


'''simple docstring'''


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




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

      return name




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Union[str, Any]):
      for key in orig_state_dict.copy().keys():
            A_						:  Dict          =				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
                  A_						:  Optional[int]          =				key.split(""".""")
                  A_   ,					A_						:  Optional[int]          =				int(key_split[2]), int(key_split[4])
                  A_						:  Union[str, Any]          =				config.vision_config.hidden_size
                  if "weight" in key:
                        A_						:  Tuple          =				val[:dim, :]
                        A_						:  int          =				val[dim : dim * 2, :]
                        A_						:  Union[str, Any]          =				val[-dim:, :]
                  else:
                        A_						:  Tuple          =				val[:dim]
                        A_						:  Optional[Any]          =				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_						:  Union[str, Any]          =				key.split(""".""")
                  A_						:  str          =				int(key_split[3])
                  A_						:  Optional[int]          =				config.text_config.hidden_size
                  if "weight" in key:
                        A_						:  Optional[int]          =				val[:dim, :]
                        A_						:  int          =				val[
                            dim : dim * 2, :
                        ]
                        A_						:  List[str]          =				val[-dim:, :]
                  else:
                        A_						:  str          =				val[:dim]
                        A_						:  Union[str, Any]          =				val[dim : dim * 2]
                        A_						:  int          =				val[-dim:]
            else:
                  A_						:  List[Any]          =				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
                  ):
                        A_						:  List[Any]          =				val.squeeze_()
                  else:
                        A_						:  Optional[Any]          =				val

      return orig_state_dict




def      lowerCamelCase					(      ):
      A_						:  Union[str, Any]          =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      A_						:  Any          =				Image.open(requests.get(lowerCamelCase							,						stream=lowerCamelCase).raw)
      return im




@torch.no_grad()
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      int							,						lowerCamelCase  :      List[str]="groupvit-gcc-yfcc"							,						lowerCamelCase  :      Dict=False):
      A_						:  int          =				GroupViTConfig()
      A_						:  Tuple          =				GroupViTModel(lowerCamelCase).eval()

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

      with torch.no_grad():
            A_						:  Optional[int]          =				model(**lowerCamelCase)

      if model_name == "groupvit-gcc-yfcc":
            A_						:  List[Any]          =				torch.tensor([[13.3523, 6.3629]])
      elif model_name == "groupvit-gcc-redcaps":
            A_						:  Tuple          =				torch.tensor([[16.1873, 8.6230]])
      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__":
     __magic_name__			=      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`.',
     )
     __magic_name__			=      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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__			=      {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
