
'''simple docstring'''



import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase    =		logging.get_logger(__name__)


def A				(__lowerCamelCase				:Optional[int]		):
						print("""Loading config file..."""		)

						def flatten_yaml_as_dict(__lowerCamelCase				:Any			,     __lowerCamelCase				:str=""			,     __lowerCamelCase				:Optional[int]="."		):
												_lowerCAmelCase  						=			[]
												for k, v in d.items():
																		_lowerCAmelCase  						=			parent_key + sep + k if parent_key else k
																		if isinstance(__lowerCamelCase			,     collections.abc.MutableMapping		):
																								items.extend(flatten_yaml_as_dict(__lowerCamelCase			,     __lowerCamelCase			,     sep=__lowerCamelCase		).items()		)
																		else:
																								items.append((new_key, v)		)
												return dict(__lowerCamelCase		)

						_lowerCAmelCase  						=			argparse.Namespace()
						with open(__lowerCamelCase			,     """r"""		) as yaml_file:
												try:
																		_lowerCAmelCase  						=			yaml.load(__lowerCamelCase			,     Loader=yaml.FullLoader		)

																		_lowerCAmelCase  						=			flatten_yaml_as_dict(__lowerCamelCase		)
																		for k, v in flat_cfg.items():
																								setattr(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)
												except yaml.YAMLError as exc:
																		logger.error("""Error while loading config file: {}. Error message: {}""".format(__lowerCamelCase			,     str(__lowerCamelCase		)		)		)
						return config


def A				(__lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Dict		):
						_lowerCAmelCase  						=			MobileViTVaConfig()

						_lowerCAmelCase  						=			False

						# dataset
						if task_name.startswith("""imagenet1k_"""		):
												_lowerCAmelCase  						=			1000
												if int(task_name.strip().split("""_"""		)[-1]		) == 384:
																		_lowerCAmelCase  						=			384
												else:
																		_lowerCAmelCase  						=			256
												_lowerCAmelCase  						=			"""imagenet-1k-id2label.json"""
						elif task_name.startswith("""imagenet21k_to_1k_"""		):
												_lowerCAmelCase  						=			21000
												if int(task_name.strip().split("""_"""		)[-1]		) == 384:
																		_lowerCAmelCase  						=			384
												else:
																		_lowerCAmelCase  						=			256
												_lowerCAmelCase  						=			"""imagenet-22k-id2label.json"""
						elif task_name.startswith("""ade20k_"""		):
												_lowerCAmelCase  						=			151
												_lowerCAmelCase  						=			512
												_lowerCAmelCase  						=			"""ade20k-id2label.json"""
												_lowerCAmelCase  						=			True
						elif task_name.startswith("""voc_"""		):
												_lowerCAmelCase  						=			21
												_lowerCAmelCase  						=			512
												_lowerCAmelCase  						=			"""pascal-voc-id2label.json"""
												_lowerCAmelCase  						=			True

						# orig_config
						_lowerCAmelCase  						=			load_orig_config_file(__lowerCamelCase		)
						assert getattr(__lowerCamelCase			,     """model.classification.name"""			,     -1		) == "mobilevit_v2", "Invalid model"
						_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     """model.classification.mitv2.width_multiplier"""			,     1.0		)
						assert (
						    getattr(__lowerCamelCase			,     """model.classification.mitv2.attn_norm_layer"""			,     -1		) == "layer_norm_2d"
						), "Norm layers other than layer_norm_2d is not supported"
						_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     """model.classification.activation.name"""			,     """swish"""		)
						# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

						if is_segmentation_model:
												_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     """model.segmentation.output_stride"""			,     16		)
												if "_deeplabv3" in task_name:
																		_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     """model.segmentation.deeplabv3.aspp_rates"""			,     [12, 24, 36]		)
																		_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     """model.segmentation.deeplabv3.aspp_out_channels"""			,     512		)
																		_lowerCAmelCase  						=			getattr(__lowerCamelCase			,     """model.segmentation.deeplabv3.aspp_dropout"""			,     0.1		)

    # id2label
						_lowerCAmelCase  						=			"""huggingface/label-files"""
						_lowerCAmelCase  						=			json.load(open(hf_hub_download(__lowerCamelCase			,     __lowerCamelCase			,     repo_type="""dataset"""		)			,     """r"""		)		)
						_lowerCAmelCase  						=			{int(__lowerCamelCase		): v for k, v in idalabel.items()}
						_lowerCAmelCase  						=			idalabel
						_lowerCAmelCase  						=			{v: k for k, v in idalabel.items()}

						return config


def A				(__lowerCamelCase				:Any			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:Optional[Any]		):
						_lowerCAmelCase  						=			dct.pop(__lowerCamelCase		)
						_lowerCAmelCase  						=			val


def A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:str=False		):
						if base_model:
												_lowerCAmelCase  						=			""""""
						else:
												_lowerCAmelCase  						=			"""mobilevitv2."""

						_lowerCAmelCase  						=			[]
						for k in state_dict.keys():
												if k[:8] == "encoder.":
																		_lowerCAmelCase  						=			k[8:]
												else:
																		_lowerCAmelCase  						=			k

												if ".block." in k:
																		_lowerCAmelCase  						=			k_new.replace(""".block."""			,     """."""		)
												if ".conv." in k:
																		_lowerCAmelCase  						=			k_new.replace(""".conv."""			,     """.convolution."""		)
												if ".norm." in k:
																		_lowerCAmelCase  						=			k_new.replace(""".norm."""			,     """.normalization."""		)

												if "conv_1." in k:
																		_lowerCAmelCase  						=			k_new.replace("""conv_1."""			,     f'{model_prefix}conv_stem.'		)
												for i in [1, 2]:
																		if f'layer_{i}.' in k:
																								_lowerCAmelCase  						=			k_new.replace(f'layer_{i}.'			,     f'{model_prefix}encoder.layer.{i-1}.layer.'		)
												if ".exp_1x1." in k:
																		_lowerCAmelCase  						=			k_new.replace(""".exp_1x1."""			,     """.expand_1x1."""		)
												if ".red_1x1." in k:
																		_lowerCAmelCase  						=			k_new.replace(""".red_1x1."""			,     """.reduce_1x1."""		)

												for i in [3, 4, 5]:
																		if f'layer_{i}.0.' in k:
																								_lowerCAmelCase  						=			k_new.replace(f'layer_{i}.0.'			,     f'{model_prefix}encoder.layer.{i-1}.downsampling_layer.'		)
																		if f'layer_{i}.1.local_rep.0.' in k:
																								_lowerCAmelCase  						=			k_new.replace(f'layer_{i}.1.local_rep.0.'			,     f'{model_prefix}encoder.layer.{i-1}.conv_kxk.'		)
																		if f'layer_{i}.1.local_rep.1.' in k:
																								_lowerCAmelCase  						=			k_new.replace(f'layer_{i}.1.local_rep.1.'			,     f'{model_prefix}encoder.layer.{i-1}.conv_1x1.'		)

												for i in [3, 4, 5]:
																		if i == 3:
																								_lowerCAmelCase  						=			[0, 1]
																		elif i == 4:
																								_lowerCAmelCase  						=			[0, 1, 2, 3]
																		elif i == 5:
																								_lowerCAmelCase  						=			[0, 1, 2]

																		for j in j_in:
																								if f'layer_{i}.1.global_rep.{j}.' in k:
																														_lowerCAmelCase  						=			k_new.replace(
																														    f'layer_{i}.1.global_rep.{j}.'			,     f'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'		)
																		if f'layer_{i}.1.global_rep.{j+1}.' in k:
																								_lowerCAmelCase  						=			k_new.replace(
																								    f'layer_{i}.1.global_rep.{j+1}.'			,     f'{model_prefix}encoder.layer.{i-1}.layernorm.'		)

																		if f'layer_{i}.1.conv_proj.' in k:
																								_lowerCAmelCase  						=			k_new.replace(f'layer_{i}.1.conv_proj.'			,     f'{model_prefix}encoder.layer.{i-1}.conv_projection.'		)

												if "pre_norm_attn.0." in k:
																		_lowerCAmelCase  						=			k_new.replace("""pre_norm_attn.0."""			,     """layernorm_before."""		)
												if "pre_norm_attn.1." in k:
																		_lowerCAmelCase  						=			k_new.replace("""pre_norm_attn.1."""			,     """attention."""		)
												if "pre_norm_ffn.0." in k:
																		_lowerCAmelCase  						=			k_new.replace("""pre_norm_ffn.0."""			,     """layernorm_after."""		)
												if "pre_norm_ffn.1." in k:
																		_lowerCAmelCase  						=			k_new.replace("""pre_norm_ffn.1."""			,     """ffn.conv1."""		)
												if "pre_norm_ffn.3." in k:
																		_lowerCAmelCase  						=			k_new.replace("""pre_norm_ffn.3."""			,     """ffn.conv2."""		)

												if "classifier.1." in k:
																		_lowerCAmelCase  						=			k_new.replace("""classifier.1."""			,     """classifier."""		)

												if "seg_head." in k:
																		_lowerCAmelCase  						=			k_new.replace("""seg_head."""			,     """segmentation_head."""		)
												if ".aspp_layer." in k:
																		_lowerCAmelCase  						=			k_new.replace(""".aspp_layer."""			,     """."""		)
												if ".aspp_pool." in k:
																		_lowerCAmelCase  						=			k_new.replace(""".aspp_pool."""			,     """."""		)

												rename_keys.append((k, k_new)		)
						return rename_keys


def A				(__lowerCamelCase				:Tuple		):
						_lowerCAmelCase  						=			[]
						for k in state_dict.keys():
												if k.startswith("""seg_head.aux_head."""		):
																		keys_to_ignore.append(__lowerCamelCase		)
						for k in keys_to_ignore:
												state_dict.pop(__lowerCamelCase			,     __lowerCamelCase		)


def A				():
						_lowerCAmelCase  						=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
						_lowerCAmelCase  						=			Image.open(requests.get(__lowerCamelCase			,     stream=__lowerCamelCase		).raw		)
						return im


@torch.no_grad()
def A				(__lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:List[str]			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:int		):
						_lowerCAmelCase  						=			get_mobilevitva_config(__lowerCamelCase			,     __lowerCamelCase		)

						# load original state_dict
						_lowerCAmelCase  						=			torch.load(__lowerCamelCase			,     map_location="""cpu"""		)

						# load huggingface model
						if task_name.startswith("""ade20k_"""		) or task_name.startswith("""voc_"""		):
												_lowerCAmelCase  						=			MobileViTVaForSemanticSegmentation(__lowerCamelCase		).eval()
												_lowerCAmelCase  						=			False
						else:
												_lowerCAmelCase  						=			MobileViTVaForImageClassification(__lowerCamelCase		).eval()
												_lowerCAmelCase  						=			False

						# remove and rename some keys of load the original model
						_lowerCAmelCase  						=			checkpoint
						remove_unused_keys(__lowerCamelCase		)
						_lowerCAmelCase  						=			create_rename_keys(__lowerCamelCase			,     base_model=__lowerCamelCase		)
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase		)

						# load modified state_dict
						model.load_state_dict(__lowerCamelCase		)

						# Check outputs on an image, prepared by MobileViTImageProcessor
						_lowerCAmelCase  						=			MobileViTImageProcessor(crop_size=config.image_size			,     size=config.image_size + 32		)
						_lowerCAmelCase  						=			image_processor(images=prepare_img()			,     return_tensors="""pt"""		)
						_lowerCAmelCase  						=			model(**__lowerCamelCase		)

						# verify classification model
						if task_name.startswith("""imagenet"""		):
												_lowerCAmelCase  						=			outputs.logits
												_lowerCAmelCase  						=			logits.argmax(-1		).item()
												print("""Predicted class:"""			,     model.config.idalabel[predicted_class_idx]		)
												if task_name.startswith("""imagenet1k_256"""		) and config.width_multiplier == 1.0:
																		# expected_logits for base variant
																		_lowerCAmelCase  						=			torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]		)
																		assert torch.allclose(logits[0, :3]			,     __lowerCamelCase			,     atol=1e-4		)

						Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
						print(f'Saving model {task_name} to {pytorch_dump_folder_path}'		)
						model.save_pretrained(__lowerCamelCase		)
						print(f'Saving image processor to {pytorch_dump_folder_path}'		)
						image_processor.save_pretrained(__lowerCamelCase		)


if __name__ == "__main__":
						_lowercase    =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--task""",
						    default="""imagenet1k_256""",
						    type=str,
						    help=(
						        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
						        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
						    ),
						    choices=[
						        """imagenet1k_256""",
						        """imagenet1k_384""",
						        """imagenet21k_to_1k_256""",
						        """imagenet21k_to_1k_384""",
						        """ade20k_deeplabv3""",
						        """voc_deeplabv3""",
						    ],
						)

						parser.add_argument(
						    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
						)
						parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
						parser.add_argument(
						    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
						)

						_lowercase    =		parser.parse_args()
						convert_mobilevitva_checkpoint(
						    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
						)


'''simple docstring'''



from __future__ import annotations

import math


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						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 A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						return [
						    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]		)		)]
						    for row in range(len(__lowerCamelCase		)		)
						]


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						return [
						    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]		)		)]
						    for row in range(len(__lowerCamelCase		)		)
						]


def A				(__lowerCamelCase				:list		):
						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 A				(__lowerCamelCase				:list		):
						return len(__lowerCamelCase		), len(matrix[0]		)


def A				(__lowerCamelCase				:list		):
						print("""\n""".join(str(__lowerCamelCase		) for line in matrix		)		)


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						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 A				(__lowerCamelCase				:list			,     __lowerCamelCase				:list		):
						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__":
						_lowercase    =		[
						    [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],
						]
						_lowercase    =		[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
						print(strassen(matrixa, matrixa))
