

from __future__ import annotations

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



class    _a						:


     '''simple docstring'''






     def __init__(			self   ,	A__ = None      ):
      if components is None:
       A__							:   List[Any]					       =		[]
      A__							:   Union[str, Any]					       =		list(A__      )






     def __len__(			self      ):
      return len(self.__components      )






     def __str__(			self      ):
      return "(" + ",".join(map(A__   ,	self.__components      )      ) + ")"






     def __add__(			self   ,	A__      ):
      A__							:   int					       =		len(self      )
      if size == len(A__      ):
       A__							:   Tuple					       =		[self.__components[i] + other.component(A__      ) for i in range(A__      )]
       return Vector(A__      )
      else:
       raise Exception("""must have the same size"""      )






     def __sub__(			self   ,	A__      ):
      A__							:   Optional[int]					       =		len(self      )
      if size == len(A__      ):
       A__							:   Optional[Any]					       =		[self.__components[i] - other.component(A__      ) for i in range(A__      )]
       return Vector(A__      )
      else:  # error case
       raise Exception("""must have the same size"""      )






     @overload
     def __mul__(			self   ,	A__      ):
      ...






     @overload
     def __mul__(			self   ,	A__      ):
      ...






     def __mul__(			self   ,	A__      ):
      if isinstance(A__   ,	(float, int)      ):
       A__							:   Any					       =		[c * other for c in self.__components]
       return Vector(A__      )
      elif isinstance(A__   ,	A__      ) and len(self      ) == len(A__      ):
       A__							:   str					       =		len(self      )
       A__							:   int					       =		[self.__components[i] * other.component(A__      ) for i in range(A__      )]
       return sum(A__      )
      else:  # error case
       raise Exception("""invalid operand!"""      )






     def   __A      (			self      ):
      return Vector(self.__components      )






     def   __A      (			self   ,	A__      ):
      if isinstance(A__   ,	A__      ) and -len(self.__components      ) <= i < len(self.__components      ):
       return self.__components[i]
      else:
       raise Exception("""index out of range"""      )






     def   __A      (			self   ,	A__   ,	A__      ):
      assert -len(self.__components      ) <= pos < len(self.__components      )
      A__							:   Any					       =		value






     def   __A      (			self      ):
      if len(self.__components      ) == 0:
       raise Exception("""Vector is empty"""      )
      A__							:   Optional[int]					       =		[c**2 for c in self.__components]
      return math.sqrt(sum(A__      )      )






     def   __A      (			self   ,	A__   ,	A__ = False      ):
      A__							:   Optional[int]					       =		self * other
      A__							:   str					       =		self.euclidean_length() * other.euclidean_length()
      if deg:
       return math.degrees(math.acos(num / den      )      )
      else:
       return math.acos(num / den      )



def 						UpperCamelCase (lowercase_:			int )					->				Vector:
 assert isinstance(lowercase_   ,				lowercase_ )
 return Vector([0] * dimension )

def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			int )					->				Vector:
 assert isinstance(lowercase_   ,				lowercase_ ) and (isinstance(lowercase_   ,				lowercase_ ))
 A__							:   Tuple					       =		[0] * dimension
 A__							:   str					       =		1
 return Vector(lowercase_ )

def 						UpperCamelCase (lowercase_:			float   ,				lowercase_:			Vector   ,				lowercase_:			Vector )					->				Vector:
 assert (
     isinstance(lowercase_   ,				lowercase_ )
     and isinstance(lowercase_   ,				lowercase_ )
     and (isinstance(lowercase_   ,				(int, float) ))
 )
 return x * scalar + y

def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			int   ,				lowercase_:			int )					->				Vector:
 random.seed(lowercase_ )
 A__							:   int					       =		[random.randint(lowercase_   ,				lowercase_ ) for _ in range(lowercase_ )]
 return Vector(lowercase_ )



class    _a						:


     '''simple docstring'''






     def __init__(			self   ,	A__   ,	A__   ,	A__      ):
      A__							:   List[Any]					       =		matrix
      A__							:   str					       =		w
      A__							:   List[str]					       =		h






     def __str__(			self      ):
      A__							:   Any					       =		""""""
      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   ,	A__      ):
      if self.__width == other.width() and self.__height == other.height():
       A__							:   List[str]					       =		[]
       for i in range(self.__height      ):
        A__							:   Optional[Any]					       =		[
            self.__matrix[i][j] + other.component(A__   ,	A__      )
            for j in range(self.__width      )
        ]
        matrix.append(A__      )
       return Matrix(A__   ,	self.__width   ,	self.__height      )
      else:
       raise Exception("""matrix must have the same dimension!"""      )






     def __sub__(			self   ,	A__      ):
      if self.__width == other.width() and self.__height == other.height():
       A__							:   List[Any]					       =		[]
       for i in range(self.__height      ):
        A__							:   str					       =		[
            self.__matrix[i][j] - other.component(A__   ,	A__      )
            for j in range(self.__width      )
        ]
        matrix.append(A__      )
       return Matrix(A__   ,	self.__width   ,	self.__height      )
      else:
       raise Exception("""matrices must have the same dimension!"""      )






     @overload
     def __mul__(			self   ,	A__      ):
      ...






     @overload
     def __mul__(			self   ,	A__      ):
      ...






     def __mul__(			self   ,	A__      ):
      if isinstance(A__   ,	A__      ):  # matrix-vector
       if len(A__      ) == self.__width:
        A__							:   Union[str, Any]					       =		zero_vector(self.__height      )
        for i in range(self.__height      ):
         A__							:   Dict					       =		[
             self.__matrix[i][j] * other.component(A__      )
             for j in range(self.__width      )
         ]
         ans.change_component(A__   ,	sum(A__      )      )
        return ans
       else:
        raise Exception(
            """vector must have the same size as the """
            """number of columns of the matrix!"""      )
      elif isinstance(A__   ,	(int, float)      ):  # matrix-scalar
       A__							:   Union[str, Any]					       =		[
           [self.__matrix[i][j] * other for j in range(self.__width      )]
           for i in range(self.__height      )
       ]
       return Matrix(A__   ,	self.__width   ,	self.__height      )
      return None






     def   __A      (			self      ):
      return self.__height






     def   __A      (			self      ):
      return self.__width






     def   __A      (			self   ,	A__   ,	A__      ):
      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   __A      (			self   ,	A__   ,	A__   ,	A__      ):
      if 0 <= x < self.__height and 0 <= y < self.__width:
       A__							:   List[Any]					       =		value
      else:
       raise Exception("""change_component: indices out of bounds"""      )






     def   __A      (			self   ,	A__   ,	A__      ):
      if self.__height != self.__width:
       raise Exception("""Matrix is not square"""      )
      A__							:   Union[str, Any]					       =		self.__matrix[:x] + self.__matrix[x + 1 :]
      for i in range(len(A__      )      ):
       A__							:   Union[str, Any]					       =		minor[i][:y] + minor[i][y + 1 :]
      return Matrix(A__   ,	self.__width - 1   ,	self.__height - 1      ).determinant()






     def   __A      (			self   ,	A__   ,	A__      ):
      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(A__   ,	A__      )
      else:
       raise Exception("""Indices out of bounds"""      )






     def   __A      (			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:
       A__							:   Union[str, Any]					       =		[
           self.__matrix[0][y] * self.cofactor(0   ,	A__      ) for y in range(self.__width      )
       ]
       return sum(A__      )



def 						UpperCamelCase (lowercase_:			int )					->				Matrix:
 A__							:   list[list[float]]					       =		[[0] * n for _ in range(lowercase_ )]
 return Matrix(lowercase_   ,				lowercase_   ,				lowercase_ )


def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			int   ,				lowercase_:			int   ,				lowercase_:			int )					->				Matrix:
 random.seed(lowercase_ )
 A__							:   list[list[float]]					       =		[
     [random.randint(lowercase_   ,				lowercase_ ) for _ in range(lowercase_ )] for _ in range(lowercase_ )
 ]
 return Matrix(lowercase_   ,				lowercase_   ,				lowercase_ )






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_	:			List[Any]  	=      {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[Any]  	=      ['GPTNeoXTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			Optional[int]  	=      [
	    'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXForCausalLM',
	    'GPTNeoXForQuestionAnswering',
	    'GPTNeoXForSequenceClassification',
	    'GPTNeoXForTokenClassification',
	    'GPTNeoXLayer',
	    'GPTNeoXModel',
	    'GPTNeoXPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	A_	:			Any  	=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



