





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Tuple:




    """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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """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 				__lowerCAmelCase(       )							-> str:




    """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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc"							,     _SCREAMING_SNAKE_CASE=False				)							-> Any:




    """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__":
 __A							:			Tuple         =			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`.",
 )
 __A							:			Tuple         =			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_tokenizers_available, is_torch_available


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

