





import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


SCREAMING_SNAKE_CASE_						   =       datasets.utils.logging.get_logger(__name__)

@dataclass
class 		UpperCamelCase__ (      datasets.BuilderConfig   ):



     '''simple docstring'''

     __snake_case    :	int			=			10000
     __snake_case    :	Optional[List[str]]			=			None
     __snake_case    :	Optional[datasets.Features]			=			None

class 		UpperCamelCase__ (      datasets.ArrowBasedBuilder   ):



     '''simple docstring'''

     __snake_case    :	List[Any]			=			ParquetConfig






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Tuple:




           '''simple docstring'''


           return datasets.DatasetInfo(features=self.config.features       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple						,lowerCamelCase__						:    Any       )     ->		int:




           '''simple docstring'''


           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}"""       )
           SCREAMING_SNAKE_CASE		=						dl_manager.download_and_extract(self.config.data_files       )
           if isinstance(lowerCamelCase__						,(str, list, tuple)       ):
                 SCREAMING_SNAKE_CASE		=						data_files
                 if isinstance(lowerCamelCase__						,lowerCamelCase__       ):
                       SCREAMING_SNAKE_CASE		=						[files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 SCREAMING_SNAKE_CASE		=						[dl_manager.iter_files(lowerCamelCase__       ) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,gen_kwargs={"""files""": files}       )]
           SCREAMING_SNAKE_CASE		=						[]
           for split_name, files in data_files.items():
                 if isinstance(lowerCamelCase__						,lowerCamelCase__       ):
                       SCREAMING_SNAKE_CASE		=						[files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 SCREAMING_SNAKE_CASE		=						[dl_manager.iter_files(lowerCamelCase__       ) for file in files]
                 # Infer features is they are stoed in the arrow schema
                 if self.info.features is None:
                       for file in itertools.chain.from_iterable(lowerCamelCase__       ):
                             with open(lowerCamelCase__						,"""rb"""       ) as f:
                                   SCREAMING_SNAKE_CASE		=						datasets.Features.from_arrow_schema(pq.read_schema(lowerCamelCase__       )       )
                             break
                 splits.append(datasets.SplitGenerator(name=lowerCamelCase__						,gen_kwargs={"""files""": files}       )       )
           return splits






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]						,lowerCamelCase__						:    pa.Table       )     ->		pa.Table:




           '''simple docstring'''


           if self.info.features is not None:
                 # more expensive cast to support nested features with keys in a different order
                 # allows str <-> int/float or str to Audio for example
                 SCREAMING_SNAKE_CASE		=						table_cast(lowerCamelCase__						,self.info.features.arrow_schema       )
           return pa_table






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any						,lowerCamelCase__						:    List[str]       )     ->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.info.features.arrow_schema if self.info.features is not None else None
           if self.info.features is not None and self.config.columns is not None:
                 if sorted(field.name for field in schema       ) != sorted(self.config.columns       ):
                       raise ValueError(
                           F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""       )
           for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase__       )       ):
                 with open(lowerCamelCase__						,"""rb"""       ) as f:
                       SCREAMING_SNAKE_CASE		=						pq.ParquetFile(lowerCamelCase__       )
                       try:
                             for batch_idx, record_batch in enumerate(
                                 parquet_file.iter_batches(batch_size=self.config.batch_size						,columns=self.config.columns       )       ):
                                   SCREAMING_SNAKE_CASE		=						pa.Table.from_batches([record_batch]       )
                                   # 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 F"""{file_idx}_{batch_idx}""", self._cast_table(lowerCamelCase__       )
                       except ValueError as e:
                             logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase__       )}: {e}"""       )
                             raise









import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						SwinConfig()
      SCREAMING_SNAKE_CASE		=						swin_name.split("""_"""	)

      SCREAMING_SNAKE_CASE		=						name_split[1]
      SCREAMING_SNAKE_CASE		=						int(name_split[4]	)
      SCREAMING_SNAKE_CASE		=						int(name_split[3][-1]	)

      if model_size == "tiny":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 6, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "small":
            SCREAMING_SNAKE_CASE		=						96
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(3, 6, 12, 24)
      elif model_size == "base":
            SCREAMING_SNAKE_CASE		=						1_28
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(4, 8, 16, 32)
      else:
            SCREAMING_SNAKE_CASE		=						1_92
            SCREAMING_SNAKE_CASE		=						(2, 2, 18, 2)
            SCREAMING_SNAKE_CASE		=						(6, 12, 24, 48)

      if "in22k" in swin_name:
            SCREAMING_SNAKE_CASE		=						2_18_41
      else:
            SCREAMING_SNAKE_CASE		=						10_00
            SCREAMING_SNAKE_CASE		=						"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE		=						"""imagenet-1k-id2label.json"""
            SCREAMING_SNAKE_CASE		=						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						repo_type="""dataset"""	) ,						"""r"""	)	)
            SCREAMING_SNAKE_CASE		=						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE		=						idalabel
            SCREAMING_SNAKE_CASE		=						{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE		=						img_size
      SCREAMING_SNAKE_CASE		=						num_classes
      SCREAMING_SNAKE_CASE		=						embed_dim
      SCREAMING_SNAKE_CASE		=						depths
      SCREAMING_SNAKE_CASE		=						num_heads
      SCREAMING_SNAKE_CASE		=						window_size

      return config



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Dict:


      '''simple docstring'''


      if "patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.proj""" ,						"""embeddings.patch_embeddings.projection"""	)
      if "patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""patch_embed.norm""" ,						"""embeddings.norm"""	)
      if "layers" in name:
            SCREAMING_SNAKE_CASE		=						"""encoder.""" + name
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""attn.proj""" ,						"""attention.output.dense"""	)
      if "attn" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""attn""" ,						"""attention.self"""	)
      if "norm1" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""norm1""" ,						"""layernorm_before"""	)
      if "norm2" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""norm2""" ,						"""layernorm_after"""	)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""mlp.fc1""" ,						"""intermediate.dense"""	)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""mlp.fc2""" ,						"""output.dense"""	)

      if name == "norm.weight":
            SCREAMING_SNAKE_CASE		=						"""layernorm.weight"""
      if name == "norm.bias":
            SCREAMING_SNAKE_CASE		=						"""layernorm.bias"""

      if "head" in name:
            SCREAMING_SNAKE_CASE		=						name.replace("""head""" ,						"""classifier"""	)
      else:
            SCREAMING_SNAKE_CASE		=						"""swin.""" + name

      return name



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Any:


      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE		=						orig_state_dict.pop(_SCREAMING_SNAKE_CASE	)

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

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

      return orig_state_dict



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						timm.create_model(_SCREAMING_SNAKE_CASE ,						pretrained=_SCREAMING_SNAKE_CASE	)
      timm_model.eval()

      SCREAMING_SNAKE_CASE		=						get_swin_config(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						SwinForImageClassification(_SCREAMING_SNAKE_CASE	)
      model.eval()

      SCREAMING_SNAKE_CASE		=						convert_state_dict(timm_model.state_dict() ,						_SCREAMING_SNAKE_CASE	)
      model.load_state_dict(_SCREAMING_SNAKE_CASE	)

      SCREAMING_SNAKE_CASE		=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

      SCREAMING_SNAKE_CASE		=						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,						"""-"""	)	)	)
      SCREAMING_SNAKE_CASE		=						Image.open(requests.get(_SCREAMING_SNAKE_CASE ,						stream=_SCREAMING_SNAKE_CASE	).raw	)
      SCREAMING_SNAKE_CASE		=						image_processor(images=_SCREAMING_SNAKE_CASE ,						return_tensors="""pt"""	)

      SCREAMING_SNAKE_CASE		=						timm_model(inputs["""pixel_values"""]	)
      SCREAMING_SNAKE_CASE		=						model(**_SCREAMING_SNAKE_CASE	).logits

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

      print(F"""Saving model {swin_name} 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__":
      SCREAMING_SNAKE_CASE_						   =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--swin_name""",
          default="""swin_tiny_patch4_window7_224""",
          type=str,
          help="""Name of the Swin 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."""
      )

      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


