






'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







def    lowercase__			(				__UpperCamelCase   )->			int:
      # vision encoder
      if "img_encoder.pos_embed" in name:
            UpperCamelCase      = name.replace("""img_encoder.pos_embed"""		,						"""vision_model.embeddings.position_embeddings"""   )
      if "img_encoder.patch_embed.proj" in name:
            UpperCamelCase      = name.replace("""img_encoder.patch_embed.proj"""		,						"""vision_model.embeddings.patch_embeddings.projection"""   )
      if "img_encoder.patch_embed.norm" in name:
            UpperCamelCase      = name.replace("""img_encoder.patch_embed.norm"""		,						"""vision_model.embeddings.layernorm"""   )
      if "img_encoder.layers" in name:
            UpperCamelCase      = name.replace("""img_encoder.layers"""		,						"""vision_model.encoder.stages"""   )
      if "blocks" in name and "res" not in name:
            UpperCamelCase      = name.replace("""blocks"""		,						"""layers"""   )
      if "attn" in name and "pre_assign" not in name:
            UpperCamelCase      = name.replace("""attn"""		,						"""self_attn"""   )
      if "proj" in name and "self_attn" in name and "text" not in name:
            UpperCamelCase      = name.replace("""proj"""		,						"""out_proj"""   )
      if "pre_assign_attn.attn.proj" in name:
            UpperCamelCase      = name.replace("""pre_assign_attn.attn.proj"""		,						"""pre_assign_attn.attn.out_proj"""   )
      if "norm1" in name:
            UpperCamelCase      = name.replace("""norm1"""		,						"""layer_norm1"""   )
      if "norm2" in name and "pre_assign" not in name:
            UpperCamelCase      = name.replace("""norm2"""		,						"""layer_norm2"""   )
      if "img_encoder.norm" in name:
            UpperCamelCase      = name.replace("""img_encoder.norm"""		,						"""vision_model.layernorm"""   )
      # text encoder
      if "text_encoder.token_embedding" in name:
            UpperCamelCase      = name.replace("""text_encoder.token_embedding"""		,						"""text_model.embeddings.token_embedding"""   )
      if "text_encoder.positional_embedding" in name:
            UpperCamelCase      = name.replace("""text_encoder.positional_embedding"""		,						"""text_model.embeddings.position_embedding.weight"""   )
      if "text_encoder.transformer.resblocks." in name:
            UpperCamelCase      = name.replace("""text_encoder.transformer.resblocks."""		,						"""text_model.encoder.layers."""   )
      if "ln_1" in name:
            UpperCamelCase      = name.replace("""ln_1"""		,						"""layer_norm1"""   )
      if "ln_2" in name:
            UpperCamelCase      = name.replace("""ln_2"""		,						"""layer_norm2"""   )
      if "c_fc" in name:
            UpperCamelCase      = name.replace("""c_fc"""		,						"""fc1"""   )
      if "c_proj" in name:
            UpperCamelCase      = name.replace("""c_proj"""		,						"""fc2"""   )
      if "text_encoder" in name:
            UpperCamelCase      = name.replace("""text_encoder"""		,						"""text_model"""   )
      if "ln_final" in name:
            UpperCamelCase      = name.replace("""ln_final"""		,						"""final_layer_norm"""   )
      # projection layers
      if "img_projector.linear_hidden." in name:
            UpperCamelCase      = name.replace("""img_projector.linear_hidden."""		,						"""visual_projection."""   )
      if "img_projector.linear_out." in name:
            UpperCamelCase      = name.replace("""img_projector.linear_out."""		,						"""visual_projection.3."""   )
      if "text_projector.linear_hidden" in name:
            UpperCamelCase      = name.replace("""text_projector.linear_hidden"""		,						"""text_projection"""   )
      if "text_projector.linear_out" in name:
            UpperCamelCase      = name.replace("""text_projector.linear_out"""		,						"""text_projection.3"""   )

      return name







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Optional[Any]:
      for key in orig_state_dict.copy().keys():
            UpperCamelCase      = 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
                  UpperCamelCase      = key.split("""."""   )
                  UpperCamelCase  ,UpperCamelCase      = int(key_split[2]   ), int(key_split[4]   )
                  UpperCamelCase      = config.vision_config.hidden_size
                  if "weight" in key:
                        UpperCamelCase      = val[:dim, :]
                        UpperCamelCase      = val[dim : dim * 2, :]
                        UpperCamelCase      = val[-dim:, :]
                  else:
                        UpperCamelCase      = val[:dim]
                        UpperCamelCase      = val[dim : dim * 2]
                        UpperCamelCase      = 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
                  UpperCamelCase      = key.split("""."""   )
                  UpperCamelCase      = int(key_split[3]   )
                  UpperCamelCase      = config.text_config.hidden_size
                  if "weight" in key:
                        UpperCamelCase      = val[:dim, :]
                        UpperCamelCase      = val[
                            dim : dim * 2, :
                        ]
                        UpperCamelCase      = val[-dim:, :]
                  else:
                        UpperCamelCase      = val[:dim]
                        UpperCamelCase      = val[dim : dim * 2]
                        UpperCamelCase      = val[-dim:]
            else:
                  UpperCamelCase      = 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
                  ):
                        UpperCamelCase      = val.squeeze_()
                  else:
                        UpperCamelCase      = val

      return orig_state_dict







def    lowercase__			(				)->			int:
      UpperCamelCase      = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase      = Image.open(requests.get(__UpperCamelCase		,						stream=__UpperCamelCase   ).raw   )
      return im







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase="groupvit-gcc-yfcc"		,						__UpperCamelCase=False   )->			str:
      UpperCamelCase      = GroupViTConfig()
      UpperCamelCase      = GroupViTModel(__UpperCamelCase   ).eval()

      UpperCamelCase      = torch.load(__UpperCamelCase		,						map_location="""cpu"""   )["""model"""]
      UpperCamelCase      = convert_state_dict(__UpperCamelCase		,						__UpperCamelCase   )
      UpperCamelCase  ,UpperCamelCase      = 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
      UpperCamelCase      = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32"""   )
      UpperCamelCase      = prepare_img()
      UpperCamelCase      = processor(text=["""a photo of a cat""", """a photo of a dog"""]		,						images=__UpperCamelCase		,						padding=__UpperCamelCase		,						return_tensors="""pt"""   )

      with torch.no_grad():
            UpperCamelCase      = model(**__UpperCamelCase   )

      if model_name == "groupvit-gcc-yfcc":
            UpperCamelCase      = torch.tensor([[13.3_523, 6.3_629]]   )
      elif model_name == "groupvit-gcc-redcaps":
            UpperCamelCase      = 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		,						__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__":
     SCREAMING_SNAKE_CASE__     =       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`.',
     )
     SCREAMING_SNAKE_CASE__     =       parser.parse_args()

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










'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""Salesforce/blip-image-captioning-base"""
  lowercase							=				(
      """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
      """image to caption, and returns a text that contains the description in English."""
  )
  lowercase							=				"""image_captioner"""
  lowercase							=				AutoModelForVisionaSeq

  lowercase							=				["""image"""]
  lowercase							=				["""text"""]

  def __init__(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        requires_backends(self      ,		["""vision"""]				)
        super().__init__(*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self.pre_processor(images=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self.model.generate(**_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        return self.pre_processor.batch_decode(_SCREAMING_SNAKE_CASE      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE				)[0].strip()
