





'''simple docstring'''







import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification

def    __UpperCamelCase	(	a    : List[str]     ) ->Tuple:
 snake_case				=			SwinvaConfig()
 snake_case				=			swinva_name.split('''_'''     )

 snake_case				=			name_split[1]
 if "to" in name_split[3]:
  snake_case				=			int(name_split[3][-3:]     )
 else:
  snake_case				=			int(name_split[3]     )
 if "to" in name_split[2]:
  snake_case				=			int(name_split[2][-2:]     )
 else:
  snake_case				=			int(name_split[2][6:]     )

 if model_size == "tiny":
  snake_case				=			96
  snake_case				=			(2, 2, 6, 2)
  snake_case				=			(3, 6, 12, 24)
 elif model_size == "small":
  snake_case				=			96
  snake_case				=			(2, 2, 18, 2)
  snake_case				=			(3, 6, 12, 24)
 elif model_size == "base":
  snake_case				=			128
  snake_case				=			(2, 2, 18, 2)
  snake_case				=			(4, 8, 16, 32)
 else:
  snake_case				=			192
  snake_case				=			(2, 2, 18, 2)
  snake_case				=			(6, 12, 24, 48)

 if "to" in swinva_name:
  snake_case				=			(12, 12, 12, 6)

 if ("22k" in swinva_name) and ("to" not in swinva_name):
  snake_case				=			2_1841
  snake_case				=			'''huggingface/label-files'''
  snake_case				=			'''imagenet-22k-id2label.json'''
  snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
  snake_case				=			{int(a     ): v for k, v in idalabel.items()}
  snake_case				=			idalabel
  snake_case				=			{v: k for k, v in idalabel.items()}

 else:
  snake_case				=			1000
  snake_case				=			'''huggingface/label-files'''
  snake_case				=			'''imagenet-1k-id2label.json'''
  snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
  snake_case				=			{int(a     ): v for k, v in idalabel.items()}
  snake_case				=			idalabel
  snake_case				=			{v: k for k, v in idalabel.items()}

 snake_case				=			img_size
 snake_case				=			num_classes
 snake_case				=			embed_dim
 snake_case				=			depths
 snake_case				=			num_heads
 snake_case				=			window_size

 return config

def    __UpperCamelCase	(	a    : Dict     ) ->int:
 if "patch_embed.proj" in name:
  snake_case				=			name.replace('''patch_embed.proj'''					,   '''embeddings.patch_embeddings.projection'''     )
 if "patch_embed.norm" in name:
  snake_case				=			name.replace('''patch_embed.norm'''					,   '''embeddings.norm'''     )
 if "layers" in name:
  snake_case				=			'''encoder.''' + name
 if "attn.proj" in name:
  snake_case				=			name.replace('''attn.proj'''					,   '''attention.output.dense'''     )
 if "attn" in name:
  snake_case				=			name.replace('''attn'''					,   '''attention.self'''     )
 if "norm1" in name:
  snake_case				=			name.replace('''norm1'''					,   '''layernorm_before'''     )
 if "norm2" in name:
  snake_case				=			name.replace('''norm2'''					,   '''layernorm_after'''     )
 if "mlp.fc1" in name:
  snake_case				=			name.replace('''mlp.fc1'''					,   '''intermediate.dense'''     )
 if "mlp.fc2" in name:
  snake_case				=			name.replace('''mlp.fc2'''					,   '''output.dense'''     )
 if "q_bias" in name:
  snake_case				=			name.replace('''q_bias'''					,   '''query.bias'''     )
 if "k_bias" in name:
  snake_case				=			name.replace('''k_bias'''					,   '''key.bias'''     )
 if "v_bias" in name:
  snake_case				=			name.replace('''v_bias'''					,   '''value.bias'''     )
 if "cpb_mlp" in name:
  snake_case				=			name.replace('''cpb_mlp'''					,   '''continuous_position_bias_mlp'''     )
 if name == "norm.weight":
  snake_case				=			'''layernorm.weight'''
 if name == "norm.bias":
  snake_case				=			'''layernorm.bias'''

 if "head" in name:
  snake_case				=			name.replace('''head'''					,   '''classifier'''     )
 else:
  snake_case				=			'''swinv2.''' + name

 return name

def    __UpperCamelCase	(	a    : Any					,   a    : Union[str, Any]     ) ->Dict:
 for key in orig_state_dict.copy().keys():
  snake_case				=			orig_state_dict.pop(a     )

  if "mask" in key:
   continue
  elif "qkv" in key:
   snake_case				=			key.split('''.'''     )
   snake_case				=			int(key_split[1]     )
   snake_case				=			int(key_split[3]     )
   snake_case				=			model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

   if "weight" in key:
    snake_case				=			val[:dim, :]
    snake_case				=			val[dim : dim * 2, :]
    snake_case				=			val[-dim:, :]
   else:
    snake_case				=			val[:dim]
    snake_case				=			val[
        dim : dim * 2
    ]
    snake_case				=			val[-dim:]
  else:
   snake_case				=			val

 return orig_state_dict

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : Union[str, Any]     ) ->str:
 snake_case				=			timm.create_model(a					,   pretrained=a     )
 timm_model.eval()

 snake_case				=			get_swinva_config(a     )
 snake_case				=			SwinvaForImageClassification(a     )
 model.eval()

 snake_case				=			convert_state_dict(timm_model.state_dict()					,   a     )
 model.load_state_dict(a     )

 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''

 snake_case				=			AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''					,   '''-'''     )     )     )
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     )

 snake_case				=			timm_model(inputs['''pixel_values''']     )
 snake_case				=			model(**a     ).logits

 assert torch.allclose(a					,   a					,   atol=1e-3     )

 print(f"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""     )
 model.save_pretrained(a     )

 print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
 image_processor.save_pretrained(a     )

 model.push_to_hub(
     repo_path_or_name=Path(a					,   a     )					,   organization='''nandwalritik'''					,   commit_message='''Add model'''					,   )


if __name__ == "__main__":
 _lowercase       						=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--swinv2_name',
     default='swinv2_tiny_patch4_window8_256',
     type=str,
     help='Name of the Swinv2 timm 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.'
 )

 _lowercase       						=			parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








'''simple docstring'''







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
   from .features import FeatureType


_lowercase						,				_lowercase						,				_lowercase       						=			False, False, False
@dataclass
class 			_lowercase						:
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					None
     # Automatically constructed
     _UpperCAmelCase										=					"dict"
     _UpperCAmelCase										=					pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}					)
     _UpperCAmelCase										=					field(default='''Audio'''				,		init=__a				,		repr=__a					)







     def __call__( self    ) ->	Optional[Any]:
      return self.pa_type







     def     UpperCamelCase			( self    ,   A__    ) ->	dict:
      try:
       import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
      except ImportError as err:
       raise ImportError('''To support encoding audio data, please install \'soundfile\'.'''    ) from err
      if isinstance(A__    ,   A__    ):
       return {"bytes": None, "path": value}
      elif isinstance(A__    ,   A__    ):
       return {"bytes": value, "path": None}
      elif "array" in value:
       # convert the audio array to wav bytes
       snake_case				=			BytesIO()
       sf.write(A__    ,   value['''array''']    ,   value['''sampling_rate''']    ,   format='''wav'''    )
       return {"bytes": buffer.getvalue(), "path": None}
      elif value.get('''path'''    ) is not None and os.path.isfile(value['''path''']    ):
       # we set "bytes": None to not duplicate the data if they're already available locally
       if value["path"].endswith('''pcm'''    ):
        # "PCM" only has raw audio bytes
        if value.get('''sampling_rate'''    ) is None:
         # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
         raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object'''    )
        if value.get('''bytes'''    ):
         # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
         snake_case				=			np.frombuffer(value['''bytes''']    ,   dtype=np.intaa    ).astype(np.floataa    ) / 3_27_67
        else:
         snake_case				=			np.memmap(value['''path''']    ,   dtype='''h'''    ,   mode='''r'''    ).astype(np.floataa    ) / 3_27_67

        snake_case				=			BytesIO(bytes()    )
        sf.write(A__    ,   A__    ,   value['''sampling_rate''']    ,   format='''wav'''    )
        return {"bytes": buffer.getvalue(), "path": None}
       else:
        return {"bytes": None, "path": value.get('''path'''    )}
      elif value.get('''bytes'''    ) is not None or value.get('''path'''    ) is not None:
       # store the audio bytes, and path is used to infer the audio format using the file extension
       return {"bytes": value.get('''bytes'''    ), "path": value.get('''path'''    )}
      else:
       raise ValueError(
           F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	dict:
      if not self.decode:
       raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.'''    )

      snake_case				,		snake_case				=			(value['''path'''], BytesIO(value['''bytes''']    )) if value['''bytes'''] is not None else (value['''path'''], None)
      if path is None and file is None:
       raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""    )

      try:
       import librosa
       import soundfile as sf
      except ImportError as err:
       raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.'''    ) from err

      snake_case				=			xsplitext(A__    )[1][1:].lower() if path is not None else None
      if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
       raise RuntimeError(
           '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''    )
      elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
       raise RuntimeError(
           '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''    )

      if file is None:
       snake_case				=			token_per_repo_id or {}
       snake_case				=			path.split('''::'''    )[-1]
       try:
        snake_case				=			string_to_dict(A__    ,   config.HUB_DATASETS_URL    )['''repo_id''']
        snake_case				=			token_per_repo_id[repo_id]
       except (ValueError, KeyError):
        snake_case				=			None

       with xopen(A__    ,   '''rb'''    ,   use_auth_token=A__    ) as f:
        snake_case				,		snake_case				=			sf.read(A__    )

      else:
       snake_case				,		snake_case				=			sf.read(A__    )

      snake_case				=			array.T
      if self.mono:
       snake_case				=			librosa.to_mono(A__    )
      if self.sampling_rate and self.sampling_rate != sampling_rate:
       snake_case				=			librosa.resample(A__    ,   orig_sr=A__    ,   target_sr=self.sampling_rate    )
       snake_case				=			self.sampling_rate

      return {"path": path, "array": array, "sampling_rate": sampling_rate}







     def     UpperCamelCase			( self    ) ->	Union["FeatureType", Dict[str, "FeatureType"]]:
      from .features import Value

      if self.decode:
       raise ValueError('''Cannot flatten a decoded Audio feature.'''    )
      return {
          "bytes": Value('''binary'''    ),
          "path": Value('''string'''    ),
      }







     def     UpperCamelCase			( self    ,   A__    ) ->	pa.StructArray:
      if pa.types.is_string(storage.type    ):
       snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.binary()    )
       snake_case				=			pa.StructArray.from_arrays([bytes_array, storage]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      elif pa.types.is_binary(storage.type    ):
       snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.string()    )
       snake_case				=			pa.StructArray.from_arrays([storage, path_array]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      elif pa.types.is_struct(storage.type    ) and storage.type.get_all_field_indices('''array'''    ):
       snake_case				=			pa.array([Audio().encode_example(A__    ) if x is not None else None for x in storage.to_pylist()]    )
      elif pa.types.is_struct(storage.type    ):
       if storage.type.get_field_index('''bytes'''    ) >= 0:
        snake_case				=			storage.field('''bytes'''    )
       else:
        snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.binary()    )
       if storage.type.get_field_index('''path'''    ) >= 0:
        snake_case				=			storage.field('''path'''    )
       else:
        snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.string()    )
       snake_case				=			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      return array_cast(A__    ,   self.pa_type    )







     def     UpperCamelCase			( self    ,   A__    ) ->	pa.StructArray:

      @no_op_if_value_is_null
      def path_to_bytes(A__    ):
       with xopen(A__    ,   '''rb'''    ) as f:
        snake_case				=			f.read()
       return bytes_

      snake_case				=			pa.array(
          [
              (path_to_bytes(x['''path''']    ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
              for x in storage.to_pylist()
          ]    ,   type=pa.binary()    ,   )
      snake_case				=			pa.array(
          [os.path.basename(A__    ) if path is not None else None for path in storage.field('''path'''    ).to_pylist()]    ,   type=pa.string()    ,   )
      snake_case				=			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ['''bytes''', '''path''']    ,   mask=bytes_array.is_null()    )
      return array_cast(A__    ,   self.pa_type    )

