



'''simple docstring'''



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


lowerCAmelCase	:		List[str]				=   get_logger(__name__)


lowerCAmelCase	:		Dict				=   Path(__file__).parent / '''model_card_template.md'''
lowerCAmelCase	:		int				=   uuida().hex
lowerCAmelCase	:		List[str]				=   os.getenv('''HF_HUB_OFFLINE''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase	:		str				=   os.getenv('''DISABLE_TELEMETRY''', '''''').upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase	:		int				=   HUGGINGFACE_CO_RESOLVE_ENDPOINT + '''/api/telemetry/'''



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[Dict, str, None] = None					):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
	if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
		return ua + "; telemetry/off"
	if is_torch_available():
		ua += f'''; torch/{_torch_version}'''
	if is_flax_available():
		ua += f'''; jax/{_jax_version}'''
		ua += f'''; flax/{_flax_version}'''
	if is_onnx_available():
		ua += f'''; onnxruntime/{_onnxruntime_version}'''
	# CI will set this value to True
	if os.environ.get("DIFFUSERS_IS_CI"				,		""					).upper() in ENV_VARS_TRUE_VALUES:
		ua += "; is_ci/true"
	if isinstance(lowerCamelCase				,		lowerCamelCase					):
		ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()					)
	elif isinstance(lowerCamelCase				,		lowerCamelCase					):
		ua += "; " + user_agent
	return ua



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Optional[str] = None				,		lowerCamelCase   :						Optional[str] = None					):






	'''simple docstring'''




	if token is None:
		__lowerCAmelCase												=  HfFolder.get_token()
	if organization is None:
		__lowerCAmelCase												=  whoami(lowerCamelCase					)["name"]
		return f'''{username}/{model_id}'''
	else:
		return f'''{organization}/{model_id}'''



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	if not is_jinja_available():
		raise ValueError(
		    "Modelcard rendering is based on Jinja templates."
		    " Please make sure to have `jinja` installed before using `create_model_card`."
		    " To install it, please run `pip install Jinja2`."					)

	if hasattr(lowerCamelCase				,		"local_rank"					) and args.local_rank not in [-1, 0]:
		return

	__lowerCAmelCase												=  args.hub_token if hasattr(lowerCamelCase				,		"hub_token"					) else None
	__lowerCAmelCase												=  get_full_repo_name(lowerCamelCase				,		token=lowerCamelCase					)

	__lowerCAmelCase												=  ModelCard.from_template(
	    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
	        language="en"				,		license="apache-2.0"				,		library_name="diffusers"				,		tags=[]				,		datasets=args.dataset_name				,		metrics=[]				,		)				,		template_path=lowerCamelCase				,		model_name=lowerCamelCase				,		repo_name=lowerCamelCase				,		dataset_name=args.dataset_name if hasattr(lowerCamelCase				,		"dataset_name"					) else None				,		learning_rate=args.learning_rate				,		train_batch_size=args.train_batch_size				,		eval_batch_size=args.eval_batch_size				,		gradient_accumulation_steps=(
	        args.gradient_accumulation_steps if hasattr(lowerCamelCase				,		"gradient_accumulation_steps"					) else None
	    )				,		adam_betaa=args.adam_betaa if hasattr(lowerCamelCase				,		"adam_beta1"					) else None				,		adam_betaa=args.adam_betaa if hasattr(lowerCamelCase				,		"adam_beta2"					) else None				,		adam_weight_decay=args.adam_weight_decay if hasattr(lowerCamelCase				,		"adam_weight_decay"					) else None				,		adam_epsilon=args.adam_epsilon if hasattr(lowerCamelCase				,		"adam_epsilon"					) else None				,		lr_scheduler=args.lr_scheduler if hasattr(lowerCamelCase				,		"lr_scheduler"					) else None				,		lr_warmup_steps=args.lr_warmup_steps if hasattr(lowerCamelCase				,		"lr_warmup_steps"					) else None				,		ema_inv_gamma=args.ema_inv_gamma if hasattr(lowerCamelCase				,		"ema_inv_gamma"					) else None				,		ema_power=args.ema_power if hasattr(lowerCamelCase				,		"ema_power"					) else None				,		ema_max_decay=args.ema_max_decay if hasattr(lowerCamelCase				,		"ema_max_decay"					) else None				,		mixed_precision=args.mixed_precision				,		)

	__lowerCAmelCase												=  os.path.join(args.output_dir				,		"README.md"					)
	model_card.save(lowerCamelCase					)



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[str] = None					):






	'''simple docstring'''




	if resolved_file is None or commit_hash is not None:
		return commit_hash
	__lowerCAmelCase												=  str(Path(lowerCamelCase					).as_posix()					)
	__lowerCAmelCase												=  re.search(r"snapshots/([^/]+)/"				,		lowerCamelCase					)
	if search is None:
		return None
	__lowerCAmelCase												=  search.groups()[0]
	return commit_hash if REGEX_COMMIT_HASH.match(lowerCamelCase					) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowerCAmelCase	:		Union[str, Any]				=   os.path.expanduser(
    os.getenv('''HF_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''huggingface'''))
)
lowerCAmelCase	:		List[str]				=   os.path.join(hf_cache_home, '''diffusers''')



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str] = None				,		lowerCamelCase   :						Optional[str] = None					):






	'''simple docstring'''




	if new_cache_dir is None:
		__lowerCAmelCase												=  DIFFUSERS_CACHE
	if old_cache_dir is None:
		__lowerCAmelCase												=  old_diffusers_cache

	__lowerCAmelCase												=  Path(lowerCamelCase					).expanduser()
	__lowerCAmelCase												=  Path(lowerCamelCase					).expanduser()
	for old_blob_path in old_cache_dir.glob("**/blobs/*"					):
		if old_blob_path.is_file() and not old_blob_path.is_symlink():
			__lowerCAmelCase												=  new_cache_dir / old_blob_path.relative_to(lowerCamelCase					)
			new_blob_path.parent.mkdir(parents=lowerCamelCase				,		exist_ok=lowerCamelCase					)
			os.replace(lowerCamelCase				,		lowerCamelCase					)
			try:
				os.symlink(lowerCamelCase				,		lowerCamelCase					)
			except OSError:
				logger.warning(
				    "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."					)
	# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


lowerCAmelCase	:		str				=   os.path.join(DIFFUSERS_CACHE, '''version_diffusers_cache.txt''')
if not os.path.isfile(cache_version_file):
	lowerCAmelCase	:		Any				=   0
else:
	with open(cache_version_file) as f:
		try:
			lowerCAmelCase	:		Union[str, Any]				=   int(f.read())
		except ValueError:
			lowerCAmelCase	:		Optional[Any]				=   0

if cache_version < 1:
	lowerCAmelCase	:		Tuple				=   os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
	if old_cache_is_not_empty:
		logger.warning(
		    '''The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '''
		    '''existing cached models. This is a one-time operation, you can interrupt it or run it '''
		    '''later by calling `diffusers.utils.hub_utils.move_cache()`.'''
		)
		try:
			move_cache()
		except Exception as e:
			lowerCAmelCase	:		Union[str, Any]				=   '''\n'''.join(traceback.format_tb(e.__traceback__))
			logger.error(
			    f'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
			    '''file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '''
			    '''message and we will do our best to help.'''
			)

if cache_version < 1:
	try:
		os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
		with open(cache_version_file, '''w''') as f:
			f.write('''1''')
	except Exception:
		logger.warning(
		    f'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
		    '''the directory exists and can be written to.'''
		)



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						Optional[str] = None					):






	'''simple docstring'''




	if variant is not None:
		__lowerCAmelCase												=  weights_name.split("."					)
		__lowerCAmelCase												=  splits[:-1] + [variant] + splits[-1:]
		__lowerCAmelCase												=  ".".join(lowerCamelCase					)

	return weights_name




def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		*,
    lowerCamelCase   :						Dict				,		lowerCamelCase   :						str				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Any				,		lowerCamelCase   :						str				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						str				,		lowerCamelCase   :						List[Any]				,		lowerCamelCase   :						Tuple=None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  str(lowerCamelCase					)
	if os.path.isfile(lowerCamelCase					):
		return pretrained_model_name_or_path
	elif os.path.isdir(lowerCamelCase					):
		if os.path.isfile(os.path.join(lowerCamelCase				,		lowerCamelCase					)					):
			# Load from a PyTorch checkpoint
			__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		lowerCamelCase					)
			return model_file
		elif subfolder is not None and os.path.isfile(
		    os.path.join(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)					):
			__lowerCAmelCase												=  os.path.join(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)
			return model_file
		else:
			raise EnvironmentError(
			    f'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'''					)
	else:
		# 1. First check if deprecated way of loading from branches is used
		if (
		    revision in DEPRECATED_REVISION_ARGS
		    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
		    and version.parse(version.parse(lowerCamelCase					).base_version					) >= version.parse("0.20.0"					)
		):
			try:
				__lowerCAmelCase												=  hf_hub_download(
				    lowerCamelCase				,		filename=_add_variant(lowerCamelCase				,		lowerCamelCase					)				,		cache_dir=lowerCamelCase				,		force_download=lowerCamelCase				,		proxies=lowerCamelCase				,		resume_download=lowerCamelCase				,		local_files_only=lowerCamelCase				,		use_auth_token=lowerCamelCase				,		user_agent=lowerCamelCase				,		subfolder=lowerCamelCase				,		revision=revision or commit_hash				,		)
				warnings.warn(
				    f'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'''				,		lowerCamelCase				,		)
				return model_file
			except:  # noqa: E722
				warnings.warn(
				    f'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(lowerCamelCase				,		lowerCamelCase					)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(lowerCamelCase				,		lowerCamelCase					)}\' so that the correct variant file can be added.'''				,		lowerCamelCase				,		)
		try:
			# 2. Load model file as usual
			__lowerCAmelCase												=  hf_hub_download(
			    lowerCamelCase				,		filename=lowerCamelCase				,		cache_dir=lowerCamelCase				,		force_download=lowerCamelCase				,		proxies=lowerCamelCase				,		resume_download=lowerCamelCase				,		local_files_only=lowerCamelCase				,		use_auth_token=lowerCamelCase				,		user_agent=lowerCamelCase				,		subfolder=lowerCamelCase				,		revision=revision or commit_hash				,		)
			return model_file

		except RepositoryNotFoundError:
			raise EnvironmentError(
			    f'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
			    "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
			    "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
			    "login`."					)
		except RevisionNotFoundError:
			raise EnvironmentError(
			    f'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
			    "this model name. Check the model page at "
			    f'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'''					)
		except EntryNotFoundError:
			raise EnvironmentError(
			    f'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'''					)
		except HTTPError as err:
			raise EnvironmentError(
			    f'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'''					)
		except ValueError:
			raise EnvironmentError(
			    f'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
			    f''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
			    f''' directory containing a file named {weights_name} or'''
			    " \nCheckout your internet connection or see how to run the library in"
			    " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."					)
		except EnvironmentError:
			raise EnvironmentError(
			    f'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
			    "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
			    f'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
			    f'''containing a file named {weights_name}'''					)




'''simple docstring'''



from __future__ import annotations

import math



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




	if len(lowerCamelCase					) != 2 or len(a[0]					) != 2 or len(lowerCamelCase					) != 2 or len(b[0]					) != 2:
		raise Exception("Matrices are not 2x2"					)
	__lowerCAmelCase												=  [
	    [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   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




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

	__lowerCAmelCase												=  len(lowerCamelCase					)
	__lowerCAmelCase												=  matrix_length // 2

	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase				,		lowerCamelCase					)] for i in range(lowerCamelCase					)]
	__lowerCAmelCase												=  [
	    [a[i][j] for j in range(lowerCamelCase				,		lowerCamelCase					)] for i in range(lowerCamelCase				,		lowerCamelCase					)
	]

	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase					)] for i in range(lowerCamelCase					)]
	__lowerCAmelCase												=  [[a[i][j] for j in range(lowerCamelCase					)] for i in range(lowerCamelCase				,		lowerCamelCase					)]

	return top_left, top_right, bot_left, bot_right



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						list					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




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

	__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  split_matrix(lowerCamelCase					)
	__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase												=  split_matrix(lowerCamelCase					)

	__lowerCAmelCase												=  actual_strassen(lowerCamelCase				,		matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  actual_strassen(lowerCamelCase				,		matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)
	__lowerCAmelCase												=  actual_strassen(matrix_subtraction(lowerCamelCase				,		lowerCamelCase					)				,		matrix_addition(lowerCamelCase				,		lowerCamelCase					)					)

	__lowerCAmelCase												=  matrix_addition(matrix_subtraction(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_addition(lowerCamelCase				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_addition(lowerCamelCase				,		lowerCamelCase					)
	__lowerCAmelCase												=  matrix_subtraction(matrix_subtraction(matrix_addition(lowerCamelCase				,		lowerCamelCase					)				,		lowerCamelCase					)				,		lowerCamelCase					)

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



def   __lowerCAmelCase					(							lowerCamelCase   :						list				,		lowerCamelCase   :						list					):






	'''simple docstring'''




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

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

	__lowerCAmelCase												=  max(*lowerCamelCase				,		*lowerCamelCase					)
	__lowerCAmelCase												=  int(math.pow(2				,		math.ceil(math.loga(lowerCamelCase					)					)					)					)
	__lowerCAmelCase												=  matrixa
	__lowerCAmelCase												=  matrixa

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

	__lowerCAmelCase												=  actual_strassen(lowerCamelCase				,		lowerCamelCase					)

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


if __name__ == "__main__":
	lowerCAmelCase	:		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],
	]
	lowerCAmelCase	:		Any				=   [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
	print(strassen(matrixa, matrixa))