






'''simple docstring'''







import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A			:    int					  =logging.get_logger(__name__)

_A			:    Tuple					  ={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class _lowercase			(       _lowercase	):
							a          = """conditional_detr"""
							a          = ["""past_key_values"""]
							a          = {
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """encoder_attention_heads""",
							}




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       int=True    ,							UpperCamelCase__:       str=None    ,							UpperCamelCase__:       Any=3    ,							UpperCamelCase__:       Dict=300    ,							UpperCamelCase__:       int=6    ,							UpperCamelCase__:       Optional[int]=2_048    ,							UpperCamelCase__:       Tuple=8    ,							UpperCamelCase__:       List[str]=6    ,							UpperCamelCase__:       Optional[Any]=2_048    ,							UpperCamelCase__:       List[str]=8    ,							UpperCamelCase__:       Optional[Any]=0.0    ,							UpperCamelCase__:       Optional[Any]=0.0    ,							UpperCamelCase__:       Optional[Any]=True    ,							UpperCamelCase__:       Tuple="relu"    ,							UpperCamelCase__:       Any=256    ,							UpperCamelCase__:       int=0.1    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Optional[int]=0.0    ,							UpperCamelCase__:       Union[str, Any]=0.02    ,							UpperCamelCase__:       List[Any]=1.0    ,							UpperCamelCase__:       Union[str, Any]=False    ,							UpperCamelCase__:       Dict="sine"    ,							UpperCamelCase__:       Dict="resnet50"    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Optional[Any]=False    ,							UpperCamelCase__:       Dict=2    ,							UpperCamelCase__:       Union[str, Any]=5    ,							UpperCamelCase__:       Optional[Any]=2    ,							UpperCamelCase__:       Dict=1    ,							UpperCamelCase__:       List[str]=1    ,							UpperCamelCase__:       Union[str, Any]=2    ,							UpperCamelCase__:       Optional[Any]=5    ,							UpperCamelCase__:       Optional[Any]=2    ,							UpperCamelCase__:       str=0.25    ,							**UpperCamelCase__:       Tuple    ,							):
														if backbone_config is not None and use_timm_backbone:
																					raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""  )

														if not use_timm_backbone:
																					if backbone_config is None:
																												logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""  )
																												lowerCamelCase__      :			Optional[int] 						= CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]  )
																					elif isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																												lowerCamelCase__      :			List[Any] 						= backbone_config.get("""model_type"""  )
																												lowerCamelCase__      :			List[Any] 						= CONFIG_MAPPING[backbone_model_type]
																												lowerCamelCase__      :			Optional[Any] 						= config_class.from_dict(UpperCamelCase__  )

														lowerCamelCase__      :			List[Any] 						= use_timm_backbone
														lowerCamelCase__      :			str 						= backbone_config
														lowerCamelCase__      :			int 						= num_channels
														lowerCamelCase__      :			int 						= num_queries
														lowerCamelCase__      :			Dict 						= d_model
														lowerCamelCase__      :			Optional[Any] 						= encoder_ffn_dim
														lowerCamelCase__      :			Union[str, Any] 						= encoder_layers
														lowerCamelCase__      :			int 						= encoder_attention_heads
														lowerCamelCase__      :			str 						= decoder_ffn_dim
														lowerCamelCase__      :			Optional[Any] 						= decoder_layers
														lowerCamelCase__      :			List[str] 						= decoder_attention_heads
														lowerCamelCase__      :			Dict 						= dropout
														lowerCamelCase__      :			List[Any] 						= attention_dropout
														lowerCamelCase__      :			Optional[Any] 						= activation_dropout
														lowerCamelCase__      :			Optional[Any] 						= activation_function
														lowerCamelCase__      :			Any 						= init_std
														lowerCamelCase__      :			Tuple 						= init_xavier_std
														lowerCamelCase__      :			Dict 						= encoder_layerdrop
														lowerCamelCase__      :			Any 						= decoder_layerdrop
														lowerCamelCase__      :			List[Any] 						= encoder_layers
														lowerCamelCase__      :			str 						= auxiliary_loss
														lowerCamelCase__      :			List[str] 						= position_embedding_type
														lowerCamelCase__      :			List[Any] 						= backbone
														lowerCamelCase__      :			Tuple 						= use_pretrained_backbone
														lowerCamelCase__      :			int 						= dilation
														# Hungarian matcher
														lowerCamelCase__      :			List[str] 						= class_cost
														lowerCamelCase__      :			str 						= bbox_cost
														lowerCamelCase__      :			Union[str, Any] 						= giou_cost
														# Loss coefficients
														lowerCamelCase__      :			str 						= mask_loss_coefficient
														lowerCamelCase__      :			List[str] 						= dice_loss_coefficient
														lowerCamelCase__      :			Optional[int] 						= cls_loss_coefficient
														lowerCamelCase__      :			Optional[Any] 						= bbox_loss_coefficient
														lowerCamelCase__      :			str 						= giou_loss_coefficient
														lowerCamelCase__      :			Tuple 						= focal_alpha
														super().__init__(is_encoder_decoder=UpperCamelCase__    ,							**UpperCamelCase__  )




							@property
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														return self.encoder_attention_heads




							@property
							def 	lowerCamelCase_       (							self:       Any  ):
														return self.d_model




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			str 						= copy.deepcopy(self.__dict__  )
														if self.backbone_config is not None:
																					lowerCamelCase__      :			List[Any] 						= self.backbone_config.to_dict()
														lowerCamelCase__      :			Union[str, Any] 						= self.__class__.model_type
														return output



class _lowercase			(       _lowercase	):
							a          = version.parse("""1.11"""	)




							@property
							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														return OrderedDict(
														    [
														        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
														        ("""pixel_mask""", {0: """batch"""}),
														    ]  )




							@property
							def 	lowerCamelCase_       (							self:       int  ):
														return 1e-5




							@property
							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														return 12









'''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			:    int					  =logging.get_logger(__name__)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							print("""Loading config file..."""   )

							def flatten_yaml_as_dict(UpperCamelCase       ,    UpperCamelCase=""       ,    UpperCamelCase="."   ):
														lowerCamelCase__      :			Optional[int] 						= []
														for k, v in d.items():
																					lowerCamelCase__      :			Optional[int] 						= 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__      :			Any 						= argparse.Namespace()
							with open(UpperCamelCase       ,    """r"""   ) as yaml_file:
														try:
																					lowerCamelCase__      :			int 						= yaml.load(UpperCamelCase       ,    Loader=yaml.FullLoader   )

																					lowerCamelCase__      :			Tuple 						= 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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaConfig()

							lowerCamelCase__      :			str 						= False

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

							# orig_config
							lowerCamelCase__      :			Optional[int] 						= load_orig_config_file(UpperCamelCase   )
							assert getattr(UpperCamelCase       ,    """model.classification.name"""       ,    -1   ) == "mobilevit_v2", "Invalid model"
							lowerCamelCase__      :			int 						= 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__      :			Tuple 						= getattr(UpperCamelCase       ,    """model.classification.activation.name"""       ,    """swish"""   )
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

    # id2label
							lowerCamelCase__      :			Tuple 						= """huggingface/label-files"""
							lowerCamelCase__      :			Optional[Any] 						= json.load(open(hf_hub_download(UpperCamelCase       ,    UpperCamelCase       ,    repo_type="""dataset"""   )       ,    """r"""   )   )
							lowerCamelCase__      :			Union[str, Any] 						= {int(UpperCamelCase   ): v for k, v in idalabel.items()}
							lowerCamelCase__      :			int 						= idalabel
							lowerCamelCase__      :			Optional[Any] 						= {v: k for k, v in idalabel.items()}

							return config





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Any:
							lowerCamelCase__      :			List[Any] 						= dct.pop(UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= val





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> Tuple:
							if base_model:
														lowerCamelCase__      :			Optional[int] 						= """"""
							else:
														lowerCamelCase__      :			Optional[Any] 						= """mobilevitv2."""

							lowerCamelCase__      :			List[str] 						= []
							for k in state_dict.keys():
														if k[:8] == "encoder.":
																					lowerCamelCase__      :			Optional[Any] 						= k[8:]
														else:
																					lowerCamelCase__      :			Optional[Any] 						= k

														if ".block." in k:
																					lowerCamelCase__      :			Dict 						= k_new.replace(""".block."""       ,    """."""   )
														if ".conv." in k:
																					lowerCamelCase__      :			List[Any] 						= k_new.replace(""".conv."""       ,    """.convolution."""   )
														if ".norm." in k:
																					lowerCamelCase__      :			str 						= k_new.replace(""".norm."""       ,    """.normalization."""   )

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

														for i in [3, 4, 5]:
																					if f'''layer_{i}.0.''' in k:
																												lowerCamelCase__      :			List[str] 						= 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__      :			Optional[Any] 						= 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__      :			Dict 						= 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__      :			int 						= [0, 1]
																					elif i == 4:
																												lowerCamelCase__      :			str 						= [0, 1, 2, 3]
																					elif i == 5:
																												lowerCamelCase__      :			Dict 						= [0, 1, 2]

																					for j in j_in:
																												if f'''layer_{i}.1.global_rep.{j}.''' in k:
																																			lowerCamelCase__      :			List[Any] 						= 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__      :			Optional[int] 						= 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__      :			Optional[int] 						= 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__      :			str 						= k_new.replace("""pre_norm_attn.0."""       ,    """layernorm_before."""   )
														if "pre_norm_attn.1." in k:
																					lowerCamelCase__      :			str 						= k_new.replace("""pre_norm_attn.1."""       ,    """attention."""   )
														if "pre_norm_ffn.0." in k:
																					lowerCamelCase__      :			Optional[Any] 						= k_new.replace("""pre_norm_ffn.0."""       ,    """layernorm_after."""   )
														if "pre_norm_ffn.1." in k:
																					lowerCamelCase__      :			Union[str, Any] 						= k_new.replace("""pre_norm_ffn.1."""       ,    """ffn.conv1."""   )
														if "pre_norm_ffn.3." in k:
																					lowerCamelCase__      :			List[Any] 						= k_new.replace("""pre_norm_ffn.3."""       ,    """ffn.conv2."""   )

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

														if "seg_head." in k:
																					lowerCamelCase__      :			Optional[int] 						= k_new.replace("""seg_head."""       ,    """segmentation_head."""   )
														if ".aspp_layer." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace(""".aspp_layer."""       ,    """."""   )
														if ".aspp_pool." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace(""".aspp_pool."""       ,    """."""   )

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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							lowerCamelCase__      :			Union[str, Any] 						= []
							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 				SCREAMING_SNAKE_CASE_ ()   -> Dict:
							lowerCamelCase__      :			Union[str, Any] 						= """http://images.cocodataset.org/val2017/000000039769.jpg"""
							# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
							lowerCamelCase__      :			Tuple 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )
							return im





@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			str 						= get_mobilevitva_config(UpperCamelCase       ,    UpperCamelCase   )

							# load original state_dict
							lowerCamelCase__      :			List[str] 						= torch.load(UpperCamelCase       ,    map_location="""cpu"""   )

							# load huggingface model
							if task_name.startswith("""ade20k_"""   ) or task_name.startswith("""voc_"""   ):
														lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation(UpperCamelCase   ).eval()
														lowerCamelCase__      :			Tuple 						= False
							else:
														lowerCamelCase__      :			int 						= MobileViTVaForImageClassification(UpperCamelCase   ).eval()
														lowerCamelCase__      :			Optional[Any] 						= False

							# remove and rename some keys of load the original model
							lowerCamelCase__      :			Tuple 						= checkpoint
							remove_unused_keys(UpperCamelCase   )
							lowerCamelCase__      :			Union[str, Any] 						= 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__      :			int 						= MobileViTImageProcessor(crop_size=config.image_size       ,    size=config.image_size + 32   )
							lowerCamelCase__      :			Dict 						= image_processor(images=prepare_img()       ,    return_tensors="""pt"""   )
							lowerCamelCase__      :			str 						= model(**UpperCamelCase   )

							# verify classification model
							if task_name.startswith("""imagenet"""   ):
														lowerCamelCase__      :			Dict 						= outputs.logits
														lowerCamelCase__      :			Optional[Any] 						= 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__      :			Optional[Any] 						= torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-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			:    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 . '''
			        '''
                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.'''
			)

			_A			:    Dict					  =parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)

