
'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def 	_snake_case						(      A_       :    Dict   ):
  """simple docstring"""







  if "img_encoder.pos_embed" in name:
    a_      :	Optional[Any]      =							name.replace("""img_encoder.pos_embed"""     ,		"""vision_model.embeddings.position_embeddings"""   )
  if "img_encoder.patch_embed.proj" in name:
    a_      :	Dict      =							name.replace("""img_encoder.patch_embed.proj"""     ,		"""vision_model.embeddings.patch_embeddings.projection"""   )
  if "img_encoder.patch_embed.norm" in name:
    a_      :	str      =							name.replace("""img_encoder.patch_embed.norm"""     ,		"""vision_model.embeddings.layernorm"""   )
  if "img_encoder.layers" in name:
    a_      :	Optional[Any]      =							name.replace("""img_encoder.layers"""     ,		"""vision_model.encoder.stages"""   )
  if "blocks" in name and "res" not in name:
    a_      :	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_      :	Dict      =							name.replace("""proj"""     ,		"""out_proj"""   )
  if "pre_assign_attn.attn.proj" in name:
    a_      :	Optional[int]      =							name.replace("""pre_assign_attn.attn.proj"""     ,		"""pre_assign_attn.attn.out_proj"""   )
  if "norm1" in name:
    a_      :	Union[str, Any]      =							name.replace("""norm1"""     ,		"""layer_norm1"""   )
  if "norm2" in name and "pre_assign" not in name:
    a_      :	Tuple      =							name.replace("""norm2"""     ,		"""layer_norm2"""   )
  if "img_encoder.norm" in name:
    a_      :	Any      =							name.replace("""img_encoder.norm"""     ,		"""vision_model.layernorm"""   )
  # text encoder
  if "text_encoder.token_embedding" in name:
    a_      :	Dict      =							name.replace("""text_encoder.token_embedding"""     ,		"""text_model.embeddings.token_embedding"""   )
  if "text_encoder.positional_embedding" in name:
    a_      :	Optional[int]      =							name.replace("""text_encoder.positional_embedding"""     ,		"""text_model.embeddings.position_embedding.weight"""   )
  if "text_encoder.transformer.resblocks." in name:
    a_      :	str      =							name.replace("""text_encoder.transformer.resblocks."""     ,		"""text_model.encoder.layers."""   )
  if "ln_1" in name:
    a_      :	Dict      =							name.replace("""ln_1"""     ,		"""layer_norm1"""   )
  if "ln_2" in name:
    a_      :	Optional[Any]      =							name.replace("""ln_2"""     ,		"""layer_norm2"""   )
  if "c_fc" in name:
    a_      :	int      =							name.replace("""c_fc"""     ,		"""fc1"""   )
  if "c_proj" in name:
    a_      :	Tuple      =							name.replace("""c_proj"""     ,		"""fc2"""   )
  if "text_encoder" in name:
    a_      :	List[str]      =							name.replace("""text_encoder"""     ,		"""text_model"""   )
  if "ln_final" in name:
    a_      :	Dict      =							name.replace("""ln_final"""     ,		"""final_layer_norm"""   )
  # projection layers
  if "img_projector.linear_hidden." in name:
    a_      :	Dict      =							name.replace("""img_projector.linear_hidden."""     ,		"""visual_projection."""   )
  if "img_projector.linear_out." in name:
    a_      :	List[str]      =							name.replace("""img_projector.linear_out."""     ,		"""visual_projection.3."""   )
  if "text_projector.linear_hidden" in name:
    a_      :	Optional[int]      =							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 	_snake_case						(      A_       :    int     ,		A_       :    Tuple   ):
  """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_      :	Union[str, Any]      =							key.split("""."""   )
      a_      , a_      :	Dict      =							int(key_split[2]   ), int(key_split[4]   )
      a_      :	List[str]      =							config.vision_config.hidden_size
      if "weight" in key:
        a_      :	Optional[int]      =							val[:dim, :]
        a_      :	int      =							val[dim : dim * 2, :]
        a_      :	str      =							val[-dim:, :]
      else:
        a_      :	Optional[Any]      =							val[:dim]
        a_      :	Tuple      =							val[dim : dim * 2]
        a_      :	Tuple      =							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_      :	List[Any]      =							int(key_split[3]   )
      a_      :	Dict      =							config.text_config.hidden_size
      if "weight" in key:
        a_      :	List[Any]      =							val[:dim, :]
        a_      :	Tuple      =							val[
            dim : dim * 2, :
        ]
        a_      :	Tuple      =							val[-dim:, :]
      else:
        a_      :	Any      =							val[:dim]
        a_      :	List[Any]      =							val[dim : dim * 2]
        a_      :	str      =							val[-dim:]
    else:
      a_      :	Union[str, 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_      :	Optional[Any]      =							val.squeeze_()
      else:
        a_      :	str      =							val

  return orig_state_dict






def 	_snake_case						(      ):
  """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 	_snake_case						(      A_       :    int     ,		A_       :    List[Any]     ,		A_       :    Any="groupvit-gcc-yfcc"     ,		A_       :    List[Any]=False   ):
  """simple docstring"""







  a_      :	Tuple      =							GroupViTConfig()
  a_      :	List[str]      =							GroupViTModel(A_   ).eval()

  a_      :	Dict      =							torch.load(A_     ,		map_location="""cpu"""   )["""model"""]
  a_      :	List[str]      =							convert_state_dict(A_     ,		A_   )
  a_      , a_      :	Any      =							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_      :	Optional[Any]      =							CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""   )
  a_      :	List[Any]      =							prepare_img()
  a_      :	List[Any]      =							processor(text=["""a photo of a cat""", """a photo of a dog"""]     ,		images=A_     ,		padding=A_     ,		return_tensors="""pt"""   )

  with torch.no_grad():
    a_      :	Tuple      =							model(**A_   )

  if model_name == "groupvit-gcc-yfcc":
    a_      :	Optional[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__":
    __snake_case:  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`.",
    )
    __snake_case:  Union[str, Any]								=			parser.parse_args()

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




'''simple docstring'''

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


