
'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any]):
   if "cls_token" in name:
      UpperCamelCase       =  name.replace('''cls_token''',      '''vit.embeddings.cls_token''')
   if "mask_token" in name:
      UpperCamelCase       =  name.replace('''mask_token''',      '''decoder.mask_token''')
   if "decoder_pos_embed" in name:
      UpperCamelCase       =  name.replace('''decoder_pos_embed''',      '''decoder.decoder_pos_embed''')
   if "pos_embed" in name and "decoder" not in name:
      UpperCamelCase       =  name.replace('''pos_embed''',      '''vit.embeddings.position_embeddings''')
   if "patch_embed.proj" in name:
      UpperCamelCase       =  name.replace('''patch_embed.proj''',      '''vit.embeddings.patch_embeddings.projection''')
   if "patch_embed.norm" in name:
      UpperCamelCase       =  name.replace('''patch_embed.norm''',      '''vit.embeddings.norm''')
   if "decoder_blocks" in name:
      UpperCamelCase       =  name.replace('''decoder_blocks''',      '''decoder.decoder_layers''')
   if "blocks" in name:
      UpperCamelCase       =  name.replace('''blocks''',      '''vit.encoder.layer''')
   if "attn.proj" in name:
      UpperCamelCase       =  name.replace('''attn.proj''',      '''attention.output.dense''')
   if "attn" in name:
      UpperCamelCase       =  name.replace('''attn''',      '''attention.self''')
   if "norm1" in name:
      UpperCamelCase       =  name.replace('''norm1''',      '''layernorm_before''')
   if "norm2" in name:
      UpperCamelCase       =  name.replace('''norm2''',      '''layernorm_after''')
   if "mlp.fc1" in name:
      UpperCamelCase       =  name.replace('''mlp.fc1''',      '''intermediate.dense''')
   if "mlp.fc2" in name:
      UpperCamelCase       =  name.replace('''mlp.fc2''',      '''output.dense''')
   if "decoder_embed" in name:
      UpperCamelCase       =  name.replace('''decoder_embed''',      '''decoder.decoder_embed''')
   if "decoder_norm" in name:
      UpperCamelCase       =  name.replace('''decoder_norm''',      '''decoder.decoder_norm''')
   if "decoder_pred" in name:
      UpperCamelCase       =  name.replace('''decoder_pred''',      '''decoder.decoder_pred''')
   if "norm.weight" in name and "decoder" not in name:
      UpperCamelCase       =  name.replace('''norm.weight''',      '''vit.layernorm.weight''')
   if "norm.bias" in name and "decoder" not in name:
      UpperCamelCase       =  name.replace('''norm.bias''',      '''vit.layernorm.bias''')

   return name



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Optional[Any]):
   for key in orig_state_dict.copy().keys():
      UpperCamelCase       =  orig_state_dict.pop(_UpperCAmelCase)

      if "qkv" in key:
         UpperCamelCase       =  key.split('''.''')
         UpperCamelCase       =  int(key_split[1])
         if "decoder_blocks" in key:
            UpperCamelCase       =  config.decoder_hidden_size
            UpperCamelCase       =  '''decoder.decoder_layers.'''
            if "weight" in key:
               UpperCamelCase       =  val[:dim, :]
               UpperCamelCase       =  val[dim : dim * 2, :]
               UpperCamelCase       =  val[-dim:, :]
            elif "bias" in key:
               UpperCamelCase       =  val[:dim]
               UpperCamelCase       =  val[dim : dim * 2]
               UpperCamelCase       =  val[-dim:]
         else:
            UpperCamelCase       =  config.hidden_size
            UpperCamelCase       =  '''vit.encoder.layer.'''
            if "weight" in key:
               UpperCamelCase       =  val[:dim, :]
               UpperCamelCase       =  val[dim : dim * 2, :]
               UpperCamelCase       =  val[-dim:, :]
            elif "bias" in key:
               UpperCamelCase       =  val[:dim]
               UpperCamelCase       =  val[dim : dim * 2]
               UpperCamelCase       =  val[-dim:]

      else:
         UpperCamelCase       =  val

   return orig_state_dict



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Union[str, Any]):
   UpperCamelCase       =  ViTMAEConfig()
   if "large" in checkpoint_url:
      UpperCamelCase       =  1024
      UpperCamelCase       =  4096
      UpperCamelCase       =  24
      UpperCamelCase       =  16
   elif "huge" in checkpoint_url:
      UpperCamelCase       =  14
      UpperCamelCase       =  1280
      UpperCamelCase       =  5120
      UpperCamelCase       =  32
      UpperCamelCase       =  16

   UpperCamelCase       =  ViTMAEForPreTraining(_UpperCAmelCase)

   UpperCamelCase       =  torch.hub.load_state_dict_from_url(_UpperCAmelCase,      map_location='''cpu''')['''model''']

   UpperCamelCase       =  ViTMAEImageProcessor(size=config.image_size)

   UpperCamelCase       =  convert_state_dict(_UpperCAmelCase,      _UpperCAmelCase)

   model.load_state_dict(_UpperCAmelCase)
   model.eval()

   UpperCamelCase       =  '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

   UpperCamelCase       =  Image.open(requests.get(_UpperCAmelCase,      stream=_UpperCAmelCase).raw)
   UpperCamelCase       =  ViTMAEImageProcessor(size=config.image_size)
   UpperCamelCase       =  image_processor(images=_UpperCAmelCase,      return_tensors='''pt''')

   # forward pass
   torch.manual_seed(2)
   UpperCamelCase       =  model(**_UpperCAmelCase)
   UpperCamelCase       =  outputs.logits

   if "large" in checkpoint_url:
      UpperCamelCase       =  torch.tensor(
          [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]])
   elif "huge" in checkpoint_url:
      UpperCamelCase       =  torch.tensor(
          [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]])
   else:
      UpperCamelCase       =  torch.tensor(
          [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]])

   # verify logits
   assert torch.allclose(logits[0, :3, :3],      _UpperCAmelCase,      atol=1E-4)

   print(f'Saving model 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__":
   snake_case_ :  List[Any]     =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--checkpoint_url',
       default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
       type=str,
       help='URL of the checkpoint you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )

   snake_case_ :  Tuple     =					parser.parse_args()
   convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''


import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_ :  Dict     =					logging.get_logger(__name__)

snake_case_ :  Union[str, Any]     =					{
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							'''encodec'''







   def __init__(			self ,   lowerCamelCase__=[1.5, 3.0, 6.0, 12.0, 24.0] ,   lowerCamelCase__=2_4_0_0_0 ,   lowerCamelCase__=1 ,   lowerCamelCase__=False ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=1_2_8 ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=1 ,   lowerCamelCase__=[8, 5, 4, 2] ,   lowerCamelCase__="weight_norm" ,   lowerCamelCase__=7 ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=2 ,   lowerCamelCase__=True ,   lowerCamelCase__="reflect" ,   lowerCamelCase__=2 ,   lowerCamelCase__=2 ,   lowerCamelCase__=1.0 ,   lowerCamelCase__=1_0_2_4 ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  target_bandwidths
      UpperCamelCase       =  sampling_rate
      UpperCamelCase       =  audio_channels
      UpperCamelCase       =  normalize
      UpperCamelCase       =  chunk_length_s
      UpperCamelCase       =  overlap
      UpperCamelCase       =  hidden_size
      UpperCamelCase       =  num_filters
      UpperCamelCase       =  num_residual_layers
      UpperCamelCase       =  upsampling_ratios
      UpperCamelCase       =  norm_type
      UpperCamelCase       =  kernel_size
      UpperCamelCase       =  last_kernel_size
      UpperCamelCase       =  residual_kernel_size
      UpperCamelCase       =  dilation_growth_rate
      UpperCamelCase       =  use_causal_conv
      UpperCamelCase       =  pad_mode
      UpperCamelCase       =  compress
      UpperCamelCase       =  num_lstm_layers
      UpperCamelCase       =  trim_right_ratio
      UpperCamelCase       =  codebook_size
      UpperCamelCase       =  codebook_dim if codebook_dim is not None else hidden_size
      UpperCamelCase       =  use_conv_shortcut

      if self.norm_type not in ["weight_norm", "time_group_norm"]:
         raise ValueError(
             f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'   )

      super().__init__(**lowerCamelCase__   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if self.chunk_length_s is None:
         return None
      else:
         return int(self.chunk_length_s * self.sampling_rate   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if self.chunk_length_s is None or self.overlap is None:
         return None
      else:
         return max(1 ,   int((1.0 - self.overlap) * self.chunk_length   )   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  np.prod(self.upsampling_ratios   )
      return math.ceil(self.sampling_rate / hop_length   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)   )





