

'''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()
A_	= logging.get_logger(__name__)







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			Dict:
					print('Loading config file...'	)

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

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

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







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase	)       ->			Optional[int]:
					lowerCamelCase_  				=      MobileViTVaConfig()

					lowerCamelCase_  				=      False

					# dataset
					if task_name.startswith('imagenet1k_'	):
										lowerCamelCase_  				=      10_00
										if int(task_name.strip().split('_'	)[-1]	) == 3_84:
															lowerCamelCase_  				=      3_84
										else:
															lowerCamelCase_  				=      2_56
										lowerCamelCase_  				=      'imagenet-1k-id2label.json'
					elif task_name.startswith('imagenet21k_to_1k_'	):
										lowerCamelCase_  				=      2_10_00
										if int(task_name.strip().split('_'	)[-1]	) == 3_84:
															lowerCamelCase_  				=      3_84
										else:
															lowerCamelCase_  				=      2_56
										lowerCamelCase_  				=      'imagenet-22k-id2label.json'
					elif task_name.startswith('ade20k_'	):
										lowerCamelCase_  				=      1_51
										lowerCamelCase_  				=      5_12
										lowerCamelCase_  				=      'ade20k-id2label.json'
										lowerCamelCase_  				=      True
					elif task_name.startswith('voc_'	):
										lowerCamelCase_  				=      21
										lowerCamelCase_  				=      5_12
										lowerCamelCase_  				=      'pascal-voc-id2label.json'
										lowerCamelCase_  				=      True

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

					if is_segmentation_model:
										lowerCamelCase_  				=      getattr(__UpperCamelCase    ,'model.segmentation.output_stride'    ,16	)
										if "_deeplabv3" in task_name:
															lowerCamelCase_  				=      getattr(__UpperCamelCase    ,'model.segmentation.deeplabv3.aspp_rates'    ,[12, 24, 36]	)
															lowerCamelCase_  				=      getattr(__UpperCamelCase    ,'model.segmentation.deeplabv3.aspp_out_channels'    ,5_12	)
															lowerCamelCase_  				=      getattr(__UpperCamelCase    ,'model.segmentation.deeplabv3.aspp_dropout'    ,0.1	)

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

					return config







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			List[str]:
					lowerCamelCase_  				=      dct.pop(__UpperCamelCase	)
					lowerCamelCase_  				=      val







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase=False	)       ->			Dict:
					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   _UpperCamelCase	(		__UpperCamelCase	)       ->			Optional[Any]:
					lowerCamelCase_  				=      []
					for k in state_dict.keys():
										if k.startswith('seg_head.aux_head.'	):
															keys_to_ignore.append(__UpperCamelCase	)
					for k in keys_to_ignore:
										state_dict.pop(__UpperCamelCase    ,__UpperCamelCase	)







def   _UpperCamelCase	(		)       ->			Optional[Any]:
					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(__UpperCamelCase    ,stream=__UpperCamelCase	).raw	)
					return im







@torch.no_grad()
def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)       ->			Optional[int]:
					lowerCamelCase_  				=      get_mobilevitva_config(__UpperCamelCase    ,__UpperCamelCase	)

					# load original state_dict
					lowerCamelCase_  				=      torch.load(__UpperCamelCase    ,map_location='cpu'	)

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

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

					# load modified state_dict
					model.load_state_dict(__UpperCamelCase	)

					# 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(**__UpperCamelCase	)

					# 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]    ,__UpperCamelCase    ,atol=1e-4	)

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


if __name__ == "__main__":
				A_	= 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."
				)

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Optional[Any]         =	["BridgeTowerImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE		:     Tuple         =	[
	    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BridgeTowerForContrastiveLearning",
	    "BridgeTowerForImageAndTextRetrieval",
	    "BridgeTowerForMaskedLM",
	    "BridgeTowerModel",
	    "BridgeTowerPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_bridgetower import (
	    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BridgeTowerConfig,
	    BridgeTowerTextConfig,
	    BridgeTowerVisionConfig,
	)
	from .processing_bridgetower import BridgeTowerProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bridgetower import BridgeTowerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bridgetower import (
		    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BridgeTowerForContrastiveLearning,
		    BridgeTowerForImageAndTextRetrieval,
		    BridgeTowerForMaskedLM,
		    BridgeTowerModel,
		    BridgeTowerPreTrainedModel,
		)


else:
	import sys

	SCREAMING_SNAKE_CASE		:     List[Any]         =	_LazyModule(__name__, globals()["__file__"], _import_structure)






