


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()
lowerCAmelCase       =							logging.get_logger(__name__)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		print('''Loading config file...'''     )

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

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

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


		"""simple docstring"""




		lowercase__            =      MobileViTVaConfig()

		lowercase__            =      False

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

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

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

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

		return config
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      dct.pop(SCREAMING_SNAKE_CASE     )
		lowercase__            =      val
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE=False     ):


		"""simple docstring"""




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

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

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

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

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

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

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

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

				rename_keys.append((k, k_new)     )
		return rename_keys
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      []
		for k in state_dict.keys():
				if k.startswith('''seg_head.aux_head.'''     ):
						keys_to_ignore.append(SCREAMING_SNAKE_CASE     )
		for k in keys_to_ignore:
				state_dict.pop(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )
def 			_a (      ):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__            =      get_mobilevitva_config(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     )

		# load original state_dict
		lowercase__            =      torch.load(SCREAMING_SNAKE_CASE						,				map_location='''cpu'''     )

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

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

		# load modified state_dict
		model.load_state_dict(SCREAMING_SNAKE_CASE     )

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

		# verify classification model
		if task_name.startswith('''imagenet'''     ):
				lowercase__            =      outputs.logits
				lowercase__            =      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
						lowercase__            =      torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]     )
						assert torch.allclose(logits[0, :3]						,				SCREAMING_SNAKE_CASE						,				atol=1E-4     )

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


if __name__ == "__main__":
			lowerCAmelCase       =							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.'
			)

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



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						str						=      """ctrl"""
    a__	:						Dict						=      ["""past_key_values"""]
    a__	:						Tuple						=      {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(	self , __lowercase=246_534 , __lowercase=256 , __lowercase=1_280 , __lowercase=8_192 , __lowercase=48 , __lowercase=16 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=1E-6 , __lowercase=0.02 , __lowercase=True , **__lowercase , )       ->	List[Any]:
        __UpperCamelCase     :List[str]			  =		vocab_size
        __UpperCamelCase     :Optional[Any]			  =		n_positions
        __UpperCamelCase     :Dict			  =		n_embd
        __UpperCamelCase     :Dict			  =		n_layer
        __UpperCamelCase     :List[Any]			  =		n_head
        __UpperCamelCase     :int			  =		dff
        __UpperCamelCase     :Union[str, Any]			  =		resid_pdrop
        __UpperCamelCase     :Optional[int]			  =		embd_pdrop
        __UpperCamelCase     :List[Any]			  =		layer_norm_epsilon
        __UpperCamelCase     :Dict			  =		initializer_range

        __UpperCamelCase     :Any			  =		use_cache

        super().__init__(**__lowercase)





