





"""simple docstring"""





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__A											=						logging.get_logger(__name__)






class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:Union[str, Any]						    =   ["""input_features"""]






       def __init__(       self       ,					__UpperCAmelCase=8_0       ,					__UpperCAmelCase=1_6_0_0_0       ,					__UpperCAmelCase=1_6_0       ,					__UpperCAmelCase=3_0       ,					__UpperCAmelCase=4_0_0       ,					__UpperCAmelCase=0.0       ,					__UpperCAmelCase=False       ,					**__UpperCAmelCase       ,					):



         '''simple docstring'''

         super().__init__(
             feature_size=__UpperCAmelCase       ,					sampling_rate=__UpperCAmelCase       ,					padding_value=__UpperCAmelCase       ,					return_attention_mask=__UpperCAmelCase       ,					**__UpperCAmelCase       ,					)
         lowerCAmelCase__     :Any											=				n_fft
         lowerCAmelCase__     :List[Any]											=				hop_length
         lowerCAmelCase__     :Tuple											=				chunk_length
         lowerCAmelCase__     :Tuple											=				chunk_length * sampling_rate
         lowerCAmelCase__     :Tuple											=				self.n_samples // hop_length
         lowerCAmelCase__     :List[Any]											=				sampling_rate
         lowerCAmelCase__     :Optional[int]											=				mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2       ,					num_mel_filters=__UpperCAmelCase       ,					min_frequency=0.0       ,					max_frequency=80_00.0       ,					sampling_rate=__UpperCAmelCase       ,					norm='slaney'       ,					mel_scale='slaney'       ,					)






       def 	snake_case		(       self       ,					__UpperCAmelCase ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				spectrogram(
             __UpperCAmelCase       ,					window_function(self.n_fft       ,					'hann' )       ,					frame_length=self.n_fft       ,					hop_length=self.hop_length       ,					power=2.0       ,					mel_filters=self.mel_filters       ,					log_mel='log10'       ,					)
         lowerCAmelCase__     :Dict											=				log_spec[:, :-1]
         lowerCAmelCase__     :List[Any]											=				np.maximum(__UpperCAmelCase       ,					log_spec.max() - 8.0 )
         lowerCAmelCase__     :Optional[Any]											=				(log_spec + 4.0) / 4.0
         return log_spec






       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def 	snake_case		(       __UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = 0.0 ):



         '''simple docstring'''

         if attention_mask is not None:
           lowerCAmelCase__     :Tuple											=				np.array(__UpperCAmelCase       ,					np.intaa )
           lowerCAmelCase__     :Any											=				[]

           for vector, length in zip(__UpperCAmelCase       ,					attention_mask.sum(-1 ) ):
             lowerCAmelCase__     :Any											=				(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
             if length < normed_slice.shape[0]:
               lowerCAmelCase__     :List[Any]											=				padding_value

             normed_input_values.append(__UpperCAmelCase )
         else:
           lowerCAmelCase__     :Optional[int]											=				[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

         return normed_input_values






       def __call__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "max_length"       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					**__UpperCAmelCase       ,					):



         '''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.__class__.__name__} was trained using a"
                 F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                 F" was sampled with {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.' )

         lowerCAmelCase__     :Optional[int]											=				isinstance(__UpperCAmelCase       ,					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}" )
         lowerCAmelCase__     :Optional[int]											=				is_batched_numpy or (
             isinstance(__UpperCAmelCase       ,					(list, tuple) ) and (isinstance(raw_speech[0]       ,					(np.ndarray, tuple, list) ))
         )

         if is_batched:
           lowerCAmelCase__     :Dict											=				[np.asarray([speech]       ,					dtype=np.floataa ).T for speech in raw_speech]
         elif not is_batched and not isinstance(__UpperCAmelCase       ,					np.ndarray ):
           lowerCAmelCase__     :Union[str, Any]											=				np.asarray(__UpperCAmelCase       ,					dtype=np.floataa )
         elif isinstance(__UpperCAmelCase       ,					np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
           lowerCAmelCase__     :List[str]											=				raw_speech.astype(np.floataa )

         # always return batch
         if not is_batched:
           lowerCAmelCase__     :Any											=				[np.asarray([raw_speech] ).T]

         lowerCAmelCase__     :List[str]											=				BatchFeature({'input_features': raw_speech} )

         # convert into correct format for padding

         lowerCAmelCase__     :str											=				self.pad(
             __UpperCAmelCase       ,					padding=__UpperCAmelCase       ,					max_length=max_length if max_length else self.n_samples       ,					truncation=__UpperCAmelCase       ,					pad_to_multiple_of=__UpperCAmelCase       ,					return_attention_mask=return_attention_mask or do_normalize       ,					)

         # zero-mean and unit-variance normalization
         if do_normalize:
           lowerCAmelCase__     :Union[str, Any]											=				self.zero_mean_unit_var_norm(
               padded_inputs['input_features']       ,					attention_mask=padded_inputs['attention_mask']       ,					padding_value=self.padding_value       ,					)
           lowerCAmelCase__     :Tuple											=				np.stack(padded_inputs['input_features']       ,					axis=0 )

         # make sure list is in array format
         lowerCAmelCase__     :Tuple											=				padded_inputs.get('input_features' ).transpose(2       ,					0       ,					1 )

         lowerCAmelCase__     :Optional[Any]											=				[self._np_extract_fbank_features(__UpperCAmelCase ) for waveform in input_features[0]]

         if isinstance(input_features[0]       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Any											=				[np.asarray(__UpperCAmelCase       ,					dtype=np.floataa ) for feature in input_features]
         else:
           lowerCAmelCase__     :Union[str, Any]											=				input_features

         if return_attention_mask:
           # rescale from sample (48000) to feature (3000)
           lowerCAmelCase__     :Union[str, Any]											=				padded_inputs['attention_mask'][:, :: self.hop_length]

         if return_tensors is not None:
           lowerCAmelCase__     :List[Any]											=				padded_inputs.convert_to_tensors(__UpperCAmelCase )

         return padded_inputs






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				copy.deepcopy(self.__dict__ )
         lowerCAmelCase__     :List[Any]											=				self.__class__.__name__
         if "mel_filters" in output:
           del output["mel_filters"]
         return output







"""simple docstring"""





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Union[str, Any]											=				384
  if "tiny" in model_name:
    lowerCAmelCase__     :List[Any]											=				[3, 3, 9, 3]
    lowerCAmelCase__     :Tuple											=				[96, 192, 384, 768]
  if "small" in model_name:
    lowerCAmelCase__     :Union[str, Any]											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[96, 192, 384, 768]
  if "base" in model_name:
    lowerCAmelCase__     :Dict											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[128, 256, 512, 1024]
    lowerCAmelCase__     :Union[str, Any]											=				512
  if "large" in model_name:
    lowerCAmelCase__     :int											=				[3, 3, 27, 3]
    lowerCAmelCase__     :Any											=				[192, 384, 768, 1536]
    lowerCAmelCase__     :Optional[Any]											=				768
  if "xlarge" in model_name:
    lowerCAmelCase__     :Optional[Any]											=				[3, 3, 27, 3]
    lowerCAmelCase__     :str											=				[256, 512, 1024, 2048]
    lowerCAmelCase__     :Union[str, Any]											=				1024

  # set label information
  lowerCAmelCase__     :Tuple											=				150
  lowerCAmelCase__     :List[Any]											=				'huggingface/label-files'
  lowerCAmelCase__     :Tuple											=				'ade20k-id2label.json'
  lowerCAmelCase__     :Tuple											=				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  repo_type='dataset'     )	,  'r'     )     )
  lowerCAmelCase__     :Dict											=				{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
  lowerCAmelCase__     :int											=				{v: k for k, v in idalabel.items()}

  lowerCAmelCase__     :List[str]											=				ConvNextConfig(
      depths=_SCREAMING_SNAKE_CASE	,  hidden_sizes=_SCREAMING_SNAKE_CASE	,  out_features=['stage1', 'stage2', 'stage3', 'stage4']     )
  lowerCAmelCase__     :Union[str, Any]											=				UperNetConfig(
      backbone_config=_SCREAMING_SNAKE_CASE	,  auxiliary_in_channels=_SCREAMING_SNAKE_CASE	,  num_labels=_SCREAMING_SNAKE_CASE	,  idalabel=_SCREAMING_SNAKE_CASE	,  labelaid=_SCREAMING_SNAKE_CASE	,  )

  return config
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :str											=				[]

  # fmt: off
  # stem
  rename_keys.append(('backbone.downsample_layers.0.0.weight', 'backbone.embeddings.patch_embeddings.weight')     )
  rename_keys.append(('backbone.downsample_layers.0.0.bias', 'backbone.embeddings.patch_embeddings.bias')     )
  rename_keys.append(('backbone.downsample_layers.0.1.weight', 'backbone.embeddings.layernorm.weight')     )
  rename_keys.append(('backbone.downsample_layers.0.1.bias', 'backbone.embeddings.layernorm.bias')     )
  # stages
  for i in range(len(config.backbone_config.depths     )     ):
    for j in range(config.backbone_config.depths[i]     ):
      rename_keys.append((F"backbone.stages.{i}.{j}.gamma", F"backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.depthwise_conv.weight", F"backbone.encoder.stages.{i}.layers.{j}.dwconv.weight")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.depthwise_conv.bias", F"backbone.encoder.stages.{i}.layers.{j}.dwconv.bias")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.norm.weight", F"backbone.encoder.stages.{i}.layers.{j}.layernorm.weight")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.norm.bias", F"backbone.encoder.stages.{i}.layers.{j}.layernorm.bias")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv1.weight", F"backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv1.bias", F"backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv2.weight", F"backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight")     )
      rename_keys.append((F"backbone.stages.{i}.{j}.pointwise_conv2.bias", F"backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias")     )
    if i > 0:
      rename_keys.append((F"backbone.downsample_layers.{i}.0.weight", F"backbone.encoder.stages.{i}.downsampling_layer.0.weight")     )
      rename_keys.append((F"backbone.downsample_layers.{i}.0.bias", F"backbone.encoder.stages.{i}.downsampling_layer.0.bias")     )
      rename_keys.append((F"backbone.downsample_layers.{i}.1.weight", F"backbone.encoder.stages.{i}.downsampling_layer.1.weight")     )
      rename_keys.append((F"backbone.downsample_layers.{i}.1.bias", F"backbone.encoder.stages.{i}.downsampling_layer.1.bias")     )

    rename_keys.append((F"backbone.norm{i}.weight", F"backbone.hidden_states_norms.stage{i+1}.weight")     )
    rename_keys.append((F"backbone.norm{i}.bias", F"backbone.hidden_states_norms.stage{i+1}.bias")     )

  # decode head
  rename_keys.extend(
      [
          ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
          ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
          ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
          ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
      ]     )
  # fmt: on

  return rename_keys
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				dct.pop(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Tuple											=				val
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->int:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				{
      'upernet-convnext-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth',
      'upernet-convnext-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth',
      'upernet-convnext-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth',
      'upernet-convnext-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth',
      'upernet-convnext-xlarge': 'https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth',
  }
  lowerCAmelCase__     :List[Any]											=				model_name_to_url[model_name]
  lowerCAmelCase__     :Optional[Any]											=				torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )['state_dict']

  lowerCAmelCase__     :List[Any]											=				get_upernet_config(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				UperNetForSemanticSegmentation(_SCREAMING_SNAKE_CASE     )
  model.eval()

  # replace "bn" => "batch_norm"
  for key in state_dict.copy().keys():
    lowerCAmelCase__     :Optional[int]											=				state_dict.pop(_SCREAMING_SNAKE_CASE     )
    if "bn" in key:
      lowerCAmelCase__     :Any											=				key.replace('bn'	,  'batch_norm'     )
    lowerCAmelCase__     :int											=				val

  # rename keys
  lowerCAmelCase__     :Optional[Any]											=				create_rename_keys(_SCREAMING_SNAKE_CASE     )
  for src, dest in rename_keys:
    rename_key(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  model.load_state_dict(_SCREAMING_SNAKE_CASE     )

  # verify on image
  lowerCAmelCase__     :str											=				'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
  lowerCAmelCase__     :Optional[Any]											=				Image.open(requests.get(_SCREAMING_SNAKE_CASE	,  stream=_SCREAMING_SNAKE_CASE     ).raw     ).convert('RGB'     )

  lowerCAmelCase__     :Tuple											=				SegformerImageProcessor()
  lowerCAmelCase__     :List[Any]											=				processor(_SCREAMING_SNAKE_CASE	,  return_tensors='pt'     ).pixel_values

  with torch.no_grad():
    lowerCAmelCase__     :Optional[Any]											=				model(_SCREAMING_SNAKE_CASE     )

  if model_name == "upernet-convnext-tiny":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]]     )
  elif model_name == "upernet-convnext-small":
    lowerCAmelCase__     :Union[str, Any]											=				torch.tensor(
        [[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]]     )
  elif model_name == "upernet-convnext-base":
    lowerCAmelCase__     :Dict											=				torch.tensor(
        [[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]]     )
  elif model_name == "upernet-convnext-large":
    lowerCAmelCase__     :List[str]											=				torch.tensor(
        [[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]]     )
  elif model_name == "upernet-convnext-xlarge":
    lowerCAmelCase__     :Optional[Any]											=				torch.tensor(
        [[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]]     )
  print('Logits:'	,  outputs.logits[0, 0, :3, :3]     )
  assert torch.allclose(outputs.logits[0, 0, :3, :3]	,  _SCREAMING_SNAKE_CASE	,  atol=1e-4     )
  print('Looks ok!'     )

  if pytorch_dump_folder_path is not None:
    print(F"Saving model {model_name} to {pytorch_dump_folder_path}"     )
    model.save_pretrained(_SCREAMING_SNAKE_CASE     )
    print(F"Saving processor to {pytorch_dump_folder_path}"     )
    processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

  if push_to_hub:
    print(F"Pushing model and processor for {model_name} to hub"     )
    model.push_to_hub(F"openmmlab/{model_name}"     )
    processor.push_to_hub(F"openmmlab/{model_name}"     )


if __name__ == "__main__":
  __A											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""upernet-convnext-tiny""",
      type=str,
      choices=[F'''upernet-convnext-{size}''' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
      help="""Name of the ConvNext UperNet model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  __A											=						parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
