





'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

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

   return name
def 							UpperCAmelCase_  (						__lowerCamelCase : str     ,__lowerCamelCase : Tuple			):
   for key in orig_state_dict.copy().keys():
      lowercase_      :Optional[Any]         =			orig_state_dict.pop(__lowerCamelCase			)

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

   return orig_state_dict
def 							UpperCAmelCase_  (						):
   lowercase_      :str         =			"http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_      :int         =			Image.open(requests.get(__lowerCamelCase     ,stream=__lowerCamelCase			).raw			)
   return im
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase : Tuple     ,__lowerCamelCase : str     ,__lowerCamelCase : int="groupvit-gcc-yfcc"     ,__lowerCamelCase : List[Any]=False			):
   lowercase_      :Union[str, Any]         =			GroupViTConfig()
   lowercase_      :Tuple         =			GroupViTModel(__lowerCamelCase			).eval()

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

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

   with torch.no_grad():
      lowercase_      :int         =			model(**__lowerCamelCase			)

   if model_name == "groupvit-gcc-yfcc":
      lowercase_      :Dict         =			torch.tensor([[13.3_523, 6.3_629]]			)
   elif model_name == "groupvit-gcc-redcaps":
      lowercase_      :List[Any]         =			torch.tensor([[16.1_873, 8.6_230]]			)
   else:
      raise ValueError(F'Model name {model_name} not supported.'			)
   assert torch.allclose(outputs.logits_per_image     ,__lowerCamelCase     ,atol=1e-3			)

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

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


if __name__ == "__main__":
    lowerCAmelCase :					Optional[int] 							=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`.''',
    )
    lowerCAmelCase :					int 							=parser.parse_args()

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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase :					Optional[int] 							={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Optional[Any] 							=[
     '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''WavLMForAudioFrameClassification''',
     '''WavLMForCTC''',
     '''WavLMForSequenceClassification''',
     '''WavLMForXVector''',
     '''WavLMModel''',
     '''WavLMPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavlm import (
      WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavLMForAudioFrameClassification,
      WavLMForCTC,
      WavLMForSequenceClassification,
      WavLMForXVector,
      WavLMModel,
      WavLMPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase :					Union[str, Any] 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


