






"""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()
lowercase_				     =     logging.get_logger(__name__)



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







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

  def flatten_yaml_as_dict(__UpperCamelCase		,       __UpperCamelCase=""		,       __UpperCamelCase="." ):
    __A									= []
    for k, v in d.items():
      __A									= 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 )

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

      __A									= 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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= MobileViTVaConfig()

  __A									= False

  # dataset
  if task_name.startswith('''imagenet1k_''' ):
    __A									= 1_0_0_0
    if int(task_name.strip().split('''_''' )[-1] ) == 3_8_4:
      __A									= 3_8_4
    else:
      __A									= 2_5_6
    __A									= '''imagenet-1k-id2label.json'''
  elif task_name.startswith('''imagenet21k_to_1k_''' ):
    __A									= 2_1_0_0_0
    if int(task_name.strip().split('''_''' )[-1] ) == 3_8_4:
      __A									= 3_8_4
    else:
      __A									= 2_5_6
    __A									= '''imagenet-22k-id2label.json'''
  elif task_name.startswith('''ade20k_''' ):
    __A									= 1_5_1
    __A									= 5_1_2
    __A									= '''ade20k-id2label.json'''
    __A									= True
  elif task_name.startswith('''voc_''' ):
    __A									= 2_1
    __A									= 5_1_2
    __A									= '''pascal-voc-id2label.json'''
    __A									= True

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

  if is_segmentation_model:
    __A									= getattr(__UpperCamelCase		,       '''model.segmentation.output_stride'''		,       1_6 )
    if "_deeplabv3" in task_name:
      __A									= getattr(__UpperCamelCase		,       '''model.segmentation.deeplabv3.aspp_rates'''		,       [1_2, 2_4, 3_6] )
      __A									= getattr(__UpperCamelCase		,       '''model.segmentation.deeplabv3.aspp_out_channels'''		,       5_1_2 )
      __A									= getattr(__UpperCamelCase		,       '''model.segmentation.deeplabv3.aspp_dropout'''		,       0.1 )

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

  return config



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= dct.pop(__UpperCamelCase )
  __A									= val



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase=False ):




  """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 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  __A									= []
  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 					lowerCAmelCase				(    ):




  """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(__UpperCamelCase		,       stream=__UpperCamelCase ).raw )
  return im



@torch.no_grad()
def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= get_mobilevitva_config(__UpperCamelCase		,       __UpperCamelCase )

  # load original state_dict
  __A									= torch.load(__UpperCamelCase		,       map_location='''cpu''' )

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

  # remove and rename some keys of load the original model
  __A									= checkpoint
  remove_unused_keys(__UpperCamelCase )
  __A									= 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
  __A									= MobileViTImageProcessor(crop_size=config.image_size		,       size=config.image_size + 3_2 )
  __A									= image_processor(images=prepare_img()		,       return_tensors='''pt''' )
  __A									= model(**__UpperCamelCase )

  # 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]		,       __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__":
       lowercase_				     =     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.'
       )

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












"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase_				     =     logging.get_logger(__name__)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	int						 = ["input_features", "attention_mask"]


   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=80,       _lowerCamelCase :     int=1_60_00,       _lowerCamelCase :     Any=80,       _lowerCamelCase :     List[str]=0.0,       _lowerCamelCase :     int=True,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Optional[int]=True,       **_lowerCamelCase :     List[str],       ):

     '''simple docstring'''
     super().__init__(feature_size=_lowerCamelCase,       sampling_rate=_lowerCamelCase,       padding_value=_lowerCamelCase,       **_lowerCamelCase  )
     __A									= num_mel_bins
     __A									= do_ceptral_normalize
     __A									= normalize_means
     __A									= normalize_vars
     __A									= True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     np.ndarray,       ):

     '''simple docstring'''
     __A									= waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
     __A									= torch.from_numpy(_lowerCamelCase  ).unsqueeze(0  )
     __A									= ta_kaldi.fbank(_lowerCamelCase,       num_mel_bins=self.num_mel_bins,       sample_frequency=self.sampling_rate  )
     return features.numpy()


   @staticmethod
   def 				_SCREAMING_SNAKE_CASE				(	_lowerCamelCase :     np.ndarray,       _lowerCamelCase :     int,       _lowerCamelCase :     Optional[bool] = True,       _lowerCamelCase :     Optional[bool] = True,       _lowerCamelCase :     float = 0.0,       ):

     '''simple docstring'''
     # make sure we normalize float32 arrays
     if normalize_means:
       __A									= x[:input_length].mean(axis=0  )
       __A									= np.subtract(_lowerCamelCase,       _lowerCamelCase  )
     if normalize_vars:
       __A									= x[:input_length].std(axis=0  )
       __A									= np.divide(_lowerCamelCase,       _lowerCamelCase  )

     if input_length < x.shape[0]:
       __A									= padding_value

     # make sure array is in float32
     __A									= x.astype(np.floataa  )

     return x


   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     List[np.ndarray],       _lowerCamelCase :     Optional[np.ndarray] = None  ):

     '''simple docstring'''
     __A									= attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [
         self.utterance_cmvn(_lowerCamelCase,       _lowerCamelCase,       self.normalize_means,       self.normalize_vars,       self.padding_value  )
         for x, n in zip(_lowerCamelCase,       _lowerCamelCase  )
     ]






   def __call__(	self :     Optional[Any],       _lowerCamelCase :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],       _lowerCamelCase :     Union[bool, str, PaddingStrategy] = False,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     bool = False,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     Optional[Union[str, TensorType]] = None,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     Optional[bool] = None,       **_lowerCamelCase :     Optional[Any],       ):

     '''simple docstring'''

     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
             f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
             f' {self.sampling_rate} and not {sampling_rate}.'  )
     else:
       logger.warning(
           '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
           '''Failing to do so can result in silent errors that might be hard to debug.'''  )

     __A									= isinstance(_lowerCamelCase,       np.ndarray  ) and len(raw_speech.shape  ) > 1
     if is_batched_numpy and len(raw_speech.shape  ) > 2:
       raise ValueError(f'Only mono-channel audio is supported for input to {self}'  )
     __A									= is_batched_numpy or (
         isinstance(_lowerCamelCase,       (list, tuple)  ) and (isinstance(raw_speech[0],       (np.ndarray, tuple, list)  ))
     )

     if is_batched:
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.floataa  ) for speech in raw_speech]
     elif not is_batched and not isinstance(_lowerCamelCase,       np.ndarray  ):
       __A									= np.asarray(_lowerCamelCase,       dtype=np.floataa  )
     elif isinstance(_lowerCamelCase,       np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
       __A									= raw_speech.astype(np.floataa  )

     # always return batch
     if not is_batched:
       __A									= [raw_speech]

     # extract fbank features
     __A									= [self._extract_fbank_features(_lowerCamelCase  ) for waveform in raw_speech]

     # convert into correct format for padding
     __A									= BatchFeature({'''input_features''': features}  )

     __A									= self.pad(
         _lowerCamelCase,       padding=_lowerCamelCase,       max_length=_lowerCamelCase,       truncation=_lowerCamelCase,       pad_to_multiple_of=_lowerCamelCase,       return_attention_mask=_lowerCamelCase,       **_lowerCamelCase,       )

     # make sure list is in array format
     __A									= padded_inputs.get('''input_features'''  )
     if isinstance(input_features[0],       _lowerCamelCase  ):
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.floataa  ) for feature in input_features]

     __A									= padded_inputs.get('''attention_mask'''  )
     if attention_mask is not None:
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.intaa  ) for array in attention_mask]

     # Utterance-level cepstral mean and variance normalization
     if self.do_ceptral_normalize:
       __A									= (
           np.array(_lowerCamelCase,       dtype=np.intaa  )
           if self._get_padding_strategies(_lowerCamelCase,       max_length=_lowerCamelCase  ) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       __A									= self.normalize(
           padded_inputs['''input_features'''],       attention_mask=_lowerCamelCase  )

     if return_tensors is not None:
       __A									= padded_inputs.convert_to_tensors(_lowerCamelCase  )

     return padded_inputs




