



'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 						__A							(     UpperCAmelCase					)     ->			Tuple:




						'''simple docstring'''






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

						return name
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Any:




						'''simple docstring'''






						for key in orig_state_dict.copy().keys():
												_UpperCamelCase			:			Union[str, Any]   			=						orig_state_dict.pop(a_					)

												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
																		_UpperCamelCase			:			List[str]   			=						key.split("."					)
																		_UpperCamelCase			:			Dict   			=						int(key_split[2]					), int(key_split[4]					)
																		_UpperCamelCase			:			Any   			=						config.vision_config.hidden_size
																		if "weight" in key:
																								_UpperCamelCase			:			str   			=						val[:dim, :]
																								_UpperCamelCase			:			List[str]   			=						val[dim : dim * 2, :]
																								_UpperCamelCase			:			Union[str, Any]   			=						val[-dim:, :]
																		else:
																								_UpperCamelCase			:			str   			=						val[:dim]
																								_UpperCamelCase			:			Dict   			=						val[dim : dim * 2]
																								_UpperCamelCase			:			Union[str, Any]   			=						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
																		_UpperCamelCase			:			Any   			=						key.split("."					)
																		_UpperCamelCase			:			Any   			=						int(key_split[3]					)
																		_UpperCamelCase			:			List[str]   			=						config.text_config.hidden_size
																		if "weight" in key:
																								_UpperCamelCase			:			Tuple   			=						val[:dim, :]
																								_UpperCamelCase			:			str   			=						val[
																								    dim : dim * 2, :
																								]
																								_UpperCamelCase			:			List[str]   			=						val[-dim:, :]
																		else:
																								_UpperCamelCase			:			Tuple   			=						val[:dim]
																								_UpperCamelCase			:			Any   			=						val[dim : dim * 2]
																								_UpperCamelCase			:			List[Any]   			=						val[-dim:]
												else:
																		_UpperCamelCase			:			List[Any]   			=						rename_key(a_					)
																		# 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
																		):
																								_UpperCamelCase			:			Union[str, Any]   			=						val.squeeze_()
																		else:
																								_UpperCamelCase			:			Dict   			=						val

						return orig_state_dict
def 						__A							(     )     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						_UpperCamelCase			:			Any   			=						Image.open(requests.get(a_     ,stream=a_					).raw					)
						return im
@torch.no_grad()
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase="groupvit-gcc-yfcc"     ,UpperCAmelCase=False					)     ->			Any:




						'''simple docstring'''
						_UpperCamelCase			:			Tuple   			=						GroupViTConfig()
						_UpperCamelCase			:			Optional[int]   			=						GroupViTModel(a_					).eval()

						_UpperCamelCase			:			str   			=						torch.load(a_     ,map_location="cpu"					)['''model''']
						_UpperCamelCase			:			str   			=						convert_state_dict(a_     ,a_					)
						_UpperCamelCase			:			List[Any]   			=						model.load_state_dict(a_     ,strict=a_					)
						assert missing_keys == ["text_model.embeddings.position_ids"]
						assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a_					) == 0)

						# verify result
						_UpperCamelCase			:			Tuple   			=						CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"					)
						_UpperCamelCase			:			List[str]   			=						prepare_img()
						_UpperCamelCase			:			List[str]   			=						processor(text=["a photo of a cat", "a photo of a dog"]     ,images=a_     ,padding=a_     ,return_tensors="pt"					)

						with torch.no_grad():
												_UpperCamelCase			:			str   			=						model(**a_					)

						if model_name == "groupvit-gcc-yfcc":
												_UpperCamelCase			:			Union[str, Any]   			=						torch.tensor([[13.35_23, 6.36_29]]					)
						elif model_name == "groupvit-gcc-redcaps":
												_UpperCamelCase			:			List[Any]   			=						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     ,a_     ,atol=1E-3					)

						processor.save_pretrained(a_					)
						model.save_pretrained(a_					)
						print("Successfully saved processor and model to"     ,a_					)

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


if __name__ == "__main__":
			lowerCAmelCase_	:		str			=     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_	:		Union[str, 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 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_	:		Union[str, Any]			=     logging.get_logger(__name__)

lowerCAmelCase_	:		List[str]			=     {
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''yolos'''





				def __init__(						self					:						int       ,		lowercase__					:						List[str]=768       ,		lowercase__					:						Optional[Any]=12       ,		lowercase__					:						Union[str, Any]=12       ,		lowercase__					:						Any=3_072       ,		lowercase__					:						List[Any]="gelu"       ,		lowercase__					:						Dict=0.0       ,		lowercase__					:						Any=0.0       ,		lowercase__					:						Dict=0.0_2       ,		lowercase__					:						Tuple=1e-12       ,		lowercase__					:						str=[512, 864]       ,		lowercase__					:						Dict=16       ,		lowercase__					:						int=3       ,		lowercase__					:						Optional[Any]=True       ,		lowercase__					:						List[Any]=100       ,		lowercase__					:						str=True       ,		lowercase__					:						str=False       ,		lowercase__					:						List[str]=1       ,		lowercase__					:						Dict=5       ,		lowercase__					:						str=2       ,		lowercase__					:						Optional[int]=5       ,		lowercase__					:						Optional[int]=2       ,		lowercase__					:						Optional[Any]=0.1       ,		**lowercase__					:						Union[str, Any]       ,		)							->Tuple:


										'''simple docstring'''




										super().__init__(**lowercase__					)

										_UpperCamelCase			:			Optional[int]   			=						hidden_size
										_UpperCamelCase			:			str   			=						num_hidden_layers
										_UpperCamelCase			:			Optional[Any]   			=						num_attention_heads
										_UpperCamelCase			:			Optional[int]   			=						intermediate_size
										_UpperCamelCase			:			Union[str, Any]   			=						hidden_act
										_UpperCamelCase			:			Tuple   			=						hidden_dropout_prob
										_UpperCamelCase			:			Any   			=						attention_probs_dropout_prob
										_UpperCamelCase			:			Optional[int]   			=						initializer_range
										_UpperCamelCase			:			str   			=						layer_norm_eps
										_UpperCamelCase			:			Optional[int]   			=						image_size
										_UpperCamelCase			:			int   			=						patch_size
										_UpperCamelCase			:			Dict   			=						num_channels
										_UpperCamelCase			:			List[str]   			=						qkv_bias
										_UpperCamelCase			:			Dict   			=						num_detection_tokens
										_UpperCamelCase			:			int   			=						use_mid_position_embeddings
										_UpperCamelCase			:			int   			=						auxiliary_loss
										# Hungarian matcher
										_UpperCamelCase			:			Tuple   			=						class_cost
										_UpperCamelCase			:			str   			=						bbox_cost
										_UpperCamelCase			:			str   			=						giou_cost
										# Loss coefficients
										_UpperCamelCase			:			List[str]   			=						bbox_loss_coefficient
										_UpperCamelCase			:			str   			=						giou_loss_coefficient
										_UpperCamelCase			:			str   			=						eos_coefficient





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							version.parse('''1.11'''	)





				@property
				def  snake_case__				(						self					:						Union[str, Any]					)							->Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ]					)





				@property
				def  snake_case__				(						self					:						Any					)							->float:


										'''simple docstring'''




										return 1e-4







				@property
				def  snake_case__				(						self					:						List[str]					)							->int:


										'''simple docstring'''




										return 12


