




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      :						Optional[Any]							=  logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Optional[Any]:



       """simple docstring"""

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

       def flatten_yaml_as_dict(lowercase_					,							lowercase_=""					,							lowercase_="."		):
              A__ 					=       []
              for k, v in d.items():
                     A__ 					=       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_		)

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

                     A__ 					=       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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Optional[Any]:



       """simple docstring"""

       A__ 					=       MobileViTVaConfig()

       A__ 					=       False

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

       # orig_config
       A__ 					=       load_orig_config_file(lowercase_		)
       assert getattr(lowercase_					,							'''model.classification.name'''					,							-1		) == "mobilevit_v2", "Invalid model"
       A__ 					=       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"
       A__ 					=       getattr(lowercase_					,							'''model.classification.activation.name'''					,							'''swish'''		)
       # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

       return config







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_		)  ->			Union[str, Any]:



       """simple docstring"""

       A__ 					=       dct.pop(lowercase_		)
       A__ 					=       val







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=False		)  ->			Union[str, Any]:



       """simple docstring"""

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

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

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

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

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

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

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

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

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







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			List[Any]:



       """simple docstring"""

       A__ 					=       []
       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 SCREAMING_SNAKE_CASE							(			)  ->			Union[str, Any]:



       """simple docstring"""

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







@torch.no_grad()
def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			Tuple:



       """simple docstring"""

       A__ 					=       get_mobilevitva_config(lowercase_					,							lowercase_		)

       # load original state_dict
       A__ 					=       torch.load(lowercase_					,							map_location='''cpu'''		)

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

       # remove and rename some keys of load the original model
       A__ 					=       checkpoint
       remove_unused_keys(lowercase_		)
       A__ 					=       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
       A__ 					=       MobileViTImageProcessor(crop_size=config.image_size					,							size=config.image_size + 32		)
       A__ 					=       image_processor(images=prepare_img()					,							return_tensors='''pt'''		)
       A__ 					=       model(**lowercase_		)

       # verify classification model
       if task_name.startswith('''imagenet'''		):
              A__ 					=       outputs.logits
              A__ 					=       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
                     A__ 					=       torch.tensor([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]		)
                     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__":
  _lowerCamelCase      :						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."""
  )

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








'''simple docstring'''
import os
import sys

_SCREAMING_SNAKE_CASE			=						os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


_SCREAMING_SNAKE_CASE			=						[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		int:
			return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		List[str]:
			return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModel.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Optional[int]:
			return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Dict:
			return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		Union[str, Any]:
			return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def 			lowerCamelCase(							*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )		->		str:
			return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_     )




