

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def       __snake_case						( __UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      if "img_encoder.pos_embed" in name:
            A_						  =				name.replace("img_encoder.pos_embed"				,"vision_model.embeddings.position_embeddings"    )
      if "img_encoder.patch_embed.proj" in name:
            A_						  =				name.replace("img_encoder.patch_embed.proj"				,"vision_model.embeddings.patch_embeddings.projection"    )
      if "img_encoder.patch_embed.norm" in name:
            A_						  =				name.replace("img_encoder.patch_embed.norm"				,"vision_model.embeddings.layernorm"    )
      if "img_encoder.layers" in name:
            A_						  =				name.replace("img_encoder.layers"				,"vision_model.encoder.stages"    )
      if "blocks" in name and "res" not in name:
            A_						  =				name.replace("blocks"				,"layers"    )
      if "attn" in name and "pre_assign" not in name:
            A_						  =				name.replace("attn"				,"self_attn"    )
      if "proj" in name and "self_attn" in name and "text" not in name:
            A_						  =				name.replace("proj"				,"out_proj"    )
      if "pre_assign_attn.attn.proj" in name:
            A_						  =				name.replace("pre_assign_attn.attn.proj"				,"pre_assign_attn.attn.out_proj"    )
      if "norm1" in name:
            A_						  =				name.replace("norm1"				,"layer_norm1"    )
      if "norm2" in name and "pre_assign" not in name:
            A_						  =				name.replace("norm2"				,"layer_norm2"    )
      if "img_encoder.norm" in name:
            A_						  =				name.replace("img_encoder.norm"				,"vision_model.layernorm"    )
      # text encoder
      if "text_encoder.token_embedding" in name:
            A_						  =				name.replace("text_encoder.token_embedding"				,"text_model.embeddings.token_embedding"    )
      if "text_encoder.positional_embedding" in name:
            A_						  =				name.replace("text_encoder.positional_embedding"				,"text_model.embeddings.position_embedding.weight"    )
      if "text_encoder.transformer.resblocks." in name:
            A_						  =				name.replace("text_encoder.transformer.resblocks."				,"text_model.encoder.layers."    )
      if "ln_1" in name:
            A_						  =				name.replace("ln_1"				,"layer_norm1"    )
      if "ln_2" in name:
            A_						  =				name.replace("ln_2"				,"layer_norm2"    )
      if "c_fc" in name:
            A_						  =				name.replace("c_fc"				,"fc1"    )
      if "c_proj" in name:
            A_						  =				name.replace("c_proj"				,"fc2"    )
      if "text_encoder" in name:
            A_						  =				name.replace("text_encoder"				,"text_model"    )
      if "ln_final" in name:
            A_						  =				name.replace("ln_final"				,"final_layer_norm"    )
      # projection layers
      if "img_projector.linear_hidden." in name:
            A_						  =				name.replace("img_projector.linear_hidden."				,"visual_projection."    )
      if "img_projector.linear_out." in name:
            A_						  =				name.replace("img_projector.linear_out."				,"visual_projection.3."    )
      if "text_projector.linear_hidden" in name:
            A_						  =				name.replace("text_projector.linear_hidden"				,"text_projection"    )
      if "text_projector.linear_out" in name:
            A_						  =				name.replace("text_projector.linear_out"				,"text_projection.3"    )

      return name

def       __snake_case						( __UpperCamelCase	: List[str]				,__UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            A_						  =				orig_state_dict.pop(__UpperCamelCase    )

            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
                  A_						  =				key.split("."    )
                  A_  ,     A_						  =				int(key_split[2]    ), int(key_split[4]    )
                  A_						  =				config.vision_config.hidden_size
                  if "weight" in key:
                        A_						  =				val[:dim, :]
                        A_						  =				val[dim : dim * 2, :]
                        A_						  =				val[-dim:, :]
                  else:
                        A_						  =				val[:dim]
                        A_						  =				val[dim : dim * 2]
                        A_						  =				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
                  A_						  =				key.split("."    )
                  A_						  =				int(key_split[3]    )
                  A_						  =				config.text_config.hidden_size
                  if "weight" in key:
                        A_						  =				val[:dim, :]
                        A_						  =				val[
                            dim : dim * 2, :
                        ]
                        A_						  =				val[-dim:, :]
                  else:
                        A_						  =				val[:dim]
                        A_						  =				val[dim : dim * 2]
                        A_						  =				val[-dim:]
            else:
                  A_						  =				rename_key(__UpperCamelCase    )
                  # 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
                  ):
                        A_						  =				val.squeeze_()
                  else:
                        A_						  =				val

      return orig_state_dict

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
      A_						  =				Image.open(requests.get(__UpperCamelCase				,stream=__UpperCamelCase    ).raw    )
      return im

@torch.no_grad()
def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Optional[int]="groupvit-gcc-yfcc"				,__UpperCamelCase	: List[Any]=False    ):



      """simple docstring"""

      A_						  =				GroupViTConfig()
      A_						  =				GroupViTModel(__UpperCamelCase    ).eval()

      A_						  =				torch.load(__UpperCamelCase				,map_location="cpu"    )["model"]
      A_						  =				convert_state_dict(__UpperCamelCase				,__UpperCamelCase    )
      A_  ,     A_						  =				model.load_state_dict(__UpperCamelCase				,strict=__UpperCamelCase    )
      assert missing_keys == ["text_model.embeddings.position_ids"]
      assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCamelCase    ) == 0)

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

      with torch.no_grad():
            A_						  =				model(**__UpperCamelCase    )

      if model_name == "groupvit-gcc-yfcc":
            A_						  =				torch.tensor([[13.3523, 6.3629]]    )
      elif model_name == "groupvit-gcc-redcaps":
            A_						  =				torch.tensor([[16.1873, 8.6230]]    )
      else:
            raise ValueError(f'''Model name {model_name} not supported.'''    )
      assert torch.allclose(outputs.logits_per_image				,__UpperCamelCase				,atol=1E-3    )

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

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


if __name__ == "__main__":
      __a    :Tuple      =			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    :int      =			parser.parse_args()

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






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


_UpperCAmelCase							:			Optional[Any]        =							datasets.utils.logging.get_logger(__name__)

@dataclass
class   lowerCAmelCase				(   datasets.BuilderConfig     ):
 UpperCAmelCase__						=						1_00_00
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None


class   lowerCAmelCase				(   datasets.ArrowBasedBuilder     ):
 UpperCAmelCase__						=						ParquetConfig
 def 	A_      ( self    : int      )     ->      Optional[int]:
     return datasets.DatasetInfo(features=self.config.features      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : str      )     ->      Any:
     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}"""      )
     lowerCamelCase__       : List[Any]   					=						dl_manager.download_and_extract(self.config.data_files      )
     if isinstance(UpperCAmelCase     ,	(str, list, tuple)      ):
         lowerCamelCase__       : int   					=						data_files
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : int   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : str   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,	gen_kwargs={'files': files}      )]
     lowerCamelCase__       : Optional[Any]   					=						[]
     for split_name, files in data_files.items():
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : Optional[Any]   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : Dict   					=						[dl_manager.iter_files(UpperCAmelCase      ) 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(UpperCAmelCase      ):
                 with open(UpperCAmelCase     ,	'rb'      ) as f:
                     lowerCamelCase__       : Optional[Any]   					=						datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase      )      )
                 break
         splits.append(datasets.SplitGenerator(name=UpperCAmelCase     ,	gen_kwargs={'files': files}      )      )
     return splits
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : pa.Table      )     ->      pa.Table:
     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
         lowerCamelCase__       : Any   					=						table_cast(UpperCAmelCase     ,	self.info.features.arrow_schema      )
     return pa_table







 def 	A_      ( self    : Any     ,	UpperCAmelCase    : Tuple      )     ->      List[str]:
     lowerCamelCase__       : Optional[Any]   					=						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(UpperCAmelCase      )      ):
         with open(UpperCAmelCase     ,	'rb'      ) as f:
             lowerCamelCase__       : List[Any]   					=						pq.ParquetFile(UpperCAmelCase      )
             try:
                 for batch_idx, record_batch in enumerate(
                     parquet_file.iter_batches(batch_size=self.config.batch_size     ,	columns=self.config.columns      )      ):
                     lowerCamelCase__       : Tuple   					=						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(UpperCAmelCase      )
             except ValueError as e:
                 logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase      )}: {e}"""      )
                 raise





