


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]				)	->		Tuple:

       """simple docstring"""

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

       return name







def        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			Union[str, Any]				)	->		List[Any]:

       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              a_					:		str							=  orig_state_dict.pop(__A				)

              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_					:		Optional[int]							=  key.split('.'				)
                     a_							,		a_					:		List[str]							=  int(key_split[2]				), int(key_split[4]				)
                     a_					:		str							=  config.vision_config.hidden_size
                     if "weight" in key:
                            a_					:		Dict							=  val[:dim, :]
                            a_					:		List[Any]							=  val[dim : dim * 2, :]
                            a_					:		str							=  val[-dim:, :]
                     else:
                            a_					:		List[str]							=  val[:dim]
                            a_					:		Union[str, Any]							=  val[dim : dim * 2]
                            a_					:		Any							=  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_					:		Optional[int]							=  key.split('.'				)
                     a_					:		Optional[Any]							=  int(key_split[3]				)
                     a_					:		List[str]							=  config.text_config.hidden_size
                     if "weight" in key:
                            a_					:		int							=  val[:dim, :]
                            a_					:		str							=  val[
                                dim : dim * 2, :
                            ]
                            a_					:		List[Any]							=  val[-dim:, :]
                     else:
                            a_					:		str							=  val[:dim]
                            a_					:		Union[str, Any]							=  val[dim : dim * 2]
                            a_					:		Dict							=  val[-dim:]
              else:
                     a_					:		List[Any]							=  rename_key(__A				)
                     # 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_					:		Tuple							=  val.squeeze_()
                     else:
                            a_					:		List[str]							=  val

       return orig_state_dict







def        SCREAMING_SNAKE_CASE_						(			)	->		List[Any]:

       """simple docstring"""

       a_					:		List[Any]							=  'http://images.cocodataset.org/val2017/000000039769.jpg'
       a_					:		List[str]							=  Image.open(requests.get(__A  ,    stream=__A				).raw				)
       return im







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

       """simple docstring"""

       a_					:		Optional[int]							=  GroupViTConfig()
       a_					:		Union[str, Any]							=  GroupViTModel(__A				).eval()

       a_					:		List[str]							=  torch.load(__A  ,    map_location='cpu'				)['model']
       a_					:		Dict							=  convert_state_dict(__A  ,    __A				)
       a_							,		a_					:		Tuple							=  model.load_state_dict(__A  ,    strict=__A				)
       assert missing_keys == ["text_model.embeddings.position_ids"]
       assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__A				) == 0)

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

       with torch.no_grad():
              a_					:		List[str]							=  model(**__A				)

       if model_name == "groupvit-gcc-yfcc":
              a_					:		Any							=  torch.tensor([[13.3523, 6.3629]]				)
       elif model_name == "groupvit-gcc-redcaps":
              a_					:		Optional[int]							=  torch.tensor([[16.1873, 8.6230]]				)
       else:
              raise ValueError(F"""Model name {model_name} not supported."""				)
       assert torch.allclose(outputs.logits_per_image  ,    __A  ,    atol=1e-3				)

       processor.save_pretrained(__A				)
       model.save_pretrained(__A				)
       print('Successfully saved processor and model to'  ,    __A				)

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


if __name__ == "__main__":
   UpperCAmelCase_      : Dict  					=       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`.',
   )
   UpperCAmelCase_      : int  					=       parser.parse_args()

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






import flax.linen as nn
import jax
import jax.numpy as jnp
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             a_					:		Dict							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Optional[int]:
             a_							,		a_							,		a_							,		a_					:		Union[str, Any]							=  hidden_states.shape
             a_					:		List[str]							=  jax.image.resize(
                 SCREAMING_SNAKE_CASE__     ,							shape=(batch, height * 2, width * 2, channels)     ,							method='nearest'     ,							)
             a_					:		Any							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[int]:
             a_					:		Optional[int]							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(2, 2)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Optional[int]:
             # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
             # hidden_states = jnp.pad(hidden_states, pad_width=pad)
             a_					:		str							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     int        =						None
      snake_case__				:     float        =						0.0
      snake_case__				:     bool        =						None
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             a_					:		List[str]							=  self.in_channels if self.out_channels is None else self.out_channels

             a_					:		Optional[int]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		Any							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		Optional[int]							=  nn.Dense(SCREAMING_SNAKE_CASE__     ,							dtype=self.dtype     )

             a_					:		Union[str, Any]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		int							=  nn.Dropout(self.dropout_prob     )
             a_					:		Optional[Any]							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		List[str]							=  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

             a_					:		List[Any]							=  None
             if use_nin_shortcut:
                    a_					:		Union[str, Any]							=  nn.Conv(
                        SCREAMING_SNAKE_CASE__     ,							kernel_size=(1, 1)     ,							strides=(1, 1)     ,							padding='VALID'     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     )		->			int:
             a_					:		List[Any]							=  hidden_states
             a_					:		Any							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  self.time_emb_proj(nn.swish(SCREAMING_SNAKE_CASE__     )     )
             a_					:		List[str]							=  jnp.expand_dims(jnp.expand_dims(SCREAMING_SNAKE_CASE__     ,							1     )     ,							1     )
             a_					:		Optional[int]							=  hidden_states + temb

             a_					:		List[str]							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  self.dropout(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             if self.conv_shortcut is not None:
                    a_					:		List[str]							=  self.conv_shortcut(SCREAMING_SNAKE_CASE__     )

             return hidden_states + residual


