
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()
_SCREAMING_SNAKE_CASE    :      str          =			logging.get_logger(__name__)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						Union[str, Any]=False       ) ->       List[Any]:

				"""simple docstring"""







				A__  :	Optional[int]         =							[]
				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"
								A__  :	Optional[int]         =							[(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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						Dict							,						__UpperCamelCase :						Union[str, Any]=False       ) ->       str:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Optional[int]       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(__UpperCamelCase							,						__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						List[Any]							,						__UpperCamelCase :						int       ) ->       Union[str, Any]:

				"""simple docstring"""







				A__  :	List[Any]         =							dct.pop(__UpperCamelCase       )
				A__  :	List[Any]         =							val

def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[Any]:

				"""simple docstring"""







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

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Any							,						__UpperCamelCase :						str       ) ->       Dict:

				"""simple docstring"""







				A__  :	List[str]         =							ViTConfig()
				A__  :	Union[str, Any]         =							False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								A__  :	Optional[int]         =							True
								A__  :	int         =							int(vit_name[-12:-10]       )
								A__  :	List[Any]         =							int(vit_name[-9:-6]       )
				else:
								A__  :	Any         =							10_00
								A__  :	str         =							'''huggingface/label-files'''
								A__  :	Optional[Any]         =							'''imagenet-1k-id2label.json'''
								A__  :	Any         =							json.load(open(hf_hub_download(__UpperCamelCase							,						__UpperCamelCase							,						repo_type='''dataset'''       )							,						'''r'''       )       )
								A__  :	Dict         =							{int(__UpperCamelCase       ): v for k, v in idalabel.items()}
								A__  :	int         =							idalabel
								A__  :	List[str]         =							{v: k for k, v in idalabel.items()}
								A__  :	List[str]         =							int(vit_name[-6:-4]       )
								A__  :	Any         =							int(vit_name[-3:]       )
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith('''tiny'''       ):
												A__  :	List[str]         =							1_92
												A__  :	Optional[Any]         =							7_68
												A__  :	Optional[int]         =							12
												A__  :	Tuple         =							3
								elif vit_name[9:].startswith('''small'''       ):
												A__  :	int         =							3_84
												A__  :	Union[str, Any]         =							15_36
												A__  :	Union[str, Any]         =							12
												A__  :	List[str]         =							6
								else:
												pass
				else:
								if vit_name[4:].startswith('''small'''       ):
												A__  :	List[Any]         =							7_68
												A__  :	Tuple         =							23_04
												A__  :	List[str]         =							8
												A__  :	Union[str, Any]         =							8
								elif vit_name[4:].startswith('''base'''       ):
												pass
								elif vit_name[4:].startswith('''large'''       ):
												A__  :	Optional[int]         =							10_24
												A__  :	List[Any]         =							40_96
												A__  :	List[str]         =							24
												A__  :	Optional[Any]         =							16
								elif vit_name[4:].startswith('''huge'''       ):
												A__  :	Optional[Any]         =							12_80
												A__  :	Dict         =							51_20
												A__  :	Dict         =							32
												A__  :	Optional[Any]         =							16

    # load original model from timm
				A__  :	Optional[int]         =							timm.create_model(__UpperCamelCase							,						pretrained=__UpperCamelCase       )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				A__  :	Optional[int]         =							timm_model.state_dict()
				if base_model:
								remove_classification_head_(__UpperCamelCase       )
				A__  :	List[Any]         =							create_rename_keys(__UpperCamelCase							,						__UpperCamelCase       )
				for src, dest in rename_keys:
								rename_key(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )
				read_in_q_k_v(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								A__  :	Optional[Any]         =							ViTModel(__UpperCamelCase       ).eval()
				else:
								A__  :	List[str]         =							ViTForImageClassification(__UpperCamelCase       ).eval()
				model.load_state_dict(__UpperCamelCase       )

				# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
				if "deit" in vit_name:
								A__  :	Optional[Any]         =							DeiTImageProcessor(size=config.image_size       )
				else:
								A__  :	int         =							ViTImageProcessor(size=config.image_size       )
				A__  :	List[str]         =							image_processor(images=prepare_img()							,						return_tensors='''pt'''       )
				A__  :	Dict         =							encoding['''pixel_values''']
				A__  :	List[str]         =							model(__UpperCamelCase       )

				if base_model:
								A__  :	List[Any]         =							timm_model.forward_features(__UpperCamelCase       )
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(__UpperCamelCase							,						outputs.pooler_output							,						atol=1e-3       )
				else:
								A__  :	Optional[int]         =							timm_model(__UpperCamelCase       )
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(__UpperCamelCase							,						outputs.logits							,						atol=1e-3       )

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


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      Tuple          =			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.'
					)

					_SCREAMING_SNAKE_CASE    :      Any          =			parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment







@require_huggingface_suite
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Dict         =							inspect.getfile(accelerate.test_utils		)
					A__  :	Any         =							os.path.sep.join(
					    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']		)

					from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

					A__  :	Tuple         =							test_metrics





	@require_cpu
	def   __snake_case				(			self		):
					debug_launcher(self.test_metrics.main				,       num_processes=1		)





	@require_cpu
	def   __snake_case				(			self		):
					debug_launcher(self.test_metrics.main		)





	@require_single_gpu
	def   __snake_case				(			self		):
					self.test_metrics.main()





	@require_multi_gpu
	def   __snake_case				(			self		):
					print(F"Found {torch.cuda.device_count()} devices."		)
					A__  :	int         =							['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
					with patch_environment(omp_num_threads=1		):
									execute_subprocess_async(UpperCamelCase__				,       env=os.environ.copy()		)