





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							:			str         =			logging.get_logger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=False				)							-> int:




    """simple docstring"""




    _A     =							[]
    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"
        _A     =							[(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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=False				)							-> int:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    _A     =							['head.weight', 'head.bias']
    for k in ignore_keys:
        state_dict.pop(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> List[str]:




    """simple docstring"""




    _A     =							dct.pop(_SCREAMING_SNAKE_CASE				)
    _A     =							val





def 				__lowerCAmelCase(       )							-> str:




    """simple docstring"""




    _A     =							'http://images.cocodataset.org/val2017/000000039769.jpg'
    _A     =							Image.open(requests.get(_SCREAMING_SNAKE_CASE							,     stream=_SCREAMING_SNAKE_CASE				).raw				)
    return im





@torch.no_grad()
def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=True				)							-> Optional[Any]:




    """simple docstring"""




    _A     =							ViTConfig()
    # patch_size
    if model_name[-1] == "8":
        _A     =							8
    # set labels if required
    if not base_model:
        _A     =							1_000
        _A     =							'huggingface/label-files'
        _A     =							'imagenet-1k-id2label.json'
        _A     =							json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     repo_type='dataset'				)							,     'r'				)				)
        _A     =							{int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
        _A     =							idalabel
        _A     =							{v: k for k, v in idalabel.items()}
    # size of the architecture
    if model_name in ["dino_vits8", "dino_vits16"]:
        _A     =							384
        _A     =							1_536
        _A     =							12
        _A     =							6

    # load original model from torch hub
    _A     =							torch.hub.load('facebookresearch/dino:main'							,     _SCREAMING_SNAKE_CASE				)
    original_model.eval()

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

    # load HuggingFace model
    if base_model:
        _A     =							ViTModel(_SCREAMING_SNAKE_CASE							,     add_pooling_layer=_SCREAMING_SNAKE_CASE				).eval()
    else:
        _A     =							ViTForImageClassification(_SCREAMING_SNAKE_CASE				).eval()
    model.load_state_dict(_SCREAMING_SNAKE_CASE				)

    # Check outputs on an image, prepared by ViTImageProcessor
    _A     =							ViTImageProcessor()
    _A     =							image_processor(images=prepare_img()							,     return_tensors='pt'				)
    _A     =							encoding['pixel_values']
    _A     =							model(_SCREAMING_SNAKE_CASE				)

    if base_model:
        _A     =							original_model(_SCREAMING_SNAKE_CASE				)
        assert torch.allclose(_SCREAMING_SNAKE_CASE							,     outputs.last_hidden_state[:, 0, :]							,     atol=1e-1				)
    else:
        _A     =							original_model(_SCREAMING_SNAKE_CASE				)
        assert logits.shape == outputs.logits.shape
        assert torch.allclose(_SCREAMING_SNAKE_CASE							,     outputs.logits							,     atol=1e-3				)

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


if __name__ == "__main__":
 __A							:			List[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							:			List[Any]         =			parser.parse_args()
 convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler


@dataclass
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							0
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							3.0


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self.assertDictEqual(MockClass().to_kwargs()						,				{}     )
											self.assertDictEqual(MockClass(a=2     ).to_kwargs()						,				{'a': 2}     )
											self.assertDictEqual(MockClass(a=2						,				b=A_     ).to_kwargs()						,				{'a': 2, 'b': True}     )
											self.assertDictEqual(MockClass(a=2						,				c=2.25     ).to_kwargs()						,				{'a': 2, 'c': 2.25}     )







				@require_cuda
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     GradScalerKwargs(init_scale=1024						,				growth_factor=2     )
											AcceleratorState._reset_state()
											UpperCamelCase   			=     Accelerator(mixed_precision='fp16'						,				kwargs_handlers=[scaler_handler]     )
											print(accelerator.use_fpaa     )
											UpperCamelCase   			=     accelerator.scaler

											# Check the kwargs have been applied
											self.assertEqual(scaler._init_scale						,				1_024.0     )
											self.assertEqual(scaler._growth_factor						,				2.0     )

											# Check the other values are at the default
											self.assertEqual(scaler._backoff_factor						,				0.5     )
											self.assertEqual(scaler._growth_interval						,				2000     )
											self.assertEqual(scaler._enabled						,				A_     )







				@require_multi_gpu
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
											execute_subprocess_async(A_						,				env=os.environ.copy()     )


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						lowerCAmelCase					:    List[str]           = Accelerator(kwargs_handlers=[ddp_scaler])
						lowerCAmelCase					:    List[Any]           = torch.nn.Linear(1_00, 2_00)
						lowerCAmelCase					:    int           = accelerator.prepare(model)

						# Check the values changed in kwargs
						lowerCAmelCase					:    Dict           = ''
						lowerCAmelCase					:    Dict           = model.bucket_bytes_cap // (10_24 * 10_24)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




