

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple      )							->      str:





    '''simple docstring'''






    for key in orig_state_dict.copy().keys():
        A__          =   orig_state_dict.pop(SCREAMING_SNAKE_CASE__      )

        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(SCREAMING_SNAKE_CASE__      )
            # 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 						_snake_case(    )							->      Optional[int]:





    '''simple docstring'''






    A__          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
    A__          =   Image.open(requests.get(SCREAMING_SNAKE_CASE__    ,			stream=SCREAMING_SNAKE_CASE__      ).raw      )
    return im
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	List[str]="groupvit-gcc-yfcc"    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=False      )							->      Dict:





    '''simple docstring'''






    A__          =   GroupViTConfig()
    A__          =   GroupViTModel(SCREAMING_SNAKE_CASE__      ).eval()

    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location='cpu'      )['model']
    A__          =   convert_state_dict(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   model.load_state_dict(SCREAMING_SNAKE_CASE__    ,			strict=SCREAMING_SNAKE_CASE__      )
    assert missing_keys == ["text_model.embeddings.position_ids"]
    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE__      ) == 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=SCREAMING_SNAKE_CASE__    ,			padding=SCREAMING_SNAKE_CASE__    ,			return_tensors='pt'      )

    with torch.no_grad():
        A__          =   model(**SCREAMING_SNAKE_CASE__      )

    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    ,			SCREAMING_SNAKE_CASE__    ,			atol=1E-3      )

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

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


if __name__ == "__main__":
     lowercase_       		=  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`.",
     )
     lowercase_       		=  parser.parse_args()

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




import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

