



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
				from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


__snake_case     :Any			       =logging.get_logger(__name__)

__snake_case     :List[Any]			       ={
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     int								=     'bloom'
				A_   :     Any								=     ['past_key_values']
				A_   :     Dict								=     {
				    'num_hidden_layers': 'n_layer',
				    'num_attention_heads': 'n_head',
				}


				def __init__(							self      :  Any						, __UpperCamelCase      :  Optional[int]=250_880						, __UpperCamelCase      :  List[str]=64						, __UpperCamelCase      :  Tuple=2						, __UpperCamelCase      :  Optional[int]=8						, __UpperCamelCase      :  str=1e-5						, __UpperCamelCase      :  Union[str, Any]=0.0_2						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Union[str, Any]=1						, __UpperCamelCase      :  List[Any]=2						, __UpperCamelCase      :  Optional[int]=False						, __UpperCamelCase      :  List[Any]=0.0						, __UpperCamelCase      :  int=0.0						, __UpperCamelCase      :  Optional[Any]=1						, __UpperCamelCase      :  List[Any]=False						, **__UpperCamelCase      :  Dict						, )		->     Any:
											A      						=    vocab_size
											# Backward compatibility with n_embed kwarg
											A      						=    kwargs.pop('n_embed'						, __UpperCamelCase       )
											A      						=    hidden_size if n_embed is None else n_embed
											A      						=    n_layer
											A      						=    n_head
											A      						=    layer_norm_epsilon
											A      						=    initializer_range
											A      						=    use_cache
											A      						=    pretraining_tp
											A      						=    apply_residual_connection_post_layernorm
											A      						=    hidden_dropout
											A      						=    attention_dropout

											A      						=    bos_token_id
											A      						=    eos_token_id
											A      						=    slow_but_exact

											super().__init__(bos_token_id=__UpperCamelCase						, eos_token_id=__UpperCamelCase						, **__UpperCamelCase       )





class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     version.parse('1.12'       )


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  PretrainedConfig						, __UpperCamelCase      :  str = "default"						, __UpperCamelCase      :  List[PatchingSpec] = None						, __UpperCamelCase      :  bool = False						, )		->     Any:
											super().__init__(__UpperCamelCase						, task=__UpperCamelCase						, patching_specs=__UpperCamelCase						, use_past=__UpperCamelCase       )
											if not getattr(self._config						, 'pad_token_id'						, __UpperCamelCase       ):
																		# TODO: how to do that better?
																		A      						=    0


				@property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Mapping[str, Mapping[int, str]]:
											A      						=    OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
											if self.use_past:
																		# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
																		self.fill_with_past_key_values_(__UpperCamelCase						, direction='inputs'						, inverted_values_shape=__UpperCamelCase       )
																		A      						=    {0: 'batch', 1: 'past_sequence + sequence'}
											else:
																		A      						=    {0: 'batch', 1: 'sequence'}

											return common_inputs


				@property
				def __UpperCamelCase						(							self      :  int       )		->     int:
											return self._config.n_layer


				@property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     int:
											return self._config.n_head


				@property
				def __UpperCamelCase						(							self      :  str       )		->     float:
											return 1e-3


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  "PreTrainedTokenizer"						, __UpperCamelCase      :  int = -1						, __UpperCamelCase      :  int = -1						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  Optional["TensorType"] = None						, )		->     Mapping[str, Any]:
											A      						=    super(__UpperCamelCase						, self       ).generate_dummy_inputs(
											    __UpperCamelCase						, batch_size=__UpperCamelCase						, seq_length=__UpperCamelCase						, is_pair=__UpperCamelCase						, framework=__UpperCamelCase       )

											# We need to order the input in the way they appears in the forward()
											A      						=    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

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

											A      						=    common_inputs['attention_mask']
											if self.use_past:
																		A      						=    ordered_inputs['attention_mask'].dtype
																		A      						=    torch.cat(
																		    [ordered_inputs['attention_mask'], torch.ones(__UpperCamelCase						, __UpperCamelCase						, dtype=__UpperCamelCase       )]						, dim=1       )

											return ordered_inputs


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     int:
											return 13

"""simple docstring"""

from __future__ import annotations

from collections.abc import Callable

SCREAMING_SNAKE_CASE_            =    list[list[float | int]]




def      lowercase__    (       lowerCAmelCase					:       Matrix			,					lowerCAmelCase					:       Matrix			)			->   Matrix:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(size + 1			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for row in range(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       matrix[row][col]

        UpperCAmelCase          =       vector[row][0]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       0
    while row < size and col < size:
        # pivoting
        UpperCAmelCase          =       max((abs(augmented[rowa][col]			), rowa) for rowa in range(lowerCAmelCase			,					lowerCAmelCase			)			)[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            UpperCAmelCase      ,				UpperCAmelCase          =       augmented[pivot_row], augmented[row]

        for rowa in range(row + 1			,					lowerCAmelCase			):
            UpperCAmelCase          =       augmented[rowa][col] / augmented[row][col]
            UpperCAmelCase          =       0
            for cola in range(col + 1			,					size + 1			):
                augmented[rowa][cola] -= augmented[row][cola] * ratio

        row += 1
        col += 1

    # back substitution
    for col in range(1			,					lowerCAmelCase			):
        for row in range(lowerCAmelCase			):
            UpperCAmelCase          =       augmented[row][col] / augmented[col][col]
            for cola in range(lowerCAmelCase			,					size + 1			):
                augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
    return [
        [round(augmented[row][size] / augmented[row][row]			,					10			)] for row in range(lowerCAmelCase			)
    ]




def      lowercase__    (       lowerCAmelCase					:       list[int]			)			->   Callable[[int], int]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    UpperCAmelCase          =       [[0 for _ in range(lowerCAmelCase			)] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       [[0] for _ in range(lowerCAmelCase			)]
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for x_val, y_val in enumerate(lowerCAmelCase			):
        for col in range(lowerCAmelCase			):
            UpperCAmelCase          =       (x_val + 1) ** (size - col - 1)
        UpperCAmelCase          =       y_val

    UpperCAmelCase          =       solve(lowerCAmelCase			,					lowerCAmelCase			)

    def interpolated_func(lowerCAmelCase					:       int			) -> int:
        return sum(
            round(coeffs[x_val][0]			) * (var ** (size - x_val - 1))
            for x_val in range(lowerCAmelCase			)			)

    return interpolated_func




def      lowercase__    (       lowerCAmelCase					:       int			)			->   int:






    """simple docstring"""




    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )




def      lowercase__    (       lowerCAmelCase					:       Callable[[int], int] = question_function			,					lowerCAmelCase					:       int = 10			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       [func(lowerCAmelCase			) for x_val in range(1			,					order + 1			)]

    UpperCAmelCase          =       [
        interpolate(data_points[:max_coeff]			) for max_coeff in range(1			,					order + 1			)
    ]

    UpperCAmelCase          =       0
    UpperCAmelCase          =       42
    UpperCAmelCase          =       42

    for poly in polynomials:
        UpperCAmelCase          =       1
        while func(lowerCAmelCase			) == poly(lowerCAmelCase			):
            x_val += 1

        ret += poly(lowerCAmelCase			)

    return ret


if __name__ == "__main__":
   print(F'{solution() = }')



