


"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _A						(						lowercase		):







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

    return name
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    for key in orig_state_dict.copy().keys():
        a					=orig_state_dict.pop(lowercase		)

        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					=key.split('''.'''		)
            a		,				a					=int(key_split[2]		), int(key_split[4]		)
            a					=config.vision_config.hidden_size
            if "weight" in key:
                a					=val[:dim, :]
                a					=val[dim : dim * 2, :]
                a					=val[-dim:, :]
            else:
                a					=val[:dim]
                a					=val[dim : dim * 2]
                a					=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					=key.split('''.'''		)
            a					=int(key_split[3]		)
            a					=config.text_config.hidden_size
            if "weight" in key:
                a					=val[:dim, :]
                a					=val[
                    dim : dim * 2, :
                ]
                a					=val[-dim:, :]
            else:
                a					=val[:dim]
                a					=val[dim : dim * 2]
                a					=val[-dim:]
        else:
            a					=rename_key(lowercase		)
            # 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					=val.squeeze_()
            else:
                a					=val

    return orig_state_dict
def _A						(						):







    """simple docstring"""
    a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
    a					=Image.open(requests.get(lowercase	,   stream=lowercase		).raw		)
    return im
@torch.no_grad()
def _A						(						lowercase	,   lowercase	,   lowercase="groupvit-gcc-yfcc"	,   lowercase=False		):







    """simple docstring"""
    a					=GroupViTConfig()
    a					=GroupViTModel(lowercase		).eval()

    a					=torch.load(lowercase	,   map_location='''cpu'''		)['''model''']
    a					=convert_state_dict(lowercase	,   lowercase		)
    a		,				a					=model.load_state_dict(lowercase	,   strict=lowercase		)
    assert missing_keys == ["text_model.embeddings.position_ids"]
    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase		) == 0)

    # verify result
    a					=CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''		)
    a					=prepare_img()
    a					=processor(text=['''a photo of a cat''', '''a photo of a dog''']	,   images=lowercase	,   padding=lowercase	,   return_tensors='''pt'''		)

    with torch.no_grad():
        a					=model(**lowercase		)

    if model_name == "groupvit-gcc-yfcc":
        a					=torch.tensor([[13.3523, 6.3629]]		)
    elif model_name == "groupvit-gcc-redcaps":
        a					=torch.tensor([[16.1873, 8.6230]]		)
    else:
        raise ValueError(f'''Model name {model_name} not supported.'''		)
    assert torch.allclose(outputs.logits_per_image	,   lowercase	,   atol=1E-3		)

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

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


if __name__ == "__main__":
      lowerCamelCase_       :       Any      						=  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_       :       Optional[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 typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase_       :       Dict      						=  logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE      )
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   **__A						) ->					Dict:
     super().__init__(**__A						)

     if self.framework == "tf":
         raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''						)

     requires_backends(self			,   '''vision'''						)
     self.check_model_type(__A						)


 def __call__(		self			,   __A			,   __A = None			,   **__A			,   ) ->					List[str]:
     if "text_queries" in kwargs:
         a					=kwargs.pop('''text_queries'''						)

     if isinstance(__A			,   (str, Image.Image)						):
         a					={'''image''': image, '''candidate_labels''': candidate_labels}
     else:
         a					=image
     a					=super().__call__(__A			,   **__A						)
     return results


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[Any]:
     a					={}
     if "threshold" in kwargs:
         a					=kwargs['''threshold''']
     if "top_k" in kwargs:
         a					=kwargs['''top_k''']
     return {}, {}, postprocess_params


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					str:
     a					=load_image(inputs['''image''']						)
     a					=inputs['''candidate_labels''']
     if isinstance(__A			,   __A						):
         a					=candidate_labels.split(''','''						)

     a					=torch.tensor([[image.height, image.width]]			,   dtype=torch.intaa						)
     for i, candidate_label in enumerate(__A						):
         a					=self.tokenizer(__A			,   return_tensors=self.framework						)
         a					=self.image_processor(__A			,   return_tensors=self.framework						)
         yield {
             "is_last": i == len(__A						) - 1,
             "target_size": target_size,
             "candidate_label": candidate_label,
             **text_inputs,
             **image_features,
         }


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[Any]:
     a					=model_inputs.pop('''target_size'''						)
     a					=model_inputs.pop('''candidate_label'''						)
     a					=model_inputs.pop('''is_last'''						)

     a					=self.model(**__A						)

     a					={'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
     return model_outputs


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0.1			,   __A=None						) ->					List[str]:
     a					=[]
     for model_output in model_outputs:
         a					=model_output['''candidate_label''']
         a					=BaseModelOutput(__A						)
         a					=self.image_processor.post_process_object_detection(
             outputs=__A			,   threshold=__A			,   target_sizes=model_output['''target_size''']						)[0]

         for index in outputs["scores"].nonzero():
             a					=outputs['''scores'''][index].item()
             a					=self._get_bounding_box(outputs['''boxes'''][index][0]						)

             a					={'''score''': score, '''label''': label, '''box''': box}
             results.append(__A						)

     a					=sorted(__A			,   key=lambda __A						: x["score"]			,   reverse=__A						)
     if top_k:
         a					=results[:top_k]

     return results





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict[str, int]:
     if self.framework != "pt":
         raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''						)
     a		,				a		,				a		,				a					=box.int().tolist()
     a					={
         '''xmin''': xmin,
         '''ymin''': ymin,
         '''xmax''': xmax,
         '''ymax''': ymax,
     }
     return bbox