



'''simple docstring'''





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


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







@dataclass
class 		snake_case						(	datasets.BuilderConfig  ):



   '''simple docstring'''






   snake_case_					: int          =		1_00_00
   snake_case_					: Optional[List[str]]          =		None
   snake_case_					: Optional[datasets.Features]          =		None







class 		snake_case						(	datasets.ArrowBasedBuilder  ):



   '''simple docstring'''






   snake_case_					: Optional[int]          =		ParquetConfig

   def      UpperCamelCase_  (					self							:			Optional[Any])				->				str:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			Dict)				->				Any:

    """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}''')
    _snake_case    :  Tuple					=					dl_manager.download_and_extract(self.config.data_files)
    if isinstance(lowerCAmelCase				,		(str, list, tuple)):
     _snake_case    :  Optional[Any]					=					data_files
     if isinstance(lowerCAmelCase				,		lowerCAmelCase):
      _snake_case    :  List[str]					=					[files]
     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
     _snake_case    :  List[Any]					=					[dl_manager.iter_files(lowerCAmelCase) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,		gen_kwargs={"""files""": files})]
    _snake_case    :  List[str]					=					[]
    for split_name, files in data_files.items():
     if isinstance(lowerCAmelCase				,		lowerCAmelCase):
      _snake_case    :  Optional[Any]					=					[files]
     # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
     _snake_case    :  int					=					[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:
        _snake_case    :  Optional[Any]					=					datasets.Features.from_arrow_schema(pq.read_schema(lowerCAmelCase))
       break
     splits.append(datasets.SplitGenerator(name=lowerCAmelCase				,		gen_kwargs={"""files""": files}))
    return splits

   def      UpperCamelCase_  (					self							:			Any				,		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
     _snake_case    :  Tuple					=					table_cast(lowerCAmelCase				,		self.info.features.arrow_schema)
    return pa_table


   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			Union[str, Any])				->				Dict:

    """simple docstring"""







    _snake_case    :  List[str]					=					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:
      _snake_case    :  str					=					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)):
        _snake_case    :  int					=					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 requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				)  ->     Optional[Any]:
 # vision encoder
 if "img_encoder.pos_embed" in name:
  _snake_case    :  Any					=					name.replace("""img_encoder.pos_embed"""				,       """vision_model.embeddings.position_embeddings"""				)
 if "img_encoder.patch_embed.proj" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""img_encoder.patch_embed.proj"""				,       """vision_model.embeddings.patch_embeddings.projection"""				)
 if "img_encoder.patch_embed.norm" in name:
  _snake_case    :  Tuple					=					name.replace("""img_encoder.patch_embed.norm"""				,       """vision_model.embeddings.layernorm"""				)
 if "img_encoder.layers" in name:
  _snake_case    :  Optional[Any]					=					name.replace("""img_encoder.layers"""				,       """vision_model.encoder.stages"""				)
 if "blocks" in name and "res" not in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""blocks"""				,       """layers"""				)
 if "attn" in name and "pre_assign" not in name:
  _snake_case    :  Any					=					name.replace("""attn"""				,       """self_attn"""				)
 if "proj" in name and "self_attn" in name and "text" not in name:
  _snake_case    :  int					=					name.replace("""proj"""				,       """out_proj"""				)
 if "pre_assign_attn.attn.proj" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""pre_assign_attn.attn.proj"""				,       """pre_assign_attn.attn.out_proj"""				)
 if "norm1" in name:
  _snake_case    :  Any					=					name.replace("""norm1"""				,       """layer_norm1"""				)
 if "norm2" in name and "pre_assign" not in name:
  _snake_case    :  Dict					=					name.replace("""norm2"""				,       """layer_norm2"""				)
 if "img_encoder.norm" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""img_encoder.norm"""				,       """vision_model.layernorm"""				)
 # text encoder
 if "text_encoder.token_embedding" in name:
  _snake_case    :  Dict					=					name.replace("""text_encoder.token_embedding"""				,       """text_model.embeddings.token_embedding"""				)
 if "text_encoder.positional_embedding" in name:
  _snake_case    :  str					=					name.replace("""text_encoder.positional_embedding"""				,       """text_model.embeddings.position_embedding.weight"""				)
 if "text_encoder.transformer.resblocks." in name:
  _snake_case    :  int					=					name.replace("""text_encoder.transformer.resblocks."""				,       """text_model.encoder.layers."""				)
 if "ln_1" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""ln_1"""				,       """layer_norm1"""				)
 if "ln_2" in name:
  _snake_case    :  Optional[Any]					=					name.replace("""ln_2"""				,       """layer_norm2"""				)
 if "c_fc" in name:
  _snake_case    :  Union[str, Any]					=					name.replace("""c_fc"""				,       """fc1"""				)
 if "c_proj" in name:
  _snake_case    :  Tuple					=					name.replace("""c_proj"""				,       """fc2"""				)
 if "text_encoder" in name:
  _snake_case    :  Any					=					name.replace("""text_encoder"""				,       """text_model"""				)
 if "ln_final" in name:
  _snake_case    :  str					=					name.replace("""ln_final"""				,       """final_layer_norm"""				)
 # projection layers
 if "img_projector.linear_hidden." in name:
  _snake_case    :  List[Any]					=					name.replace("""img_projector.linear_hidden."""				,       """visual_projection."""				)
 if "img_projector.linear_out." in name:
  _snake_case    :  Optional[int]					=					name.replace("""img_projector.linear_out."""				,       """visual_projection.3."""				)
 if "text_projector.linear_hidden" in name:
  _snake_case    :  List[Any]					=					name.replace("""text_projector.linear_hidden"""				,       """text_projection"""				)
 if "text_projector.linear_out" in name:
  _snake_case    :  Tuple					=					name.replace("""text_projector.linear_out"""				,       """text_projection.3"""				)

 return name



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	int				)  ->     List[str]:
 for key in orig_state_dict.copy().keys():
  _snake_case    :  Tuple					=					orig_state_dict.pop(SCREAMING_SNAKE_CASE__				)

  if "qkv" in key:
   # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   _snake_case    :  Any					=					key.split("""."""				)
   _snake_case	,    _snake_case    :  List[Any]					=					int(key_split[2]				), int(key_split[4]				)
   _snake_case    :  List[Any]					=					config.vision_config.hidden_size
   if "weight" in key:
    _snake_case    :  List[Any]					=					val[:dim, :]
    _snake_case    :  Union[str, Any]					=					val[dim : dim * 2, :]
    _snake_case    :  int					=					val[-dim:, :]
   else:
    _snake_case    :  Union[str, Any]					=					val[:dim]
    _snake_case    :  str					=					val[dim : dim * 2]
    _snake_case    :  Dict					=					val[-dim:]
  elif "in_proj" in key:
   # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
   # we need to split them up into separate matrices/vectors
   _snake_case    :  int					=					key.split("""."""				)
   _snake_case    :  Optional[int]					=					int(key_split[3]				)
   _snake_case    :  Union[str, Any]					=					config.text_config.hidden_size
   if "weight" in key:
    _snake_case    :  int					=					val[:dim, :]
    _snake_case    :  Tuple					=					val[
        dim : dim * 2, :
    ]
    _snake_case    :  int					=					val[-dim:, :]
   else:
    _snake_case    :  Tuple					=					val[:dim]
    _snake_case    :  Tuple					=					val[dim : dim * 2]
    _snake_case    :  str					=					val[-dim:]
  else:
   _snake_case    :  Tuple					=					rename_key(SCREAMING_SNAKE_CASE__				)
   # squeeze if necessary
   if (
       "text_projection.0" in new_name
       or "text_projection.3" in new_name
       or "visual_projection.0" in new_name
       or "visual_projection.3" in new_name
   ):
    _snake_case    :  Any					=					val.squeeze_()
   else:
    _snake_case    :  Optional[int]					=					val

 return orig_state_dict



def       lowercase       (   )  ->     List[str]:
 _snake_case    :  List[str]					=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 _snake_case    :  Dict					=					Image.open(requests.get(SCREAMING_SNAKE_CASE__				,       stream=SCREAMING_SNAKE_CASE__				).raw				)
 return im



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	int="groupvit-gcc-yfcc"				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=False				)  ->     Optional[Any]:
 _snake_case    :  Any					=					GroupViTConfig()
 _snake_case    :  List[Any]					=					GroupViTModel(SCREAMING_SNAKE_CASE__				).eval()

 _snake_case    :  Optional[int]					=					torch.load(SCREAMING_SNAKE_CASE__				,       map_location="""cpu"""				)["""model"""]
 _snake_case    :  Tuple					=					convert_state_dict(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 _snake_case	,    _snake_case    :  int					=					model.load_state_dict(SCREAMING_SNAKE_CASE__				,       strict=SCREAMING_SNAKE_CASE__				)
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE__				) == 0)

 # verify result
 _snake_case    :  List[Any]					=					CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""				)
 _snake_case    :  List[Any]					=					prepare_img()
 _snake_case    :  int					=					processor(text=["""a photo of a cat""", """a photo of a dog"""]				,       images=SCREAMING_SNAKE_CASE__				,       padding=SCREAMING_SNAKE_CASE__				,       return_tensors="""pt"""				)

 with torch.no_grad():
  _snake_case    :  Optional[int]					=					model(**SCREAMING_SNAKE_CASE__				)

 if model_name == "groupvit-gcc-yfcc":
  _snake_case    :  Union[str, Any]					=					torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]				)
 elif model_name == "groupvit-gcc-redcaps":
  _snake_case    :  Union[str, Any]					=					torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]				)
 else:
  raise ValueError(F'''Model name {model_name} not supported.'''				)
 assert torch.allclose(outputs.logits_per_image				,       SCREAMING_SNAKE_CASE__				,       atol=1e-3				)

 processor.save_pretrained(SCREAMING_SNAKE_CASE__				)
 model.save_pretrained(SCREAMING_SNAKE_CASE__				)
 print("""Successfully saved processor and model to"""				,       SCREAMING_SNAKE_CASE__				)

 if push_to_hub:
  print("""Pushing to the hub..."""				)
  processor.push_to_hub(SCREAMING_SNAKE_CASE__				,       organization="""nielsr"""				)
  model.push_to_hub(SCREAMING_SNAKE_CASE__				,       organization="""nielsr"""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
  )
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
  parser.add_argument(
      """--model_name""",
      default="""groupvit-gccy-fcc""",
      type=str,
      help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
      help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
  )
  a__    =	parser.parse_args()

  convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
