
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()
_UpperCAmelCase :			List[Any]              =	logging.get_logger(__name__)


def 				A	( lowercase			)       ->  Dict:
		'''simple docstring'''

		print('Loading config file...'			)

		def flatten_yaml_as_dict(lowercase   ,	lowercase=""   ,	lowercase="."			):
				UpperCamelCase          =					[]
				for k, v in d.items():
						UpperCamelCase          =					parent_key + sep + k if parent_key else k
						if isinstance(lowercase   ,	collections.abc.MutableMapping			):
								items.extend(flatten_yaml_as_dict(lowercase   ,	lowercase   ,	sep=lowercase			).items()			)
						else:
								items.append((new_key, v)			)
				return dict(lowercase			)

		UpperCamelCase          =					argparse.Namespace()
		with open(lowercase   ,	'r'			) as yaml_file:
				try:
						UpperCamelCase          =					yaml.load(lowercase   ,	Loader=yaml.FullLoader			)

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


def 				A	( lowercase   ,	lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					MobileViTVaConfig()

		UpperCamelCase          =					False

		# dataset
		if task_name.startswith('imagenet1k_'			):
				UpperCamelCase          =					1_000
				if int(task_name.strip().split('_'			)[-1]			) == 384:
						UpperCamelCase          =					384
				else:
						UpperCamelCase          =					256
				UpperCamelCase          =					'imagenet-1k-id2label.json'
		elif task_name.startswith('imagenet21k_to_1k_'			):
				UpperCamelCase          =					21_000
				if int(task_name.strip().split('_'			)[-1]			) == 384:
						UpperCamelCase          =					384
				else:
						UpperCamelCase          =					256
				UpperCamelCase          =					'imagenet-22k-id2label.json'
		elif task_name.startswith('ade20k_'			):
				UpperCamelCase          =					151
				UpperCamelCase          =					512
				UpperCamelCase          =					'ade20k-id2label.json'
				UpperCamelCase          =					True
		elif task_name.startswith('voc_'			):
				UpperCamelCase          =					21
				UpperCamelCase          =					512
				UpperCamelCase          =					'pascal-voc-id2label.json'
				UpperCamelCase          =					True

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

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

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

		return config


def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					dct.pop(lowercase			)
		UpperCamelCase          =					val


def 				A	( lowercase   ,	lowercase=False			)       ->  Any:
		'''simple docstring'''

		if base_model:
				UpperCamelCase          =					''
		else:
				UpperCamelCase          =					'mobilevitv2.'

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

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

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

				for i in [3, 4, 5]:
						if f'''layer_{i}.0.''' in k:
								UpperCamelCase          =					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:
								UpperCamelCase          =					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:
								UpperCamelCase          =					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:
								UpperCamelCase          =					[0, 1]
						elif i == 4:
								UpperCamelCase          =					[0, 1, 2, 3]
						elif i == 5:
								UpperCamelCase          =					[0, 1, 2]

						for j in j_in:
								if f'''layer_{i}.1.global_rep.{j}.''' in k:
										UpperCamelCase          =					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:
								UpperCamelCase          =					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:
								UpperCamelCase          =					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:
						UpperCamelCase          =					k_new.replace('pre_norm_attn.0.'   ,	'layernorm_before.'			)
				if "pre_norm_attn.1." in k:
						UpperCamelCase          =					k_new.replace('pre_norm_attn.1.'   ,	'attention.'			)
				if "pre_norm_ffn.0." in k:
						UpperCamelCase          =					k_new.replace('pre_norm_ffn.0.'   ,	'layernorm_after.'			)
				if "pre_norm_ffn.1." in k:
						UpperCamelCase          =					k_new.replace('pre_norm_ffn.1.'   ,	'ffn.conv1.'			)
				if "pre_norm_ffn.3." in k:
						UpperCamelCase          =					k_new.replace('pre_norm_ffn.3.'   ,	'ffn.conv2.'			)

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

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

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


def 				A	( lowercase			)       ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase          =					[]
		for k in state_dict.keys():
				if k.startswith('seg_head.aux_head.'			):
						keys_to_ignore.append(lowercase			)
		for k in keys_to_ignore:
				state_dict.pop(lowercase   ,	lowercase			)


def 				A	( )       ->  int:
		'''simple docstring'''

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


@torch.no_grad()
def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase          =					get_mobilevitva_config(lowercase   ,	lowercase			)

		# load original state_dict
		UpperCamelCase          =					torch.load(lowercase   ,	map_location='cpu'			)

		# load huggingface model
		if task_name.startswith('ade20k_'			) or task_name.startswith('voc_'			):
				UpperCamelCase          =					MobileViTVaForSemanticSegmentation(lowercase			).eval()
				UpperCamelCase          =					False
		else:
				UpperCamelCase          =					MobileViTVaForImageClassification(lowercase			).eval()
				UpperCamelCase          =					False

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

		# load modified state_dict
		model.load_state_dict(lowercase			)

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

		# verify classification model
		if task_name.startswith('imagenet'			):
				UpperCamelCase          =					outputs.logits
				UpperCamelCase          =					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
						UpperCamelCase          =					torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01]			)
						assert torch.allclose(logits[0, :3]   ,	lowercase   ,	atol=1e-4			)

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


if __name__ == "__main__":
					_UpperCAmelCase :			Optional[int]              =	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 . "
					        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
					    ),
					    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."
					)

					_UpperCAmelCase :			int              =	parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)


import pprint

import requests

_UpperCAmelCase :			Union[str, Any]              =	"https://zenquotes.io/api"


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/today'			).json()


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/random'			).json()


if __name__ == "__main__":
					_UpperCAmelCase :			str              =	random_quotes()
					pprint.pprint(response)
