


'''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      _a     (						_lowercase :     List[str]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Dict  							=      SwinvaConfig()
     __UpperCAmelCase     :			Tuple  							=      swinva_name.split('''_'''  )

     __UpperCAmelCase     :			Optional[Any]  							=      name_split[1]
     if "to" in name_split[3]:
          __UpperCAmelCase     :			List[str]  							=      int(name_split[3][-3:]  )
     else:
          __UpperCAmelCase     :			str  							=      int(name_split[3]  )
     if "to" in name_split[2]:
          __UpperCAmelCase     :			Dict  							=      int(name_split[2][-2:]  )
     else:
          __UpperCAmelCase     :			Any  							=      int(name_split[2][6:]  )

     if model_size == "tiny":
          __UpperCAmelCase     :			Optional[int]  							=      96
          __UpperCAmelCase     :			Tuple  							=      (2, 2, 6, 2)
          __UpperCAmelCase     :			Optional[Any]  							=      (3, 6, 12, 24)
     elif model_size == "small":
          __UpperCAmelCase     :			int  							=      96
          __UpperCAmelCase     :			List[str]  							=      (2, 2, 18, 2)
          __UpperCAmelCase     :			Optional[Any]  							=      (3, 6, 12, 24)
     elif model_size == "base":
          __UpperCAmelCase     :			Optional[int]  							=      128
          __UpperCAmelCase     :			str  							=      (2, 2, 18, 2)
          __UpperCAmelCase     :			Any  							=      (4, 8, 16, 32)
     else:
          __UpperCAmelCase     :			Tuple  							=      192
          __UpperCAmelCase     :			Union[str, Any]  							=      (2, 2, 18, 2)
          __UpperCAmelCase     :			int  							=      (6, 12, 24, 48)

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

     if ("22k" in swinva_name) and ("to" not in swinva_name):
          __UpperCAmelCase     :			Optional[Any]  							=      21841
          __UpperCAmelCase     :			str  							=      '''huggingface/label-files'''
          __UpperCAmelCase     :			Tuple  							=      '''imagenet-22k-id2label.json'''
          __UpperCAmelCase     :			Optional[int]  							=      json.load(open(hf_hub_download(_lowercase      , _lowercase      , repo_type='''dataset'''  )      , '''r'''  )  )
          __UpperCAmelCase     :			Optional[Any]  							=      {int(_lowercase  ): v for k, v in idalabel.items()}
          __UpperCAmelCase     :			Union[str, Any]  							=      idalabel
          __UpperCAmelCase     :			Dict  							=      {v: k for k, v in idalabel.items()}

     else:
          __UpperCAmelCase     :			Optional[Any]  							=      1000
          __UpperCAmelCase     :			str  							=      '''huggingface/label-files'''
          __UpperCAmelCase     :			str  							=      '''imagenet-1k-id2label.json'''
          __UpperCAmelCase     :			Union[str, Any]  							=      json.load(open(hf_hub_download(_lowercase      , _lowercase      , repo_type='''dataset'''  )      , '''r'''  )  )
          __UpperCAmelCase     :			Optional[Any]  							=      {int(_lowercase  ): v for k, v in idalabel.items()}
          __UpperCAmelCase     :			Optional[Any]  							=      idalabel
          __UpperCAmelCase     :			Optional[int]  							=      {v: k for k, v in idalabel.items()}

     __UpperCAmelCase     :			Optional[Any]  							=      img_size
     __UpperCAmelCase     :			str  							=      num_classes
     __UpperCAmelCase     :			List[Any]  							=      embed_dim
     __UpperCAmelCase     :			Optional[Any]  							=      depths
     __UpperCAmelCase     :			Any  							=      num_heads
     __UpperCAmelCase     :			Union[str, Any]  							=      window_size

     return config




def      _a     (						_lowercase :     List[str]  ):





     '''simple docstring'''






     if "patch_embed.proj" in name:
          __UpperCAmelCase     :			List[str]  							=      name.replace('''patch_embed.proj'''      , '''embeddings.patch_embeddings.projection'''  )
     if "patch_embed.norm" in name:
          __UpperCAmelCase     :			int  							=      name.replace('''patch_embed.norm'''      , '''embeddings.norm'''  )
     if "layers" in name:
          __UpperCAmelCase     :			Any  							=      '''encoder.''' + name
     if "attn.proj" in name:
          __UpperCAmelCase     :			Optional[int]  							=      name.replace('''attn.proj'''      , '''attention.output.dense'''  )
     if "attn" in name:
          __UpperCAmelCase     :			int  							=      name.replace('''attn'''      , '''attention.self'''  )
     if "norm1" in name:
          __UpperCAmelCase     :			Optional[Any]  							=      name.replace('''norm1'''      , '''layernorm_before'''  )
     if "norm2" in name:
          __UpperCAmelCase     :			Optional[Any]  							=      name.replace('''norm2'''      , '''layernorm_after'''  )
     if "mlp.fc1" in name:
          __UpperCAmelCase     :			List[Any]  							=      name.replace('''mlp.fc1'''      , '''intermediate.dense'''  )
     if "mlp.fc2" in name:
          __UpperCAmelCase     :			Dict  							=      name.replace('''mlp.fc2'''      , '''output.dense'''  )
     if "q_bias" in name:
          __UpperCAmelCase     :			List[str]  							=      name.replace('''q_bias'''      , '''query.bias'''  )
     if "k_bias" in name:
          __UpperCAmelCase     :			Optional[Any]  							=      name.replace('''k_bias'''      , '''key.bias'''  )
     if "v_bias" in name:
          __UpperCAmelCase     :			List[str]  							=      name.replace('''v_bias'''      , '''value.bias'''  )
     if "cpb_mlp" in name:
          __UpperCAmelCase     :			List[Any]  							=      name.replace('''cpb_mlp'''      , '''continuous_position_bias_mlp'''  )
     if name == "norm.weight":
          __UpperCAmelCase     :			Optional[Any]  							=      '''layernorm.weight'''
     if name == "norm.bias":
          __UpperCAmelCase     :			Optional[Any]  							=      '''layernorm.bias'''

     if "head" in name:
          __UpperCAmelCase     :			Tuple  							=      name.replace('''head'''      , '''classifier'''  )
     else:
          __UpperCAmelCase     :			Optional[Any]  							=      '''swinv2.''' + name

     return name




def      _a     (						_lowercase :     Dict      , _lowercase :     Optional[Any]  ):





     '''simple docstring'''






     for key in orig_state_dict.copy().keys():
          __UpperCAmelCase     :			Union[str, Any]  							=      orig_state_dict.pop(_lowercase  )

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

               if "weight" in key:
                    __UpperCAmelCase     :			int  							=      val[:dim, :]
                    __UpperCAmelCase     :			Any  							=      val[dim : dim * 2, :]
                    __UpperCAmelCase     :			Any  							=      val[-dim:, :]
               else:
                    __UpperCAmelCase     :			Optional[Any]  							=      val[:dim]
                    __UpperCAmelCase     :			Dict  							=      val[
                        dim : dim * 2
                    ]
                    __UpperCAmelCase     :			List[Any]  							=      val[-dim:]
          else:
               __UpperCAmelCase     :			List[str]  							=      val

     return orig_state_dict




def      _a     (						_lowercase :     Dict      , _lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Any  							=      timm.create_model(_lowercase      , pretrained=_lowercase  )
     timm_model.eval()

     __UpperCAmelCase     :			int  							=      get_swinva_config(_lowercase  )
     __UpperCAmelCase     :			Any  							=      SwinvaForImageClassification(_lowercase  )
     model.eval()

     __UpperCAmelCase     :			Tuple  							=      convert_state_dict(timm_model.state_dict()      , _lowercase  )
     model.load_state_dict(_lowercase  )

     __UpperCAmelCase     :			Union[str, Any]  							=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     __UpperCAmelCase     :			List[str]  							=      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''      , '''-'''  )  )  )
     __UpperCAmelCase     :			Dict  							=      Image.open(requests.get(_lowercase      , stream=_lowercase  ).raw  )
     __UpperCAmelCase     :			Any  							=      image_processor(images=_lowercase      , return_tensors='''pt'''  )

     __UpperCAmelCase     :			List[Any]  							=      timm_model(inputs['''pixel_values''']  )
     __UpperCAmelCase     :			str  							=      model(**_lowercase  ).logits

     assert torch.allclose(_lowercase      , _lowercase      , atol=1E-3  )

     print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'  )
     model.save_pretrained(_lowercase  )

     print(F'Saving image processor to {pytorch_dump_folder_path}'  )
     image_processor.save_pretrained(_lowercase  )

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


if __name__ == "__main__":
      __UpperCAmelCase      :List[Any]			    =       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."
      )

      __UpperCAmelCase      :Dict			    =       parser.parse_args()
      convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)



'''simple docstring'''



import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__UpperCAmelCase      :int			    =       datasets.utils.logging.get_logger(__name__)



@dataclass
class      a    (		datasets.BuilderConfig	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[datasets.Features]							       =       None
     SCREAMING_SNAKE_CASE	:			str							       =       "utf-8"
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       None
     SCREAMING_SNAKE_CASE	:			Optional[str]							       =       None
     SCREAMING_SNAKE_CASE	:			bool							       =       True  # deprecated
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       None  # deprecated
     SCREAMING_SNAKE_CASE	:			int							       =       1_0 << 2_0  # 10MB
     SCREAMING_SNAKE_CASE	:			Optional[bool]							       =       None



class      a    (		datasets.ArrowBasedBuilder	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Tuple							       =       JsonConfig

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Dict:
          if self.config.block_size is not None:
               logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''      )
               __UpperCAmelCase     :			Tuple  							=      self.config.block_size
          if self.config.use_threads is not True:
               logger.warning(
                   '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''      )
          if self.config.newlines_in_values is not None:
               raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''      )
          return datasets.DatasetInfo(features=self.config.features      )

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		Tuple      )						->							str:
          if not self.config.data_files:
               raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}'      )
          __UpperCAmelCase     :			Optional[int]  							=      dl_manager.download_and_extract(self.config.data_files      )
          if isinstance(snake_case							,			(str, list, tuple)      ):
               __UpperCAmelCase     :			Dict  							=      data_files
               if isinstance(snake_case							,			snake_case      ):
                    __UpperCAmelCase     :			List[Any]  							=      [files]
               __UpperCAmelCase     :			Tuple  							=      [dl_manager.iter_files(snake_case      ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,			gen_kwargs={'''files''': files}      )]
          __UpperCAmelCase     :			Tuple  							=      []
          for split_name, files in data_files.items():
               if isinstance(snake_case							,			snake_case      ):
                    __UpperCAmelCase     :			Any  							=      [files]
               __UpperCAmelCase     :			Optional[int]  							=      [dl_manager.iter_files(snake_case      ) for file in files]
               splits.append(datasets.SplitGenerator(name=snake_case							,			gen_kwargs={'''files''': files}      )      )
          return splits

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		pa.Table      )						->							pa.Table:
          if self.config.features is not None:
               # adding missing columns
               for column_name in set(self.config.features      ) - set(pa_table.column_names      ):
                    __UpperCAmelCase     :			Any  							=      self.config.features.arrow_schema.field(snake_case      ).type
                    __UpperCAmelCase     :			Dict  							=      pa_table.append_column(snake_case							,			pa.array([None] * len(snake_case      )							,			type=snake_case      )      )
               # more expensive cast to support nested structures with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __UpperCAmelCase     :			Tuple  							=      table_cast(snake_case							,			self.config.features.arrow_schema      )
          return pa_table






     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		Any      )						->							Optional[int]:
          for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case      )      ):
               # If the file is one json object and if we need to look at the list of items in one specific field
               if self.config.field is not None:
                    with open(snake_case							,			encoding=self.config.encoding							,			errors=self.config.encoding_errors      ) as f:
                         __UpperCAmelCase     :			Optional[int]  							=      json.load(snake_case      )

                    # We keep only the field we are interested in
                    __UpperCAmelCase     :			int  							=      dataset[self.config.field]

                    # We accept two format: a list of dicts or a dict of lists
                    if isinstance(snake_case							,			(list, tuple)      ):
                         __UpperCAmelCase     :			Optional[Any]  							=      set().union(*[row.keys() for row in dataset]      )
                         __UpperCAmelCase     :			Union[str, Any]  							=      {col: [row.get(snake_case      ) for row in dataset] for col in keys}
                    else:
                         __UpperCAmelCase     :			Optional[int]  							=      dataset
                    __UpperCAmelCase     :			Tuple  							=      pa.Table.from_pydict(snake_case      )
                    yield file_idx, self._cast_table(snake_case      )

               # If the file has one json object per line
               else:
                    with open(snake_case							,			'''rb'''      ) as f:
                         __UpperCAmelCase     :			Optional[Any]  							=      0
                         # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                         # Set a default minimum value of 16kB if the chunk size is really small
                         __UpperCAmelCase     :			int  							=      max(self.config.chunksize // 32							,			16 << 10      )
                         __UpperCAmelCase     :			Any  							=      (
                             self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                         )
                         while True:
                              __UpperCAmelCase     :			List[Any]  							=      f.read(self.config.chunksize      )
                              if not batch:
                                   break
                              # Finish current line
                              try:
                                   batch += f.readline()
                              except (AttributeError, io.UnsupportedOperation):
                                   batch += readline(snake_case      )
                              # PyArrow only accepts utf-8 encoded bytes
                              if self.config.encoding != "utf-8":
                                   __UpperCAmelCase     :			Union[str, Any]  							=      batch.decode(self.config.encoding							,			errors=snake_case      ).encode('''utf-8'''      )
                              try:
                                   while True:
                                        try:
                                             __UpperCAmelCase     :			List[str]  							=      paj.read_json(
                                                 io.BytesIO(snake_case      )							,			read_options=paj.ReadOptions(block_size=snake_case      )      )
                                             break
                                        except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                             if (
                                                 isinstance(snake_case							,			pa.ArrowInvalid      )
                                                 and "straddling" not in str(snake_case      )
                                                 or block_size > len(snake_case      )
                                             ):
                                                  raise
                                             else:
                                                  # Increase the block size in case it was too small.
                                                  # The block size will be reset for the next file.
                                                  logger.debug(
                                                      f'Batch of {len(snake_case      )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'      )
                                                  block_size *= 2
                              except pa.ArrowInvalid as e:
                                   try:
                                        with open(
                                            snake_case							,			encoding=self.config.encoding							,			errors=self.config.encoding_errors      ) as f:
                                             __UpperCAmelCase     :			Optional[Any]  							=      json.load(snake_case      )
                                   except json.JSONDecodeError:
                                        logger.error(f'Failed to read file \'{file}\' with error {type(snake_case      )}: {e}'      )
                                        raise e
                                   # If possible, parse the file as a list of json objects and exit the loop
                                   if isinstance(snake_case							,			snake_case      ):  # list is the only sequence type supported in JSON
                                        try:
                                             __UpperCAmelCase     :			Dict  							=      set().union(*[row.keys() for row in dataset]      )
                                             __UpperCAmelCase     :			Optional[Any]  							=      {col: [row.get(snake_case      ) for row in dataset] for col in keys}
                                             __UpperCAmelCase     :			Union[str, Any]  							=      pa.Table.from_pydict(snake_case      )
                                        except (pa.ArrowInvalid, AttributeError) as e:
                                             logger.error(f'Failed to read file \'{file}\' with error {type(snake_case      )}: {e}'      )
                                             raise ValueError(f'Not able to read records in the JSON file at {file}.'      ) from None
                                        yield file_idx, self._cast_table(snake_case      )
                                        break
                                   else:
                                        logger.error(f'Failed to read file \'{file}\' with error {type(snake_case      )}: {e}'      )
                                        raise ValueError(
                                            f'Not able to read records in the JSON file at {file}. '
                                            f'You should probably indicate the field of the JSON file containing your records. '
                                            f'This JSON file contain the following fields: {str(list(dataset.keys()      )      )}. '
                                            f'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '      ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                              yield (file_idx, batch_idx), self._cast_table(snake_case      )
                              batch_idx += 1