
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict       ):




			"""simple docstring"""


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

			return name
def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			for key in orig_state_dict.copy().keys():
						lowerCAmelCase_      			=			orig_state_dict.pop(__lowerCAmelCase       )

						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
									lowerCAmelCase_      			=			key.split("."       )
									lowerCAmelCase_			,						lowerCAmelCase_      			=			int(key_split[2]       ), int(key_split[4]       )
									lowerCAmelCase_      			=			config.vision_config.hidden_size
									if "weight" in key:
												lowerCAmelCase_      			=			val[:dim, :]
												lowerCAmelCase_      			=			val[dim : dim * 2, :]
												lowerCAmelCase_      			=			val[-dim:, :]
									else:
												lowerCAmelCase_      			=			val[:dim]
												lowerCAmelCase_      			=			val[dim : dim * 2]
												lowerCAmelCase_      			=			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
									lowerCAmelCase_      			=			key.split("."       )
									lowerCAmelCase_      			=			int(key_split[3]       )
									lowerCAmelCase_      			=			config.text_config.hidden_size
									if "weight" in key:
												lowerCAmelCase_      			=			val[:dim, :]
												lowerCAmelCase_      			=			val[
												    dim : dim * 2, :
												]
												lowerCAmelCase_      			=			val[-dim:, :]
									else:
												lowerCAmelCase_      			=			val[:dim]
												lowerCAmelCase_      			=			val[dim : dim * 2]
												lowerCAmelCase_      			=			val[-dim:]
						else:
									lowerCAmelCase_      			=			rename_key(__lowerCAmelCase       )
									# 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
									):
												lowerCAmelCase_      			=			val.squeeze_()
									else:
												lowerCAmelCase_      			=			val

			return orig_state_dict
def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowerCAmelCase_      			=			Image.open(requests.get(__lowerCAmelCase					,							stream=__lowerCAmelCase       ).raw       )
			return im
@torch.no_grad()
def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						Optional[Any]					,							__lowerCAmelCase						:						Optional[Any]="groupvit-gcc-yfcc"					,							__lowerCAmelCase						:						int=False       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			GroupViTConfig()
			lowerCAmelCase_      			=			GroupViTModel(__lowerCAmelCase       ).eval()

			lowerCAmelCase_      			=			torch.load(__lowerCAmelCase					,							map_location="cpu"       )["model"]
			lowerCAmelCase_      			=			convert_state_dict(__lowerCAmelCase					,							__lowerCAmelCase       )
			lowerCAmelCase_			,						lowerCAmelCase_      			=			model.load_state_dict(__lowerCAmelCase					,							strict=__lowerCAmelCase       )
			assert missing_keys == ["text_model.embeddings.position_ids"]
			assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__lowerCAmelCase       ) == 0)

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

			with torch.no_grad():
						lowerCAmelCase_      			=			model(**__lowerCAmelCase       )

			if model_name == "groupvit-gcc-yfcc":
						lowerCAmelCase_      			=			torch.tensor([[13.3_523, 6.3_629]]       )
			elif model_name == "groupvit-gcc-redcaps":
						lowerCAmelCase_      			=			torch.tensor([[16.1_873, 8.6_230]]       )
			else:
						raise ValueError(F"""Model name {model_name} not supported."""       )
			assert torch.allclose(outputs.logits_per_image					,							__lowerCAmelCase					,							atol=1e-3       )

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

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


if __name__ == "__main__":
			_A     =  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     =  parser.parse_args()

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





from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_A     =  datasets.load_iris()

_A     =  np.array(data["data"])
_A     =  np.array(data["target"])
_A     =  data["target_names"]

_A,   _A,   _A,   _A     =  train_test_split(X, y)






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			return np.linalg.norm(np.array(__lowerCAmelCase       ) - np.array(__lowerCAmelCase       )       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						str					,							__lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						Any=5       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			zip(__lowerCAmelCase					,							__lowerCAmelCase       )
			# List of distances of all points from the point to be classified
			lowerCAmelCase_      			=			[]
			for data_point in data:
						lowerCAmelCase_      			=			euclidean_distance(data_point[0]					,							__lowerCAmelCase       )
						distances.append((distance, data_point[1])       )
			# Choosing 'k' points with the least distances.
			lowerCAmelCase_      			=			[i[1] for i in sorted(__lowerCAmelCase       )[:k]]
			# Most commonly occurring class among them
			# is the class into which the point is classified
			lowerCAmelCase_      			=			Counter(__lowerCAmelCase       ).most_common(1       )[0][0]
			return classes[result]


if __name__ == "__main__":
			print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



