'''simple docstring'''




import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
_a   :	Tuple 							=   logging.get_logger(__name__)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__=False						)					->       Any:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		[]
       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"),
                  ]						)

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              __UpperCAmelCase  :				Tuple       				=		[(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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=False						)					->       str:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				List[str]       				=		["head.weight", "head.bias"]
       for k in ignore_keys:
              state_dict.pop(lowerCamelCase__							,    lowerCamelCase__						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		dct.pop(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		val

def _lowercase	(      )					->       Tuple:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		"http://images.cocodataset.org/val2017/000000039769.jpg"
       __UpperCAmelCase  :				int       				=		Image.open(requests.get(lowerCamelCase__							,    stream=lowerCamelCase__						).raw						)
       return im

@torch.no_grad()
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=True						)					->       str:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		ViTConfig()
       # patch_size
       if model_name[-1] == "8":
              __UpperCAmelCase  :				Union[str, Any]       				=		8
       # set labels if required
       if not base_model:
              __UpperCAmelCase  :				str       				=		1000
              __UpperCAmelCase  :				str       				=		"huggingface/label-files"
              __UpperCAmelCase  :				str       				=		"imagenet-1k-id2label.json"
              __UpperCAmelCase  :				Optional[int]       				=		json.load(open(hf_hub_download(lowerCamelCase__							,    lowerCamelCase__							,    repo_type="dataset"						)							,    "r"						)						)
              __UpperCAmelCase  :				Tuple       				=		{int(lowerCamelCase__						): v for k, v in idalabel.items()}
              __UpperCAmelCase  :				str       				=		idalabel
              __UpperCAmelCase  :				Tuple       				=		{v: k for k, v in idalabel.items()}
       # size of the architecture
       if model_name in ["dino_vits8", "dino_vits16"]:
              __UpperCAmelCase  :				str       				=		384
              __UpperCAmelCase  :				Optional[int]       				=		1536
              __UpperCAmelCase  :				Union[str, Any]       				=		12
              __UpperCAmelCase  :				int       				=		6

       # load original model from torch hub
       __UpperCAmelCase  :				List[str]       				=		torch.hub.load("facebookresearch/dino:main"							,    lowerCamelCase__						)
       original_model.eval()

       # load state_dict of original model, remove and rename some keys
       __UpperCAmelCase  :				Tuple       				=		original_model.state_dict()
       if base_model:
              remove_classification_head_(lowerCamelCase__						)
       __UpperCAmelCase  :				Union[str, Any]       				=		create_rename_keys(lowerCamelCase__							,    base_model=lowerCamelCase__						)
       for src, dest in rename_keys:
              rename_key(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)
       read_in_q_k_v(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)

       # load HuggingFace model
       if base_model:
              __UpperCAmelCase  :				List[str]       				=		ViTModel(lowerCamelCase__							,    add_pooling_layer=lowerCamelCase__						).eval()
       else:
              __UpperCAmelCase  :				str       				=		ViTForImageClassification(lowerCamelCase__						).eval()
       model.load_state_dict(lowerCamelCase__						)

       # Check outputs on an image, prepared by ViTImageProcessor
       __UpperCAmelCase  :				List[str]       				=		ViTImageProcessor()
       __UpperCAmelCase  :				Dict       				=		image_processor(images=prepare_img()							,    return_tensors="pt"						)
       __UpperCAmelCase  :				Dict       				=		encoding["pixel_values"]
       __UpperCAmelCase  :				str       				=		model(lowerCamelCase__						)

       if base_model:
              __UpperCAmelCase  :				List[Any]       				=		original_model(lowerCamelCase__						)
              assert torch.allclose(lowerCamelCase__							,    outputs.last_hidden_state[:, 0, :]							,    atol=1e-1						)
       else:
              __UpperCAmelCase  :				int       				=		original_model(lowerCamelCase__						)
              assert logits.shape == outputs.logits.shape
              assert torch.allclose(lowerCamelCase__							,    outputs.logits							,    atol=1e-3						)

       Path(lowerCamelCase__						).mkdir(exist_ok=lowerCamelCase__						)
       print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""						)
       model.save_pretrained(lowerCamelCase__						)
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
       image_processor.save_pretrained(lowerCamelCase__						)


if __name__ == "__main__":
   _a   :	Optional[Any] 							=   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="dino_vitb16",
       type=str,
       help="Name of the model trained with DINO you'd like to convert.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
   )
   parser.add_argument(
       "--base_model",
       action="store_true",
       help="Whether to only convert the base model (no projection head weights).",
   )

   parser.set_defaults(base_model=True)
   _a   :	str 							=   parser.parse_args()
   convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







'''simple docstring'''




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       list[float]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase  :				str       				=		f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if colsa != 1:
              __UpperCAmelCase  :				Optional[Any]       				=		f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if rowsa != rowsa:
              __UpperCAmelCase  :				Optional[int]       				=		(
                  "Coefficient and constant matrices dimensions must be nxn and nx1 but "
                  f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if len(lowerCamelCase__						) != rowsa:
              __UpperCAmelCase  :				List[str]       				=		(
                  "Number of initial values must be equal to number of rows in coefficient "
                  f"""matrix but received {len(lowerCamelCase__						)} and {rowsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if iterations <= 0:
              raise ValueError("Iterations must be at least 1"						)

       __UpperCAmelCase  :				NDArray[floataa]       				=		np.concatenate(
           (coefficient_matrix, constant_matrix)							,    axis=1						)

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		table.shape

       strictly_diagonally_dominant(lowerCamelCase__						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				int       				=		[]
              for row in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				List[str]       				=		0
                     for col in range(lowerCamelCase__						):
                            if col == row:
                                   __UpperCAmelCase  :				int       				=		table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase  :				Any       				=		table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase  :				List[Any]       				=		(temp + val) / denom
                     new_val.append(lowerCamelCase__						)
              __UpperCAmelCase  :				str       				=		new_val

       return [float(lowerCamelCase__						) for i in new_val]

def _lowercase	(      lowerCamelCase__						)					->       bool:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		table.shape

       __UpperCAmelCase  :				str       				=		True

       for i in range(0							,    lowerCamelCase__						):
              __UpperCAmelCase  :				Union[str, Any]       				=		0
              for j in range(0							,    cols - 1						):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("Coefficient matrix is not strictly diagonally dominant"						)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






