

import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=False				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    []
     for i in range(config.num_hidden_layers				):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'''module.blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')				)
          rename_keys.append((f'''module.blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')				)
          rename_keys.append(
              (f'''module.blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')				)
          rename_keys.append((f'''module.blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')				)
          rename_keys.append((f'''module.blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')				)
          rename_keys.append((f'''module.blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')				)
          rename_keys.append((f'''module.blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')				)
          rename_keys.append((f'''module.blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')				)
          rename_keys.append((f'''module.blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')				)
          rename_keys.append((f'''module.blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')				)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ("module.cls_token", "vit.embeddings.cls_token"),
             ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
             ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
             ("module.pos_embed", "vit.embeddings.position_embeddings"),
         ]				)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ("module.norm.weight", "layernorm.weight"),
                  ("module.norm.bias", "layernorm.bias"),
              ]				)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          _lowerCAmelCase   :					Optional[int]        =    [(pair[0], pair[1][4:]) if pair[1].startswith("vit"				) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("norm.weight", "vit.layernorm.weight"),
                  ("norm.bias", "vit.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]				)

     return rename_keys







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=False				)       ->      Dict:

     """simple docstring"""







     for i in range(config.num_hidden_layers				):
          if base_model:
               _lowerCAmelCase   :					Any        =    ""
          else:
               _lowerCAmelCase   :					Union[str, Any]        =    "vit."
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          _lowerCAmelCase   :					List[str]        =    state_dict.pop(f'''module.blocks.{i}.attn.qkv.weight'''				)
          _lowerCAmelCase   :					Optional[Any]        =    state_dict.pop(f'''module.blocks.{i}.attn.qkv.bias'''				)
          # next, add query, keys and values (in that order) to the state dict
          _lowerCAmelCase   :					List[str]        =    in_proj_weight[
              : config.hidden_size, :
          ]
          _lowerCAmelCase   :					Dict        =    in_proj_bias[: config.hidden_size]
          _lowerCAmelCase   :					Union[str, Any]        =    in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _lowerCAmelCase   :					Union[str, Any]        =    in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          _lowerCAmelCase   :					Optional[int]        =    in_proj_weight[
              -config.hidden_size :, :
          ]
          _lowerCAmelCase   :					Tuple        =    in_proj_bias[-config.hidden_size :]







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    ["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Any:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    [
         "module.fc.fc1.weight",
         "module.fc.fc1.bias",
         "module.fc.bn1.weight",
         "module.fc.bn1.bias",
         "module.fc.bn1.running_mean",
         "module.fc.bn1.running_var",
         "module.fc.bn1.num_batches_tracked",
         "module.fc.fc2.weight",
         "module.fc.fc2.bias",
         "module.fc.bn2.weight",
         "module.fc.bn2.bias",
         "module.fc.bn2.running_mean",
         "module.fc.bn2.running_var",
         "module.fc.bn2.num_batches_tracked",
         "module.fc.fc3.weight",
         "module.fc.fc3.bias",
     ]
     for k in ignore_keys:
          state_dict.pop(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    dct.pop(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[Any]        =    val







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    ViTMSNConfig()
     _lowerCAmelCase   :					Dict        =    1000

     _lowerCAmelCase   :					List[str]        =    "datasets/huggingface/label-files"
     _lowerCAmelCase   :					Dict        =    "imagenet-1k-id2label.json"
     _lowerCAmelCase   :					Dict        =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)		,							"r"				)				)
     _lowerCAmelCase   :					str        =    {int(SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
     _lowerCAmelCase   :					int        =    idalabel
     _lowerCAmelCase   :					Tuple        =    {v: k for k, v in idalabel.items()}

     if "s16" in checkpoint_url:
          _lowerCAmelCase   :					Tuple        =    384
          _lowerCAmelCase   :					Any        =    1536
          _lowerCAmelCase   :					Tuple        =    6
     elif "l16" in checkpoint_url:
          _lowerCAmelCase   :					Optional[int]        =    1024
          _lowerCAmelCase   :					Any        =    4096
          _lowerCAmelCase   :					Union[str, Any]        =    24
          _lowerCAmelCase   :					List[Any]        =    16
          _lowerCAmelCase   :					str        =    0.1
     elif "b4" in checkpoint_url:
          _lowerCAmelCase   :					List[str]        =    4
     elif "l7" in checkpoint_url:
          _lowerCAmelCase   :					Dict        =    7
          _lowerCAmelCase   :					Optional[Any]        =    1024
          _lowerCAmelCase   :					Dict        =    4096
          _lowerCAmelCase   :					Optional[int]        =    24
          _lowerCAmelCase   :					int        =    16
          _lowerCAmelCase   :					int        =    0.1

     _lowerCAmelCase   :					Tuple        =    ViTMSNModel(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Optional[int]        =    torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE		,							map_location="cpu"				)["target_encoder"]

     _lowerCAmelCase   :					Any        =    ViTImageProcessor(size=config.image_size				)

     remove_projection_head(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					List[str]        =    create_rename_keys(SCREAMING_SNAKE_CASE		,							base_model=SCREAMING_SNAKE_CASE				)

     for src, dest in rename_keys:
          rename_key(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     read_in_q_k_v(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							base_model=SCREAMING_SNAKE_CASE				)

     model.load_state_dict(SCREAMING_SNAKE_CASE				)
     model.eval()

     _lowerCAmelCase   :					List[str]        =    "http://images.cocodataset.org/val2017/000000039769.jpg"

     _lowerCAmelCase   :					List[str]        =    Image.open(requests.get(SCREAMING_SNAKE_CASE		,							stream=SCREAMING_SNAKE_CASE				).raw				)
     _lowerCAmelCase   :					Union[str, Any]        =    ViTImageProcessor(
         size=config.image_size		,							image_mean=SCREAMING_SNAKE_CASE		,							image_std=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    image_processor(images=SCREAMING_SNAKE_CASE		,							return_tensors="pt"				)

     # forward pass
     torch.manual_seed(2				)
     _lowerCAmelCase   :					List[Any]        =    model(**SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					int        =    outputs.last_hidden_state

     # The following Colab Notebook was used to generate these outputs:
     # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
     if "s16" in checkpoint_url:
          _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]				)
     elif "b16" in checkpoint_url:
          _lowerCAmelCase   :					Dict        =    torch.tensor([[14.2_889, -18.9_045, 11.7_281]]				)
     elif "l16" in checkpoint_url:
          _lowerCAmelCase   :					Union[str, Any]        =    torch.tensor([[41.5_028, -22.8_681, 45.6_475]]				)
     elif "b4" in checkpoint_url:
          _lowerCAmelCase   :					Dict        =    torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]				)
     else:
          _lowerCAmelCase   :					Dict        =    torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]				)

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

     print(f'''Saving model to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(SCREAMING_SNAKE_CASE				)

     print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
      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.'
  )

  __UpperCAmelCase    				=     parser.parse_args()
  convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[Any]     ,						A_ :	float       ):






      '''simple docstring'''




      return 0.0







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[int | float, int | float]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     _lowerCAmelCase   :					Optional[Any]        =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Optional[Any]        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Dict        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Dict        =    np.abs(np.fft.fft(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    20 * np.logaa(SCREAMING_SNAKE_CASE				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     # Display within reasonable bounds
     _lowerCAmelCase   :					Tuple        =    get_bounds(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     plt.ylim(max([-80, bounds[0]]				)		,							min([80, bounds[1]]				)				)
     plt.ylabel("Gain (dB)"				)

     plt.plot(SCREAMING_SNAKE_CASE				)
     plt.show()







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Dict        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Tuple        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Tuple        =    np.angle(np.fft.fft(SCREAMING_SNAKE_CASE				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     plt.ylim(-2 * pi		,							2 * pi				)
     plt.ylabel("Phase shift (Radians)"				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE		,							-2 * pi				)				)
     plt.show()






