
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case      :							int     	=    logging.get_logger(__name__)

def  __snake_case							(	__magic_name__		,    __magic_name__=False						):







				'''simple docstring'''


				lowercase         =      []
				for i in range(config.num_hidden_layers						):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')						)
								rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')						)
								rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')						)
								rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')						)
								rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')						)
								rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')						)
								rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')						)
								rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')						)
								rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')						)
								rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')						)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("cls_token", "vit.embeddings.cls_token"),
				        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
				        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
				        ("pos_embed", "vit.embeddings.position_embeddings"),
				    ]						)

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								        ("pre_logits.fc.weight", "pooler.dense.weight"),
								        ("pre_logits.fc.bias", "pooler.dense.bias"),
								    ]						)

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								lowercase         =      [(pair[0], pair[1][4:]) if pair[1].startswith("vit"						) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("norm.weight", "vit.layernorm.weight"),
								        ("norm.bias", "vit.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]						)

				return rename_keys

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__=False						):







				'''simple docstring'''


				for i in range(config.num_hidden_layers						):
								if base_model:
												lowercase         =      ""
								else:
												lowercase         =      "vit."
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								lowercase         =      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''						)
								lowercase         =      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''						)
								# next, add query, keys and values (in that order) to the state dict
								lowercase         =      in_proj_weight[
								    : config.hidden_size, :
								]
								lowercase         =      in_proj_bias[: config.hidden_size]
								lowercase         =      in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowercase         =      in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowercase         =      in_proj_weight[
								    -config.hidden_size :, :
								]
								lowercase         =      in_proj_bias[-config.hidden_size :]

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				lowercase         =      ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(__magic_name__		,    __magic_name__						)

def  __snake_case							(	__magic_name__		,    __magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      dct.pop(__magic_name__						)
				lowercase         =      val

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      "http://images.cocodataset.org/val2017/000000039769.jpg"
				lowercase         =      Image.open(requests.get(__magic_name__		,    stream=__magic_name__						).raw						)
				return im

@torch.no_grad()
def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				lowercase         =      ViTConfig()
				lowercase         =      False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								lowercase         =      True
								lowercase         =      int(vit_name[-12:-10]						)
								lowercase         =      int(vit_name[-9:-6]						)
				else:
								lowercase         =      1000
								lowercase         =      "huggingface/label-files"
								lowercase         =      "imagenet-1k-id2label.json"
								lowercase         =      json.load(open(hf_hub_download(__magic_name__		,    __magic_name__		,    repo_type="dataset"						)		,    "r"						)						)
								lowercase         =      {int(__magic_name__						): v for k, v in idalabel.items()}
								lowercase         =      idalabel
								lowercase         =      {v: k for k, v in idalabel.items()}
								lowercase         =      int(vit_name[-6:-4]						)
								lowercase         =      int(vit_name[-3:]						)
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith("tiny"						):
												lowercase         =      192
												lowercase         =      768
												lowercase         =      12
												lowercase         =      3
								elif vit_name[9:].startswith("small"						):
												lowercase         =      384
												lowercase         =      1536
												lowercase         =      12
												lowercase         =      6
								else:
												pass
				else:
								if vit_name[4:].startswith("small"						):
												lowercase         =      768
												lowercase         =      2304
												lowercase         =      8
												lowercase         =      8
								elif vit_name[4:].startswith("base"						):
												pass
								elif vit_name[4:].startswith("large"						):
												lowercase         =      1024
												lowercase         =      4096
												lowercase         =      24
												lowercase         =      16
								elif vit_name[4:].startswith("huge"						):
												lowercase         =      1280
												lowercase         =      5120
												lowercase         =      32
												lowercase         =      16

    # load original model from timm
				lowercase         =      timm.create_model(__magic_name__		,    pretrained=__magic_name__						)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				lowercase         =      timm_model.state_dict()
				if base_model:
								remove_classification_head_(__magic_name__						)
				lowercase         =      create_rename_keys(__magic_name__		,    __magic_name__						)
				for src, dest in rename_keys:
								rename_key(__magic_name__		,    __magic_name__		,    __magic_name__						)
				read_in_q_k_v(__magic_name__		,    __magic_name__		,    __magic_name__						)

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								lowercase         =      ViTModel(__magic_name__						).eval()
				else:
								lowercase         =      ViTForImageClassification(__magic_name__						).eval()
				model.load_state_dict(__magic_name__						)

				# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
				if "deit" in vit_name:
								lowercase         =      DeiTImageProcessor(size=config.image_size						)
				else:
								lowercase         =      ViTImageProcessor(size=config.image_size						)
				lowercase         =      image_processor(images=prepare_img()		,    return_tensors="pt"						)
				lowercase         =      encoding["pixel_values"]
				lowercase         =      model(__magic_name__						)

				if base_model:
								lowercase         =      timm_model.forward_features(__magic_name__						)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(__magic_name__		,    outputs.pooler_output		,    atol=1e-3						)
				else:
								lowercase         =      timm_model(__magic_name__						)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(__magic_name__		,    outputs.logits		,    atol=1e-3						)

				Path(__magic_name__						).mkdir(exist_ok=__magic_name__						)
				print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''						)
				model.save_pretrained(__magic_name__						)
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
				image_processor.save_pretrained(__magic_name__						)


if __name__ == "__main__":
						_snake_case      :							str     	=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--vit_name",
						    default="vit_base_patch16_224",
						    type=str,
						    help="Name of the ViT timm model you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)

						_snake_case      :							List[Any]     	=    parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)


# Function to print upper half of diamond (pyramid)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(0		,    __magic_name__						):
								for _ in range(0		,    n - i - 1						):  # printing spaces
												print(" "		,    end=""						)
								for _ in range(0		,    i + 1						):  # printing stars
												print("* "		,    end=""						)
								print()

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(__magic_name__		,    0		,    -1						):
								for _ in range(__magic_name__		,    0		,    -1						):  # printing stars
												print("* "		,    end=""						)
								print()
								for _ in range(n - i + 1		,    0		,    -1						):  # printing spaces
												print(" "		,    end=""						)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				if n <= 0:
								print("       ...       ....        nothing printing :("						)
								return
				floyd(__magic_name__						)  # upper half
				reverse_floyd(__magic_name__						)  # lower half


if __name__ == "__main__":
						print(r"| /\ | |- |  |-  |--| |\  /| |-")
						print(r"|/  \| |- |_ |_  |__| | \/ | |_")
						_snake_case      :							Union[str, Any]     	=    1
						while K:
												_snake_case      :							Dict     	=    int(input("enter the number and , and see the magic : "))
												print()
												pretty_print(user_number)
												_snake_case      :							Tuple     	=    int(input("press 0 to exit... and 1 to continue..."))

						print("Good Bye...")
