

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)






'''simple docstring'''




import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowercase__       : Dict         =       logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			field(default=a__   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `max_length` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': (
                 '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                 '''to the `num_beams` value of the model configuration.'''
             )
         }   ,						)
     lowerCAmelCase						     =			field(
         default=a__   ,						metadata={
             '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
         }   ,						)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			super().to_dict()
          for k, v in d.items():
               if isinstance(_UpperCAmelCase		,      _UpperCAmelCase):
                    __A		:					List[Any]						=			v.to_dict()
          return d