
import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


SCREAMING_SNAKE_CASE				:str									=							logging.getLogger(__name__)


def 		UpperCAmelCase     (      a_						,  a_		)						->      Optional[Any]:







				"""simple docstring"""



				if os.path.exists(lowerCamelCase_		):
								if os.path.exists(os.path.join(lowerCamelCase_						,  "config.json"		)		) and os.path.isfile(
								    os.path.join(lowerCamelCase_						,  "config.json"		)		):
												os.remove(os.path.join(lowerCamelCase_						,  "config.json"		)		)
								if os.path.exists(os.path.join(lowerCamelCase_						,  "pytorch_model.bin"		)		) and os.path.isfile(
								    os.path.join(lowerCamelCase_						,  "pytorch_model.bin"		)		):
												os.remove(os.path.join(lowerCamelCase_						,  "pytorch_model.bin"		)		)
				else:
								os.makedirs(lowerCamelCase_		)
				model.save_pretrained(lowerCamelCase_		)


def 		UpperCAmelCase     (      a_						,  a_=False		)						->      str:







				"""simple docstring"""



				__A       =					2
				if unlogit:
								__A       =					torch.pow(lowerCamelCase_						,  lowerCamelCase_		)
				__A       =					p * torch.log(lowerCamelCase_		)
				__A       =					0
				return -plogp.sum(dim=-1		)


def 		UpperCAmelCase     (      a_		)						->      Dict:







				"""simple docstring"""



				logger.info("lv, h >\t" + "\t".join(F'''{x + 1}''' for x in range(len(lowerCamelCase_		)		)		)		)
				for row in range(len(lowerCamelCase_		)		):
								if tensor.dtype != torch.long:
												logger.info(F'''layer {row + 1}:\t''' + "\t".join(F'''{x:.5f}''' for x in tensor[row].cpu().data		)		)
								else:
												logger.info(F'''layer {row + 1}:\t''' + "\t".join(F'''{x:d}''' for x in tensor[row].cpu().data		)		)


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_=True						,  a_=True						,  a_=None						,  a_=False		)						->      int:







				"""simple docstring"""



				__A       =					model.config.num_hidden_layers, model.config.num_attention_heads
				__A       =					torch.zeros(lowerCamelCase_						,  lowerCamelCase_		).to(args.device		)
				__A       =					torch.zeros(lowerCamelCase_						,  lowerCamelCase_		).to(args.device		)

				if head_mask is None:
								__A       =					torch.ones(lowerCamelCase_						,  lowerCamelCase_		).to(args.device		)

				head_mask.requires_grad_(requires_grad=lowerCamelCase_		)
				# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
				if actually_pruned:
								__A       =					None

				__A       =					0.0
				__A       =					0.0
				for step, inputs in enumerate(tqdm(lowerCamelCase_						,  desc="Iteration"						,  disable=args.local_rank not in [-1, 0]		)		):
								__A       =					tuple(t.to(args.device		) for t in inputs		)
								(__A							)       =					inputs

								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
								__A       =					model(lowerCamelCase_						,  labels=lowerCamelCase_						,  head_mask=lowerCamelCase_		)
								#  (loss), lm_logits, presents, (all hidden_states), (attentions)
								__A       =					(
								    outputs[0],
								    outputs[1],
								    outputs[-1],
								)  # Loss and logits are the first, attention the last
								loss.backward()  # Backpropagate to populate the gradients in the head mask
								total_loss += loss.detach().cpu().numpy()
								if compute_entropy:
												for layer, attn in enumerate(lowerCamelCase_		):
																__A       =					entropy(attn.detach()						,  lowerCamelCase_		)
																attn_entropy[layer] += masked_entropy.sum(-1		).sum(0		).sum(0		).detach()

								if compute_importance:
												head_importance += head_mask.grad.abs().detach()
								tot_tokens += torch.ones_like(lowerCamelCase_		).float().detach().sum().data

				# Normalize
				attn_entropy /= tot_tokens
				head_importance /= tot_tokens
				# Layerwise importance normalization
				if not args.dont_normalize_importance_by_layer:
								__A       =					2
								__A       =					torch.pow(torch.pow(lowerCamelCase_						,  lowerCamelCase_		).sum(-1		)						,  1 / exponent		)
								head_importance /= norm_by_layer.unsqueeze(-1		) + 1E-20

				if not args.dont_normalize_global_importance:
								__A       =					(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

				# Print matrices
				if compute_entropy:
								logger.info("Attention entropies"		)
								print_ad_tensor(lowerCamelCase_		)
				if compute_importance:
								logger.info("Head importance scores"		)
								print_ad_tensor(lowerCamelCase_		)
				logger.info("Head ranked by importance scores"		)
				__A       =					torch.zeros(head_importance.numel()						,  dtype=torch.long						,  device=args.device		)
				__A       =					torch.arange(
				    head_importance.numel()						,  device=args.device		)
				__A       =					head_ranks.view_as(lowerCamelCase_		)
				print_ad_tensor(lowerCamelCase_		)
				return attn_entropy, head_importance, total_loss


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Dict:







				"""simple docstring"""



				__A       =					compute_heads_importance(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  compute_entropy=lowerCamelCase_		)
				__A       =					1 / loss  # instead of downsteam score use the LM loss
				logger.info("Pruning: original score: %f, threshold: %f"						,  lowerCamelCase_						,  original_score * args.masking_threshold		)

				__A       =					torch.ones_like(lowerCamelCase_		)
				__A       =					max(1						,  int(new_head_mask.numel() * args.masking_amount		)		)

				__A       =					original_score
				while current_score >= original_score * args.masking_threshold:
								__A       =					new_head_mask.clone().detach()  # save current head mask
								# heads from least important to most - keep only not-masked heads
								__A       =					float("Inf"		)
								__A       =					head_importance.view(-1		).sort()[1]

								if len(lowerCamelCase_		) <= num_to_mask:
												print("BREAK BY num_to_mask"		)
												break

								# mask heads
								__A       =					current_heads_to_mask[:num_to_mask]
								logger.info("Heads to mask: %s"						,  str(current_heads_to_mask.tolist()		)		)
								__A       =					new_head_mask.view(-1		)
								__A       =					0.0
								__A       =					new_head_mask.view_as(lowerCamelCase_		)
								__A       =					new_head_mask.clone().detach()
								print_ad_tensor(lowerCamelCase_		)

								# Compute metric and head importance again
								__A       =					compute_heads_importance(
								    lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  compute_entropy=lowerCamelCase_						,  head_mask=lowerCamelCase_		)
								__A       =					1 / loss
								logger.info(
								    "Masking: current score: %f, remaining heads %d (%.1f percents)"						,  lowerCamelCase_						,  new_head_mask.sum()						,  new_head_mask.sum() / new_head_mask.numel() * 1_0_0						,  )

				logger.info("Final head mask"		)
				print_ad_tensor(lowerCamelCase_		)
				np.save(os.path.join(args.output_dir						,  "head_mask.npy"		)						,  head_mask.detach().cpu().numpy()		)

				return head_mask


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_		)						->      str:







				"""simple docstring"""



				__A       =					datetime.now()
				__A       =					compute_heads_importance(
				    lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  compute_entropy=lowerCamelCase_						,  compute_importance=lowerCamelCase_						,  head_mask=lowerCamelCase_		)
				__A       =					1 / loss
				__A       =					datetime.now() - before_time

				__A       =					sum(p.numel() for p in model.parameters()		)
				__A       =					{
				    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCamelCase_		)		)
				}

				for k, v in heads_to_prune.items():
								if isinstance(lowerCamelCase_						,  lowerCamelCase_		):
												__A       =					[
												    v,
												]

				assert sum(len(lowerCamelCase_		) for h in heads_to_prune.values()		) == (1 - head_mask.long()).sum().item()
				model.prune_heads(lowerCamelCase_		)
				__A       =					sum(p.numel() for p in model.parameters()		)

				__A       =					datetime.now()
				__A       =					compute_heads_importance(
				    lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  compute_entropy=lowerCamelCase_						,  compute_importance=lowerCamelCase_						,  head_mask=lowerCamelCase_						,  actually_pruned=lowerCamelCase_						,  )

				__A       =					1 / loss
				__A       =					datetime.now() - before_time

				logger.info(
				    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"						,  lowerCamelCase_						,  lowerCamelCase_						,  pruned_num_params / original_num_params * 1_0_0						,  )
				logger.info("Pruning: score with masking: %f score with pruning: %f"						,  lowerCamelCase_						,  lowerCamelCase_		)
				logger.info("Pruning: speed ratio (original timing / new timing): %f percents"						,  original_time / new_time * 1_0_0		)
				save_model(lowerCamelCase_						,  args.output_dir		)


def 		UpperCAmelCase     (      )						->      List[str]:







				"""simple docstring"""



				__A       =					argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--data_dir"						,  default=lowerCamelCase_						,  type=lowerCamelCase_						,  required=lowerCamelCase_						,  help="The input data dir. Should contain the .tsv files (or other data files) for the task."						,  )
				parser.add_argument(
				    "--model_name_or_path"						,  default=lowerCamelCase_						,  type=lowerCamelCase_						,  required=lowerCamelCase_						,  help="Path to pretrained model or model identifier from huggingface.co/models"						,  )
				parser.add_argument(
				    "--output_dir"						,  default=lowerCamelCase_						,  type=lowerCamelCase_						,  required=lowerCamelCase_						,  help="The output directory where the model predictions and checkpoints will be written."						,  )

				# Other parameters
				parser.add_argument(
				    "--config_name"						,  default=""						,  type=lowerCamelCase_						,  help="Pretrained config name or path if not the same as model_name_or_path"						,  )
				parser.add_argument(
				    "--tokenizer_name"						,  default=""						,  type=lowerCamelCase_						,  help="Pretrained tokenizer name or path if not the same as model_name_or_path"						,  )
				parser.add_argument(
				    "--cache_dir"						,  default=lowerCamelCase_						,  type=lowerCamelCase_						,  help="Where do you want to store the pre-trained models downloaded from s3"						,  )
				parser.add_argument(
				    "--data_subset"						,  type=lowerCamelCase_						,  default=-1						,  help="If > 0: limit the data to a subset of data_subset instances."		)
				parser.add_argument(
				    "--overwrite_output_dir"						,  action="store_true"						,  help="Whether to overwrite data in output directory"		)
				parser.add_argument(
				    "--overwrite_cache"						,  action="store_true"						,  help="Overwrite the cached training and evaluation sets"		)

				parser.add_argument(
				    "--dont_normalize_importance_by_layer"						,  action="store_true"						,  help="Don\'t normalize importance score by layers"		)
				parser.add_argument(
				    "--dont_normalize_global_importance"						,  action="store_true"						,  help="Don\'t normalize all importance scores between 0 and 1"						,  )

				parser.add_argument(
				    "--try_masking"						,  action="store_true"						,  help="Whether to try to mask head until a threshold of accuracy."		)
				parser.add_argument(
				    "--masking_threshold"						,  default=0.9						,  type=lowerCamelCase_						,  help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."						,  )
				parser.add_argument(
				    "--masking_amount"						,  default=0.1						,  type=lowerCamelCase_						,  help="Amount to heads to masking at each masking step."		)
				parser.add_argument("--metric_name"						,  default="acc"						,  type=lowerCamelCase_						,  help="Metric to use for head masking."		)

				parser.add_argument(
				    "--max_seq_length"						,  default=1_2_8						,  type=lowerCamelCase_						,  help=(
				        "The maximum total input sequence length after WordPiece tokenization. \n"
				        "Sequences longer than this will be truncated, sequences shorter padded."
				    )						,  )
				parser.add_argument("--batch_size"						,  default=1						,  type=lowerCamelCase_						,  help="Batch size."		)

				parser.add_argument("--seed"						,  type=lowerCamelCase_						,  default=4_2		)
				parser.add_argument("--local_rank"						,  type=lowerCamelCase_						,  default=-1						,  help="local_rank for distributed training on gpus"		)
				parser.add_argument("--no_cuda"						,  action="store_true"						,  help="Whether not to use CUDA when available"		)
				parser.add_argument("--server_ip"						,  type=lowerCamelCase_						,  default=""						,  help="Can be used for distant debugging."		)
				parser.add_argument("--server_port"						,  type=lowerCamelCase_						,  default=""						,  help="Can be used for distant debugging."		)
				__A       =					parser.parse_args()

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print("Waiting for debugger attach"		)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port)						,  redirect_output=lowerCamelCase_		)
								ptvsd.wait_for_attach()

				# Setup devices and distributed training
				if args.local_rank == -1 or args.no_cuda:
								__A       =					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
								__A       =					0 if args.no_cuda else torch.cuda.device_count()
				else:
								torch.cuda.set_device(args.local_rank		)
								__A       =					torch.device("cuda"						,  args.local_rank		)
								__A       =					1
								torch.distributed.init_process_group(backend="nccl"		)  # Initializes the distributed backend

				# Setup logging
				logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN		)
				logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device						,  args.n_gpu						,  bool(args.local_rank != -1		)		)		)

				__A       =					GPTaLMHeadModel.from_pretrained(args.model_name_or_path		)

				# Distributed and parallel training
				model.to(args.device		)
				if args.local_rank != -1:
								__A       =					nn.parallel.DistributedDataParallel(
								    lowerCamelCase_						,  device_ids=[args.local_rank]						,  output_device=args.local_rank						,  find_unused_parameters=lowerCamelCase_		)
				elif args.n_gpu > 1:
								__A       =					nn.DataParallel(lowerCamelCase_		)

				# Print/save training arguments
				os.makedirs(args.output_dir						,  exist_ok=lowerCamelCase_		)
				torch.save(lowerCamelCase_						,  os.path.join(args.output_dir						,  "run_args.bin"		)		)
				logger.info("Training/evaluation parameters %s"						,  lowerCamelCase_		)

				# Prepare dataset
				__A       =					np.concatenate(
				    [
				        np.loadtxt(args.data_dir						,  dtype=np.intaa		),
				    ]		)
				__A       =					(torch.from_numpy(lowerCamelCase_		),)
				__A       =					TensorDataset(*lowerCamelCase_		)
				__A       =					RandomSampler(lowerCamelCase_		)
				__A       =					DataLoader(lowerCamelCase_						,  sampler=lowerCamelCase_						,  batch_size=args.batch_size		)

				# Compute head entropy and importance score
				compute_heads_importance(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_		)

				# Try head masking (set heads to zero until the score goes under a threshole)
				# and head pruning (remove masked heads and see the effect on the network)
				if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
								__A       =					mask_heads(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_		)
								prune_heads(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_		)


if __name__ == "__main__":
					main()




import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				List[str] 						=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
    _lowercase			:				Tuple 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Any 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Dict 						=						True if 'large' in model_name or 'huge' in model_name else False

    if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
        if "fl3" in model_name:
            _lowercase			:				Any 						=						[3, 3, 3, 3]
            _lowercase			:				Any 						=						[5, 5, 5, 5]
        elif "fl4" in model_name:
            _lowercase			:				Dict 						=						[4, 4, 4, 4]
            _lowercase			:				Tuple 						=						[3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        _lowercase			:				str 						=						[3, 3, 3, 3]
        if "lrf" in model_name:
            _lowercase			:				Optional[int] 						=						[3, 3, 3, 3]
        else:
            _lowercase			:				Dict 						=						[2, 2, 2, 2]

    if "tiny" in model_name:
        _lowercase			:				List[str] 						=						96
    elif "small" in model_name:
        _lowercase			:				Dict 						=						96
    elif "base" in model_name:
        _lowercase			:				Optional[int] 						=						128
    elif "large" in model_name:
        _lowercase			:				List[Any] 						=						192
    elif "xlarge" in model_name:
        _lowercase			:				Optional[Any] 						=						256
    elif "huge" in model_name:
        _lowercase			:				Dict 						=						352

    # set label information
    _lowercase			:				int 						=						'huggingface/label-files'
    if "large" in model_name or "huge" in model_name:
        _lowercase			:				str 						=						'imagenet-22k-id2label.json'
    else:
        _lowercase			:				Tuple 						=						'imagenet-1k-id2label.json'

    _lowercase			:				Union[str, Any] 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
    _lowercase			:				int 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
    _lowercase			:				Any 						=						{v: k for k, v in idalabel.items()}

    _lowercase			:				Optional[Any] 						=						FocalNetConfig(
        embed_dim=lowerCamelCase_				,					depths=lowerCamelCase_				,					focal_levels=lowerCamelCase_				,					focal_windows=lowerCamelCase_				,					use_conv_embed=lowerCamelCase_				,					idalabel=lowerCamelCase_				,					labelaid=lowerCamelCase_				,					use_post_layernorm=lowerCamelCase_				,					use_layerscale=lowerCamelCase_				,					)

    return config



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    if "patch_embed.proj" in name:
        _lowercase			:				Optional[Any] 						=						name.replace('patch_embed.proj'				,					'embeddings.patch_embeddings.projection'					)
    if "patch_embed.norm" in name:
        _lowercase			:				str 						=						name.replace('patch_embed.norm'				,					'embeddings.norm'					)
    if "layers" in name:
        _lowercase			:				Any 						=						'encoder.' + name
    if "encoder.layers" in name:
        _lowercase			:				int 						=						name.replace('encoder.layers'				,					'encoder.stages'					)
    if "downsample.proj" in name:
        _lowercase			:				Tuple 						=						name.replace('downsample.proj'				,					'downsample.projection'					)
    if "blocks" in name:
        _lowercase			:				str 						=						name.replace('blocks'				,					'layers'					)
    if "modulation.f.weight" in name or "modulation.f.bias" in name:
        _lowercase			:				List[str] 						=						name.replace('modulation.f'				,					'modulation.projection_in'					)
    if "modulation.h.weight" in name or "modulation.h.bias" in name:
        _lowercase			:				int 						=						name.replace('modulation.h'				,					'modulation.projection_context'					)
    if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
        _lowercase			:				Any 						=						name.replace('modulation.proj'				,					'modulation.projection_out'					)

    if name == "norm.weight":
        _lowercase			:				Any 						=						'layernorm.weight'
    if name == "norm.bias":
        _lowercase			:				Tuple 						=						'layernorm.bias'

    if "head" in name:
        _lowercase			:				Optional[int] 						=						name.replace('head'				,					'classifier'					)
    else:
        _lowercase			:				Optional[int] 						=						'focalnet.' + name

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    # fmt: off
    _lowercase			:				Dict 						=						{
        'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
        'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
        'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
        'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
        'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
        'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
        'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
        'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
        'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
        'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
    }
    # fmt: on

    _lowercase			:				Dict 						=						model_name_to_url[model_name]
    print('Checkpoint URL: '				,					lowerCamelCase_					)
    _lowercase			:				List[str] 						=						torch.hub.load_state_dict_from_url(lowerCamelCase_				,					map_location='cpu'					)['model']

    # rename keys
    for key in state_dict.copy().keys():
        _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						val

    _lowercase			:				Union[str, Any] 						=						get_focalnet_config(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						FocalNetForImageClassification(lowerCamelCase_					)
    model.eval()

    # load state dict
    model.load_state_dict(lowerCamelCase_					)

    # verify conversion
    _lowercase			:				Optional[Any] 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'

    _lowercase			:				Any 						=						BitImageProcessor(
        do_resize=lowerCamelCase_				,					size={'shortest_edge': 256}				,					resample=PILImageResampling.BILINEAR				,					do_center_crop=lowerCamelCase_				,					crop_size=224				,					do_normalize=lowerCamelCase_				,					image_mean=lowerCamelCase_				,					image_std=lowerCamelCase_				,					)
    _lowercase			:				List[str] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)
    _lowercase			:				List[Any] 						=						processor(images=lowerCamelCase_				,					return_tensors='pt'					)

    _lowercase			:				str 						=						transforms.Compose(
        [
            transforms.Resize(256					),
            transforms.CenterCrop(224					),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,					std=[0.2_29, 0.2_24, 0.2_25]					),
        ]					)

    _lowercase			:				List[str] 						=						image_transforms(lowerCamelCase_					).unsqueeze(0					)

    # verify pixel_values
    assert torch.allclose(inputs.pixel_values				,					lowerCamelCase_				,					atol=1e-4					)

    _lowercase			:				Dict 						=						model(**lowerCamelCase_					)

    _lowercase			:				int 						=						outputs.logits.argmax(-1					).item()
    print('Predicted class:'				,					model.config.idalabel[predicted_class_idx]					)

    print('First values of logits:'				,					outputs.logits[0, :3]					)

    if model_name == "focalnet-tiny":
        _lowercase			:				Optional[Any] 						=						torch.tensor([0.21_66, -0.43_68, 0.21_91]					)
    elif model_name == "focalnet-tiny-lrf":
        _lowercase			:				int 						=						torch.tensor([1.16_69, 0.01_25, -0.16_95]					)
    elif model_name == "focalnet-small":
        _lowercase			:				str 						=						torch.tensor([0.49_17, -0.04_30, 0.13_41]					)
    elif model_name == "focalnet-small-lrf":
        _lowercase			:				Any 						=						torch.tensor([-0.25_88, -0.53_42, -0.23_31]					)
    elif model_name == "focalnet-base":
        _lowercase			:				List[Any] 						=						torch.tensor([-0.16_55, -0.40_90, -0.17_30]					)
    elif model_name == "focalnet-base-lrf":
        _lowercase			:				int 						=						torch.tensor([0.53_06, -0.04_83, -0.39_28]					)
    assert torch.allclose(outputs.logits[0, :3]				,					lowerCamelCase_				,					atol=1e-4					)
    print('Looks ok!'					)

    if pytorch_dump_folder_path is not None:
        print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}'''					)
        model.save_pretrained(lowerCamelCase_					)
        processor.save_pretrained(lowerCamelCase_					)

    if push_to_hub:
        print(F'''Pushing model and processor of {model_name} to the hub...'''					)
        model.push_to_hub(F'''{model_name}'''					)
        processor.push_to_hub(F'''{model_name}'''					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: int				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="focalnet-tiny",
     type=str,
     help="Name of the FocalNet 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."
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
     help="Whether to push the model and processor to the hub.",
 )

 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					parser.parse_args()
 convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


