
from __future__ import annotations

import math


def 						UpperCAmelCase      (     a_		,       a_						)		->     list:







				"""simple docstring"""



				if len(a_						) != 2 or len(a[0]						) != 2 or len(a_						) != 2 or len(b[0]						) != 2:
								raise Exception("Matrices are not 2x2"						)
				__A             =			[
				    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
				    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
				]
				return new_matrix


def 						UpperCAmelCase      (     a_		,       a_						)		->     List[Any]:







				"""simple docstring"""



				return [
				    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]						)						)]
				    for row in range(len(a_						)						)
				]


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				return [
				    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]						)						)]
				    for row in range(len(a_						)						)
				]


def 						UpperCAmelCase      (     a_						)		->     tuple[list, list, list, list]:







				"""simple docstring"""



				if len(a_						) % 2 != 0 or len(a[0]						) % 2 != 0:
								raise Exception("Odd matrices are not supported!"						)

				__A             =			len(a_						)
				__A             =			matrix_length // 2

				__A             =			[[a[i][j] for j in range(a_		,       a_						)] for i in range(a_						)]
				__A             =			[
				    [a[i][j] for j in range(a_		,       a_						)] for i in range(a_		,       a_						)
				]

				__A             =			[[a[i][j] for j in range(a_						)] for i in range(a_						)]
				__A             =			[[a[i][j] for j in range(a_						)] for i in range(a_		,       a_						)]

				return top_left, top_right, bot_left, bot_right


def 						UpperCAmelCase      (     a_						)		->     tuple[int, int]:







				"""simple docstring"""



				return len(a_						), len(matrix[0]						)


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				print("\n".join(str(a_						) for line in matrix						)						)


def 						UpperCAmelCase      (     a_		,       a_						)		->     list:







				"""simple docstring"""



				if matrix_dimensions(a_						) == (2, 2):
								return default_matrix_multiplication(a_		,       a_						)

				__A      ,							__A      ,							__A      ,							__A             =			split_matrix(a_						)
				__A      ,							__A      ,							__A      ,							__A             =			split_matrix(a_						)

				__A             =			actual_strassen(a_		,       matrix_subtraction(a_		,       a_						)						)
				__A             =			actual_strassen(matrix_addition(a_		,       a_						)		,       a_						)
				__A             =			actual_strassen(matrix_addition(a_		,       a_						)		,       a_						)
				__A             =			actual_strassen(a_		,       matrix_subtraction(a_		,       a_						)						)
				__A             =			actual_strassen(matrix_addition(a_		,       a_						)		,       matrix_addition(a_		,       a_						)						)
				__A             =			actual_strassen(matrix_subtraction(a_		,       a_						)		,       matrix_addition(a_		,       a_						)						)
				__A             =			actual_strassen(matrix_subtraction(a_		,       a_						)		,       matrix_addition(a_		,       a_						)						)

				__A             =			matrix_addition(matrix_subtraction(matrix_addition(a_		,       a_						)		,       a_						)		,       a_						)
				__A             =			matrix_addition(a_		,       a_						)
				__A             =			matrix_addition(a_		,       a_						)
				__A             =			matrix_subtraction(matrix_subtraction(matrix_addition(a_		,       a_						)		,       a_						)		,       a_						)

				# construct the new matrix from our 4 quadrants
				__A             =			[]
				for i in range(len(a_						)						):
								new_matrix.append(top_left[i] + top_right[i]						)
				for i in range(len(a_						)						):
								new_matrix.append(bot_left[i] + bot_right[i]						)
				return new_matrix


def 						UpperCAmelCase      (     a_		,       a_						)		->     list:







				"""simple docstring"""



				if matrix_dimensions(a_						)[1] != matrix_dimensions(a_						)[0]:
								__A             =			(
								    "Unable to multiply these matrices, please check the dimensions.\n"
								    F'''Matrix A: {matrixa}\n'''
								    F'''Matrix B: {matrixa}'''
								)
								raise Exception(a_						)
				__A             =			matrix_dimensions(a_						)
				__A             =			matrix_dimensions(a_						)

				if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
								return [matrixa, matrixa]

				__A             =			max(*a_		,       *a_						)
				__A             =			int(math.pow(2		,       math.ceil(math.loga(a_						)						)						)						)
				__A             =			matrixa
				__A             =			matrixa

				# Adding zeros to the matrices so that the arrays dimensions are the same and also
				# power of 2
				for i in range(0		,       a_						):
								if i < dimensiona[0]:
												for _ in range(dimensiona[1]		,       a_						):
																new_matrixa[i].append(0						)
								else:
												new_matrixa.append([0] * maxim						)
								if i < dimensiona[0]:
												for _ in range(dimensiona[1]		,       a_						):
																new_matrixa[i].append(0						)
								else:
												new_matrixa.append([0] * maxim						)

				__A             =			actual_strassen(a_		,       a_						)

				# Removing the additional zeros
				for i in range(0		,       a_						):
								if i < dimensiona[0]:
												for _ in range(dimensiona[1]		,       a_						):
																final_matrix[i].pop()
								else:
												final_matrix.pop()
				return final_matrix


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple										=    [
					    [2, 3, 4, 5],
					    [6, 4, 3, 1],
					    [2, 3, 6, 7],
					    [3, 1, 2, 4],
					    [2, 3, 4, 5],
					    [6, 4, 3, 1],
					    [2, 3, 6, 7],
					    [3, 1, 2, 4],
					    [2, 3, 4, 5],
					    [6, 2, 3, 1],
					]
					SCREAMING_SNAKE_CASE							:Optional[int]										=    [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
					print(strassen(matrixa, matrixa))


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


SCREAMING_SNAKE_CASE							:Optional[Any]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, 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 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"bloom"
	snake_case_    =					["past_key_values"]
	snake_case_    =					{
	    "num_hidden_layers": "n_layer",
	    "num_attention_heads": "n_head",
	}






	def __init__(	self :			Optional[Any]							,A :			List[Any]=25_08_80							,A :			Optional[int]=64							,A :			List[Any]=2							,A :			Optional[int]=8							,A :			str=1E-5							,A :			str=0.02							,A :			int=True							,A :			Optional[Any]=1							,A :			int=2							,A :			str=False							,A :			Dict=0.0							,A :			List[Any]=0.0							,A :			str=1							,A :			List[Any]=False							,**A :			List[Any]							,):
					__A             =			vocab_size
					# Backward compatibility with n_embed kwarg
					__A             =			kwargs.pop("n_embed"							,A			)
					__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=A							,eos_token_id=A							,**A			)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					version.parse("1.12"  )






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






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__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_(A							,direction="inputs"							,inverted_values_shape=A			)
									__A             =			{0: "batch", 1: "past_sequence + sequence"}
					else:
									__A             =			{0: "batch", 1: "sequence"}

					return common_inputs






	@property
	def     UpperCamelCase_  (	self :			Optional[Any]			):
					return self._config.n_layer






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self._config.n_head






	@property
	def     UpperCamelCase_  (	self :			Optional[int]			):
					return 1E-3






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

					# 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(A			), torch.zeros(A			)) 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(A							,A							,dtype=A			)]							,dim=1			)

					return ordered_inputs







	@property
	def     UpperCamelCase_  (	self :			int			):
					return 13
