






"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def    _A     (__a				)					->			Any:
    """simple docstring"""






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

    return name



def    _A     (__a       ,							__a				)					->			str:
    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						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
            SCREAMING_SNAKE_CASE_ :    List[Any]       		=						key.split('''.'''				)
            SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						int(key_split[2]				), int(key_split[4]				)
            SCREAMING_SNAKE_CASE_ :    Tuple       		=						config.vision_config.hidden_size
            if "weight" in key:
                SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						val[:dim, :]
                SCREAMING_SNAKE_CASE_ :    List[Any]       		=						val[dim : dim * 2, :]
                SCREAMING_SNAKE_CASE_ :    Dict       		=						val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE_ :    Optional[Any]       		=						val[:dim]
                SCREAMING_SNAKE_CASE_ :    str       		=						val[dim : dim * 2]
                SCREAMING_SNAKE_CASE_ :    Tuple       		=						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
            SCREAMING_SNAKE_CASE_ :    List[str]       		=						key.split('''.'''				)
            SCREAMING_SNAKE_CASE_ :    List[Any]       		=						int(key_split[3]				)
            SCREAMING_SNAKE_CASE_ :    str       		=						config.text_config.hidden_size
            if "weight" in key:
                SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						val[:dim, :]
                SCREAMING_SNAKE_CASE_ :    Tuple       		=						val[
                    dim : dim * 2, :
                ]
                SCREAMING_SNAKE_CASE_ :    Dict       		=						val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						val[:dim]
                SCREAMING_SNAKE_CASE_ :    Tuple       		=						val[dim : dim * 2]
                SCREAMING_SNAKE_CASE_ :    List[str]       		=						val[-dim:]
        else:
            SCREAMING_SNAKE_CASE_ :    int       		=						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
            ):
                SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						val.squeeze_()
            else:
                SCREAMING_SNAKE_CASE_ :    Dict       		=						val

    return orig_state_dict



def    _A     ()					->			List[str]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_ :    List[str]       		=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE_ :    Union[str, Any]       		=						Image.open(requests.get(lowercase_       ,							stream=lowercase_				).raw				)
    return im



@torch.no_grad()
def    _A     (__a       ,							__a       ,							__a="groupvit-gcc-yfcc"       ,							__a=False				)					->			Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_ :    List[Any]       		=						GroupViTConfig()
    SCREAMING_SNAKE_CASE_ :    Dict       		=						GroupViTModel(lowercase_				).eval()

    SCREAMING_SNAKE_CASE_ :    Any       		=						torch.load(lowercase_       ,							map_location='''cpu'''				)['''model''']
    SCREAMING_SNAKE_CASE_ :    List[Any]       		=						convert_state_dict(lowercase_       ,							lowercase_				)
    SCREAMING_SNAKE_CASE_ :    Tuple       		=						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
    SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''				)
    SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						prepare_img()
    SCREAMING_SNAKE_CASE_ :    Optional[int]       		=						processor(text=['''a photo of a cat''', '''a photo of a dog''']       ,							images=lowercase_       ,							padding=lowercase_       ,							return_tensors='''pt'''				)

    with torch.no_grad():
        SCREAMING_SNAKE_CASE_ :    int       		=						model(**lowercase_				)

    if model_name == "groupvit-gcc-yfcc":
        SCREAMING_SNAKE_CASE_ :    List[Any]       		=						torch.tensor([[13.35_23, 6.36_29]]				)
    elif model_name == "groupvit-gcc-redcaps":
        SCREAMING_SNAKE_CASE_ :    str       		=						torch.tensor([[16.18_73, 8.62_30]]				)
    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__":
  UpperCAmelCase_  :					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`.""",
  )
  UpperCAmelCase_  :					int       =					parser.parse_args()

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






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						'''mobilenet_v2'''


	def __init__(    self				,     lowercase__=3				,     lowercase__=2_2_4				,     lowercase__=1.0				,     lowercase__=8				,     lowercase__=8				,     lowercase__=6				,     lowercase__=3_2				,     lowercase__=True				,     lowercase__=True				,     lowercase__="relu6"				,     lowercase__=True				,     lowercase__=0.8				,     lowercase__=0.0_2				,     lowercase__=0.0_0_1				,     lowercase__=2_5_5				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

							if depth_multiplier <= 0:
													raise ValueError('''depth_multiplier must be greater than zero.''')

							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Dict	    =	depth_multiplier
							__UpperCAmelCase			:							Optional[Any]	    =	depth_divisible_by
							__UpperCAmelCase			:							Union[str, Any]	    =	min_depth
							__UpperCAmelCase			:							Dict	    =	expand_ratio
							__UpperCAmelCase			:							int	    =	output_stride
							__UpperCAmelCase			:							Tuple	    =	first_layer_is_expansion
							__UpperCAmelCase			:							Union[str, Any]	    =	finegrained_output
							__UpperCAmelCase			:							int	    =	hidden_act
							__UpperCAmelCase			:							Tuple	    =	tf_padding
							__UpperCAmelCase			:							List[str]	    =	classifier_dropout_prob
							__UpperCAmelCase			:							Optional[Any]	    =	initializer_range
							__UpperCAmelCase			:							List[str]	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	semantic_loss_ignore_index






class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						version.parse('''1.11''' )


	@property
	def  A(    self):
							return OrderedDict([('''pixel_values''', {0: '''batch'''})])


	@property
	def  A(    self):
							if self.task == "image-classification":
													return OrderedDict([('''logits''', {0: '''batch'''})])
							else:
													return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])


	@property
	def  A(    self):
							return 1e-4

