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  :      List[str]            =    logging.get_logger(__name__)
def 		snake_case__	(		lowercase					):
   print("Loading config file..."					)

   def flatten_yaml_as_dict(lowercase							,				lowercase=""							,				lowercase="."					):
      lowerCAmelCase_:							Union[str, Any]       =	[]
      for k, v in d.items():
         lowerCAmelCase_:							Union[str, Any]       =	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					)

   lowerCAmelCase_:							Union[str, Any]       =	argparse.Namespace()
   with open(lowercase							,				"r"					) as yaml_file:
      try:
         lowerCAmelCase_:							List[str]       =	yaml.load(lowercase							,				Loader=yaml.FullLoader					)

         lowerCAmelCase_:							Optional[int]       =	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 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	MobileViTVaConfig()

   lowerCAmelCase_:							Tuple       =	False

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

   # orig_config
   lowerCAmelCase_:							List[Any]       =	load_orig_config_file(lowercase					)
   assert getattr(lowercase							,				"model.classification.name"							,				-1					) == "mobilevit_v2", "Invalid model"
   lowerCAmelCase_:							Any       =	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"
   lowerCAmelCase_:							Dict       =	getattr(lowercase							,				"model.classification.activation.name"							,				"swish"					)
   # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

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

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

   return config
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							List[str]       =	dct.pop(lowercase					)
   lowerCAmelCase_:							Tuple       =	val
def 		snake_case__	(		lowercase							,				lowercase=False					):
   if base_model:
      lowerCAmelCase_:							Dict       =	""
   else:
      lowerCAmelCase_:							Optional[int]       =	"mobilevitv2."

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

      if ".block." in k:
         lowerCAmelCase_:							Any       =	k_new.replace(".block."							,				"."					)
      if ".conv." in k:
         lowerCAmelCase_:							Optional[int]       =	k_new.replace(".conv."							,				".convolution."					)
      if ".norm." in k:
         lowerCAmelCase_:							Union[str, Any]       =	k_new.replace(".norm."							,				".normalization."					)

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

      for i in [3, 4, 5]:
         if F'''layer_{i}.0.''' in k:
            lowerCAmelCase_:							Tuple       =	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_:							List[str]       =	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_:							List[str]       =	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_:							Tuple       =	[0, 1]
         elif i == 4:
            lowerCAmelCase_:							List[Any]       =	[0, 1, 2, 3]
         elif i == 5:
            lowerCAmelCase_:							Optional[Any]       =	[0, 1, 2]

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

      if "classifier.1." in k:
         lowerCAmelCase_:							List[Any]       =	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_:							Optional[Any]       =	k_new.replace(".aspp_layer."							,				"."					)
      if ".aspp_pool." in k:
         lowerCAmelCase_:							Optional[int]       =	k_new.replace(".aspp_pool."							,				"."					)

      rename_keys.append((k, k_new)					)
   return rename_keys
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							List[Any]       =	[]
   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 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	"http://images.cocodataset.org/val2017/000000039769.jpg"
   # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
   lowerCAmelCase_:							str       =	Image.open(requests.get(lowercase							,				stream=lowercase					).raw					)
   return im
@torch.no_grad()
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	get_mobilevitva_config(lowercase							,				lowercase					)

   # load original state_dict
   lowerCAmelCase_:							Dict       =	torch.load(lowercase							,				map_location="cpu"					)

   # load huggingface model
   if task_name.startswith("ade20k_"					) or task_name.startswith("voc_"					):
      lowerCAmelCase_:							Dict       =	MobileViTVaForSemanticSegmentation(lowercase					).eval()
      lowerCAmelCase_:							Any       =	False
   else:
      lowerCAmelCase_:							Union[str, Any]       =	MobileViTVaForImageClassification(lowercase					).eval()
      lowerCAmelCase_:							Optional[Any]       =	False

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

   # verify classification model
   if task_name.startswith("imagenet"					):
      lowerCAmelCase_:							Optional[Any]       =	outputs.logits
      lowerCAmelCase_:							List[str]       =	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_:							Union[str, Any]       =	torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]					)
         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__":
 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  :      int            =    parser.parse_args()
 convert_mobilevitva_checkpoint(
     args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
 )
from collections import OrderedDict
from typing import Mapping

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


a  :      Tuple            =    logging.get_logger(__name__)

a  :      List[str]            =    {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Any          =    'xlm'
    SCREAMING_SNAKE_CASE:				Optional[int]          =    {
        'hidden_size': 'emb_dim',
        'num_attention_heads': 'n_heads',
        'num_hidden_layers': 'n_layers',
        'n_words': 'vocab_size',  # For backward compatibility
    }




    def __init__(    self       ,						lowerCamelCase__=30_145       ,						lowerCamelCase__=2_048       ,						lowerCamelCase__=12       ,						lowerCamelCase__=16       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=512       ,						lowerCamelCase__=2_048**-0.5       ,						lowerCamelCase__=1E-12       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=0       ,						lowerCamelCase__=1       ,						lowerCamelCase__=2       ,						lowerCamelCase__=3       ,						lowerCamelCase__=5       ,						lowerCamelCase__=True       ,						lowerCamelCase__="first"       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=5       ,						lowerCamelCase__=5       ,						lowerCamelCase__=0       ,						lowerCamelCase__=0       ,						lowerCamelCase__=2       ,						lowerCamelCase__=0       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Any       =	vocab_size
       lowerCAmelCase_:							int       =	emb_dim
       lowerCAmelCase_:							int       =	n_layers
       lowerCAmelCase_:							Optional[int]       =	n_heads
       lowerCAmelCase_:							Tuple       =	dropout
       lowerCAmelCase_:							Tuple       =	attention_dropout
       lowerCAmelCase_:							Tuple       =	gelu_activation
       lowerCAmelCase_:							Dict       =	sinusoidal_embeddings
       lowerCAmelCase_:							str       =	causal
       lowerCAmelCase_:							Tuple       =	asm
       lowerCAmelCase_:							Any       =	n_langs
       lowerCAmelCase_:							int       =	use_lang_emb
       lowerCAmelCase_:							Dict       =	layer_norm_eps
       lowerCAmelCase_:							Dict       =	bos_index
       lowerCAmelCase_:							Union[str, Any]       =	eos_index
       lowerCAmelCase_:							Optional[Any]       =	pad_index
       lowerCAmelCase_:							List[Any]       =	unk_index
       lowerCAmelCase_:							Dict       =	mask_index
       lowerCAmelCase_:							Any       =	is_encoder
       lowerCAmelCase_:							Dict       =	max_position_embeddings
       lowerCAmelCase_:							int       =	embed_init_std
       lowerCAmelCase_:							Optional[int]       =	init_std
       lowerCAmelCase_:							str       =	summary_type
       lowerCAmelCase_:							Union[str, Any]       =	summary_use_proj
       lowerCAmelCase_:							Dict       =	summary_activation
       lowerCAmelCase_:							List[Any]       =	summary_proj_to_labels
       lowerCAmelCase_:							List[str]       =	summary_first_dropout
       lowerCAmelCase_:							List[str]       =	start_n_top
       lowerCAmelCase_:							List[Any]       =	end_n_top
       lowerCAmelCase_:							Any       =	mask_token_id
       lowerCAmelCase_:							List[Any]       =	lang_id

       if "n_words" in kwargs:
          lowerCAmelCase_:							Union[str, Any]       =	kwargs["n_words"]

       super().__init__(pad_token_id=lowerCamelCase__       ,						bos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @property
    def      _a    (    self ):
       if self.task == "multiple-choice":
          lowerCAmelCase_:							Optional[Any]       =	{0: "batch", 1: "choice", 2: "sequence"}
       else:
          lowerCAmelCase_:							Any       =	{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ] )