






import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

_a      	=  logging.getLogger()


def __A    (      __lowerCAmelCase	)->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			{}
						_UpperCAmelCase									=			os.path.join(__lowerCAmelCase ,		'all_results.json'	)
						if os.path.exists(__lowerCAmelCase	):
												with open(__lowerCAmelCase ,		'r'	) as f:
																		_UpperCAmelCase									=			json.load(__lowerCAmelCase	)
						else:
												raise ValueError(F"""can't find {path}"""	)
						return results


_a      	=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





@require_torch_tpu
class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							import xla_spawn

							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			F"""
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

							with patch.object(UpperCAmelCase       ,    'argv'       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			time()
													xla_spawn.main()
													_UpperCAmelCase									=			time()

													_UpperCAmelCase									=			get_results(UpperCAmelCase    )
													self.assertGreaterEqual(result['eval_accuracy']       ,    0.75    )

													# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
													self.assertLess(end - start       ,    500    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							import xla_spawn

							_UpperCAmelCase									=			'\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
							with patch.object(UpperCAmelCase       ,    'argv'       ,    UpperCAmelCase    ):
													xla_spawn.main()








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      	=  logging.get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase=False	)->  Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase									=			[]
						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									=			[(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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=False	)->  List[str]:





						"""simple docstring"""



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


def __A    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			dct.pop(__lowerCAmelCase	)
						_UpperCAmelCase									=			val


def __A    (      )->  str:





						"""simple docstring"""



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


@torch.no_grad()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=True	)->  List[str]:





						"""simple docstring"""



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

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

						# load state_dict of original model, remove and rename some keys
						_UpperCAmelCase									=			original_model.state_dict()
						if base_model:
												remove_classification_head_(__lowerCAmelCase	)
						_UpperCAmelCase									=			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									=			ViTModel(__lowerCAmelCase ,		add_pooling_layer=__lowerCAmelCase	).eval()
						else:
												_UpperCAmelCase									=			ViTForImageClassification(__lowerCAmelCase	).eval()
						model.load_state_dict(__lowerCAmelCase	)

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

						if base_model:
												_UpperCAmelCase									=			original_model(__lowerCAmelCase	)
												assert torch.allclose(__lowerCAmelCase ,		outputs.last_hidden_state[:, 0, :] ,		atol=1E-1	)
						else:
												_UpperCAmelCase									=			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      	=  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      	=  parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
