * self.__matrix[1][0]
        )
       else:
        lowerCamelCase		:	Tuple							=      [
            self.__matrix[0][y] * self.cofactor(0    ,  __magic_name__      ) for y in range(self.__width      )
        ]
        return sum(__magic_name__      )





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	list[list[float]]							=      [[0] * n for _ in range(lowerCamelCase							)]
 return Matrix(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 random.seed(lowerCamelCase							)
 lowerCamelCase		:	list[list[float]]							=      [
     [random.randint(lowerCamelCase,	lowerCamelCase							) for _ in range(lowerCamelCase							)] for _ in range(lowerCamelCase							)
 ]
 return Matrix(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)






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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """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):
      _UpperCAmelCase				:    List[Any]           =			"""gpt_neo"""
      _UpperCAmelCase				:    Union[str, Any]           =			["""past_key_values"""]
      _UpperCAmelCase				:    List[Any]           =			{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
      def __init__(      self    ,  __magic_name__=5_0_2_5_7    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_0_4_8    ,  __magic_name__=2_4    ,  __magic_name__=[[["global", "local"], 1_2]]    ,  __magic_name__=1_6    ,  __magic_name__=None    ,  __magic_name__=2_5_6    ,  __magic_name__="gelu_new"    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.1    ,  __magic_name__=1e-5    ,  __magic_name__=0.02    ,  __magic_name__=True    ,  __magic_name__=5_0_2_5_6    ,  __magic_name__=5_0_2_5_6    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	List[Any]							=      vocab_size
       lowerCamelCase		:	str							=      max_position_embeddings
       lowerCamelCase		:	str							=      hidden_size
       lowerCamelCase		:	Optional[int]							=      num_layers
       lowerCamelCase		:	str							=      num_heads
       lowerCamelCase		:	Optional[Any]							=      intermediate_size
       lowerCamelCase		:	List[Any]							=      window_size
       lowerCamelCase		:	int							=      activation_function
       lowerCamelCase		:	Union[str, Any]							=      resid_dropout
       lowerCamelCase		:	List[Any]							=      embed_dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Dict							=      classifier_dropout
       lowerCamelCase		:	Any							=      layer_norm_epsilon
       lowerCamelCase		:	Dict							=      initializer_range
       lowerCamelCase		:	Dict							=      use_cache

       lowerCamelCase		:	Optional[Any]							=      bos_token_id
       lowerCamelCase		:	int							=      eos_token_id

       lowerCamelCase		:	List[Any]							=      attention_types
       lowerCamelCase		:	Optional[Any]							=      self.expand_attention_types_params(__magic_name__      )

       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=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )






      @staticmethod
      def 						UpperCamelCase__							(      __magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      []
       for item in attention_types:
        for _ in range(item[1]      ):
         attentions.extend(item[0]      )
       return attentions





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	Any							=      input.size()
 lowerCamelCase		:	List[Any]							=      len(lowerCamelCase							)
 lowerCamelCase		:	Optional[Any]							=      shape[dimension]

 lowerCamelCase		:	Optional[int]							=      torch.arange(0,	lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	Dict							=      torch.div(sizedim - size,	lowerCamelCase,	rounding_mode="""floor"""							) + 1
 lowerCamelCase		:	int							=      torch.arange(lowerCamelCase							) + low_indices[:min_length][:, None]

 lowerCamelCase		:	str							=      [slice(lowerCamelCase							)] * rank
 lowerCamelCase		:	List[str]							=      indices
 lowerCamelCase		:	Dict							=      input[s]

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

 return sliced.permute(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 import torch

 lowerCamelCase		:	List[Any]							=      torch.arange(1,	lowerCamelCase							)
 lowerCamelCase		:	Optional[int]							=      torch.remainder(lowerCamelCase,	lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      remainders == 0
 lowerCamelCase		:	List[Any]							=      candidates[divisor_indices]
 lowerCamelCase		:	Optional[Any]							=      torch.max(lowerCamelCase							)
 return largest_divisor, torch.div(lowerCamelCase,	lowerCamelCase,	rounding_mode="""floor"""							)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
       if self.use_past:
        self.fill_with_past_key_values_(__magic_name__    ,  direction="""inputs"""      )
        lowerCamelCase		:	int							=      {0: """batch""", 1: """past_sequence + sequence"""}
       else:
        lowerCamelCase		:	Tuple							=      {0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def 						UpperCamelCase__							(      self      ):
       return self._config.num_heads
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = -1    ,  __magic_name__ = -1    ,  __magic_name__ = False    ,  __magic_name__ = None    ,  ):
       lowerCamelCase		:	Optional[int]							=      super(__magic_name__    ,  self      ).generate_dummy_inputs(
           __magic_name__    ,  batch_size=__magic_name__    ,  seq_length=__magic_name__    ,  is_pair=__magic_name__    ,  framework=__magic_name__      )

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

       lowerCamelCase		:	Tuple							=      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(__magic_name__    ,  __magic_name__    ,  dtype=__magic_name__      )]    ,  dim=1      )

       return ordered_inputs






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_3




