
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 						__magic_name__			(   __a      :	Any						):



     '''simple docstring'''




     print("""Loading config file..."""						)

     def flatten_yaml_as_dict(__a      :	str	,		__a      :	List[str]=""	,		__a      :	Union[str, Any]="."						):
          UpperCamelCase__				=    []
          for k, v in d.items():
               UpperCamelCase__				=    parent_key + sep + k if parent_key else k
               if isinstance(__a	,		collections.abc.MutableMapping						):
                    items.extend(flatten_yaml_as_dict(__a	,		__a	,		sep=__a						).items()						)
               else:
                    items.append((new_key, v)						)
          return dict(__a						)

     UpperCamelCase__				=    argparse.Namespace()
     with open(__a	,		"""r"""						) as yaml_file:
          try:
               UpperCamelCase__				=    yaml.load(__a	,		Loader=yaml.FullLoader						)

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






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	Optional[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(__a						)
     assert getattr(__a	,		"""model.classification.name"""	,		-1						) == "mobilevit_v2", "Invalid model"
     UpperCamelCase__				=    getattr(__a	,		"""model.classification.mitv2.width_multiplier"""	,		1.0						)
     assert (
         getattr(__a	,		"""model.classification.mitv2.attn_norm_layer"""	,		-1						) == "layer_norm_2d"
     ), "Norm layers other than layer_norm_2d is not supported"
     UpperCamelCase__				=    getattr(__a	,		"""model.classification.activation.name"""	,		"""swish"""						)
     # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

     return config






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	Union[str, Any]	,		__a      :	List[str]						):



     '''simple docstring'''




     UpperCamelCase__				=    dct.pop(__a						)
     UpperCamelCase__				=    val






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Optional[int]=False						):



     '''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 						__magic_name__			(   __a      :	str						):



     '''simple docstring'''




     UpperCamelCase__				=    []
     for k in state_dict.keys():
          if k.startswith("""seg_head.aux_head."""						):
               keys_to_ignore.append(__a						)
     for k in keys_to_ignore:
          state_dict.pop(__a	,		__a						)






def 						__magic_name__			(   ):



     '''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(__a	,		stream=__a						).raw						)
     return im






@torch.no_grad()
def 						__magic_name__			(   __a      :	Union[str, Any]	,		__a      :	int	,		__a      :	str	,		__a      :	str						):



     '''simple docstring'''




     UpperCamelCase__				=    get_mobilevitva_config(__a	,		__a						)

     # load original state_dict
     UpperCamelCase__				=    torch.load(__a	,		map_location="""cpu"""						)

     # load huggingface model
     if task_name.startswith("""ade20k_"""						) or task_name.startswith("""voc_"""						):
          UpperCamelCase__				=    MobileViTVaForSemanticSegmentation(__a						).eval()
          UpperCamelCase__				=    False
     else:
          UpperCamelCase__				=    MobileViTVaForImageClassification(__a						).eval()
          UpperCamelCase__				=    False

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

     # load modified state_dict
     model.load_state_dict(__a						)

     # 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(**__a						)

     # 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([-1.6_3_3_6E0_0, -7.3_2_0_4E-0_2, -5.1_8_8_3E-0_1]						)
               assert torch.allclose(logits[0, :3]	,		__a	,		atol=1E-4						)

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


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
    )







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    R"""\w+[.]\d+"""
     UpperCamelCase__				=    re.findall(__a	,		__a						)
     for pat in pats:
          UpperCamelCase__				=    key.replace(__a	,		"""_""".join(pat.split("""."""						)						)						)
     return key






def 						__magic_name__			(   __a      :	str	,		__a      :	Dict	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
     if (
         any("""norm""" in str_ for str_ in pt_tuple_key						)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""embedding""",)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCamelCase__				=    pt_tensor.transpose(2	,		3	,		1	,		0						)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight":
          UpperCamelCase__				=    pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""weight""",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""bias""",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	List[Any]	,		__a      :	Optional[int]=42						):



     '''simple docstring'''




     UpperCamelCase__				=    {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCamelCase__				=    flax_model.init_weights(PRNGKey(__a						)						)

     UpperCamelCase__				=    flatten_dict(__a						)
     UpperCamelCase__				=    {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCamelCase__				=    rename_key(__a						)
          UpperCamelCase__				=    tuple(renamed_pt_key.split("""."""						)						)

          # Correctly rename weight parameters
          UpperCamelCase__		,	UpperCamelCase__				=    rename_key_and_reshape_tensor(__a	,		__a	,		__a						)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."						)

        # also add unexpected weight so that warning is thrown
          UpperCamelCase__				=    jnp.asarray(__a						)

     return unflatten_dict(__a						)





