


"""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"""







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def _A						(						lowercase		):







    """simple docstring"""
    a					={}
    a					=tokenizer(example['''content''']	,   truncation=lowercase		)['''input_ids''']
    a					=len(example['''content''']		) / len(output['''input_ids''']		)
    return output


lowerCamelCase_       :       Optional[int]      						=  HfArgumentParser(PretokenizationArguments)
lowerCamelCase_       :       Optional[Any]      						=  parser.parse_args()
if args.num_workers is None:
       lowerCamelCase_       :       Tuple      						=  multiprocessing.cpu_count()
lowerCamelCase_       :       Any      						=  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCamelCase_       :       Any      						=  time.time()
lowerCamelCase_       :       int      						=  load_dataset(args.dataset_name, split="""train""")
print(F'Dataset loaded in {time.time()-t_start:.2f}s')

lowerCamelCase_       :       List[str]      						=  time.time()
lowerCamelCase_       :       str      						=  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'Dataset tokenized in {time.time()-t_start:.2f}s')

lowerCamelCase_       :       Union[str, Any]      						=  time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')