

import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


SCREAMING_SNAKE_CASE_            = logging.getLogger(__name__)

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "token-classification"


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										if type(A_		) == dict:
														_UpperCAmelCase    :						Union[str, Any]							=      Namespace(**A_		)
										_UpperCAmelCase    :						int							=      import_module("tasks"		)
										try:
														_UpperCAmelCase    :						Optional[Any]							=      getattr(A_  ,   hparams.task_type		)
														_UpperCAmelCase    :						TokenClassificationTask							=      token_classification_task_clazz()
										except AttributeError:
														raise ValueError(
														    f'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
														    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'		)
										_UpperCAmelCase    :						Optional[int]							=      self.token_classification_task.get_labels(hparams.labels		)
										_UpperCAmelCase    :						List[str]							=      CrossEntropyLoss().ignore_index
										super().__init__(A_  ,   len(self.labels		)  ,   self.mode		)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return self.model(**A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
										if self.config.model_type != "distilbert":
														_UpperCAmelCase    :						Dict							=      (
														    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
														)  # XLM and RoBERTa don"t use token_type_ids

										_UpperCAmelCase    :						str							=      self(**A_		)
										_UpperCAmelCase    :						Optional[int]							=      outputs[0]
										# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
										return {"loss": loss}


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.hparams
										for mode in ["train", "dev", "test"]:
														_UpperCAmelCase    :						Optional[Any]							=      self._feature_file(A_		)
														if os.path.exists(A_		) and not args.overwrite_cache:
																		logger.info("Loading features from cached file %s"  ,   A_		)
																		_UpperCAmelCase    :						Dict							=      torch.load(A_		)
														else:
																		logger.info("Creating features from dataset file at %s"  ,   args.data_dir		)
																		_UpperCAmelCase    :						Optional[int]							=      self.token_classification_task.read_examples_from_file(args.data_dir  ,   A_		)
																		_UpperCAmelCase    :						Dict							=      self.token_classification_task.convert_examples_to_features(
																		    A_  ,   self.labels  ,   args.max_seq_length  ,   self.tokenizer  ,   cls_token_at_end=bool(self.config.model_type in ["xlnet"]		)  ,   cls_token=self.tokenizer.cls_token  ,   cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0  ,   sep_token=self.tokenizer.sep_token  ,   sep_token_extra=A_  ,   pad_on_left=bool(self.config.model_type in ["xlnet"]		)  ,   pad_token=self.tokenizer.pad_token_id  ,   pad_token_segment_id=self.tokenizer.pad_token_type_id  ,   pad_token_label_id=self.pad_token_label_id  ,   )
																		logger.info("Saving features into cached file %s"  ,   A_		)
																		torch.save(A_  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = False		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self._feature_file(A_		)
										logger.info("Loading features from cached file %s"  ,   A_		)
										_UpperCAmelCase    :						List[str]							=      torch.load(A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.tensor([f.input_ids for f in features]  ,   dtype=torch.long		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.tensor([f.attention_mask for f in features]  ,   dtype=torch.long		)
										if features[0].token_type_ids is not None:
														_UpperCAmelCase    :						str							=      torch.tensor([f.token_type_ids for f in features]  ,   dtype=torch.long		)
										else:
														_UpperCAmelCase    :						Optional[int]							=      torch.tensor([0 for f in features]  ,   dtype=torch.long		)
														# HACK(we will not use this anymore soon)
										_UpperCAmelCase    :						Any							=      torch.tensor([f.label_ids for f in features]  ,   dtype=torch.long		)
										return DataLoader(
										    TensorDataset(A_  ,   A_  ,   A_  ,   A_		)  ,   batch_size=A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										"""Compute validation""" ""
										_UpperCAmelCase    :						List[Any]							=      {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
										if self.config.model_type != "distilbert":
														_UpperCAmelCase    :						int							=      (
														    batch[2] if self.config.model_type in ["bert", "xlnet"] else None
														)  # XLM and RoBERTa don"t use token_type_ids
										_UpperCAmelCase    :						Dict							=      self(**A_		)
										_UpperCAmelCase						,							_UpperCAmelCase    :						int							=      outputs[:2]
										_UpperCAmelCase    :						Union[str, Any]							=      logits.detach().cpu().numpy()
										_UpperCAmelCase    :						int							=      inputs["labels"].detach().cpu().numpy()
										return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      torch.stack([x["val_loss"] for x in outputs]		).mean()
										_UpperCAmelCase    :						Tuple							=      np.concatenate([x["pred"] for x in outputs]  ,   axis=0		)
										_UpperCAmelCase    :						Optional[int]							=      np.argmax(A_  ,   axis=2		)
										_UpperCAmelCase    :						Tuple							=      np.concatenate([x["target"] for x in outputs]  ,   axis=0		)

										_UpperCAmelCase    :						Tuple							=      dict(enumerate(self.labels		)		)
										_UpperCAmelCase    :						List[str]							=      [[] for _ in range(out_label_ids.shape[0]		)]
										_UpperCAmelCase    :						Optional[Any]							=      [[] for _ in range(out_label_ids.shape[0]		)]

										for i in range(out_label_ids.shape[0]		):
														for j in range(out_label_ids.shape[1]		):
																		if out_label_ids[i, j] != self.pad_token_label_id:
																						out_label_list[i].append(label_map[out_label_ids[i][j]]		)
																						preds_list[i].append(label_map[preds[i][j]]		)

										_UpperCAmelCase    :						Optional[Any]							=      {
										    "val_loss": val_loss_mean,
										    "accuracy_score": accuracy_score(A_  ,   A_		),
										    "precision": precision_score(A_  ,   A_		),
										    "recall": recall_score(A_  ,   A_		),
										    "f1": fa_score(A_  ,   A_		),
										}

										_UpperCAmelCase    :						int							=      dict(results.items()		)
										_UpperCAmelCase    :						Tuple							=      results
										return ret, preds_list, out_label_list


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, Any]							=      self._eval_end(A_		)
										_UpperCAmelCase    :						List[Any]							=      ret["log"]
										return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						int							=      self._eval_end(A_		)

										# Converting to the dict required by pl
										# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
										# pytorch_lightning/trainer/logging.py#L139
										_UpperCAmelCase    :						int							=      ret["log"]
										# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
										return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





						@staticmethod
						def     _UpperCAmelCase			(    A_  ,   A_		):







										'''simple docstring'''


										BaseTransformer.add_model_specific_args(A_  ,   A_		)
										parser.add_argument(
										    "--task_type"  ,   default="NER"  ,   type=A_  ,   help="Task type to fine tune in training (e.g. NER, POS, etc)"		)
										parser.add_argument(
										    "--max_seq_length"  ,   default=128  ,   type=A_  ,   help=(
										        "The maximum total input sequence length after tokenization. Sequences longer "
										        "than this will be truncated, sequences shorter will be padded."
										    )  ,   )

										parser.add_argument(
										    "--labels"  ,   default=""  ,   type=A_  ,   help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."  ,   )
										parser.add_argument(
										    "--gpus"  ,   default=0  ,   type=A_  ,   help="The number of GPUs allocated for this, it is by default 0 meaning none"  ,   )

										parser.add_argument(
										    "--overwrite_cache"  ,   action="store_true"  ,   help="Overwrite the cached training and evaluation sets"		)

										return parser


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					add_generic_args(parser, os.getcwd())
					SCREAMING_SNAKE_CASE_            = NERTransformer.add_model_specific_args(parser, os.getcwd())
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					SCREAMING_SNAKE_CASE_            = NERTransformer(args)
					SCREAMING_SNAKE_CASE_            = generic_train(model, args)

					if args.do_predict:
										# See https://github.com/huggingface/transformers/issues/3159
										# pl use this default format to create a checkpoint:
										# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
										# /pytorch_lightning/callbacks/model_checkpoint.py#L322
										SCREAMING_SNAKE_CASE_            = sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
										SCREAMING_SNAKE_CASE_            = model.load_from_checkpoint(checkpoints[-1])
										trainer.test(model)





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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int=False	)				->    Optional[Any]:
				_UpperCAmelCase    :						Union[str, Any]							=      []

				# fmt: off
				# stem:
				rename_keys.append(("cls_token", "vit.embeddings.cls_token")	)
				rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")	)

				rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")	)
				rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")	)

				# backbone
				rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")	)
				rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")	)
				rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")	)

				for stage_idx in range(len(config.backbone_config.depths	)	):
								for layer_idx in range(config.backbone_config.depths[stage_idx]	):
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')	)
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')	)
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')	)
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')	)
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')	)
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')	)
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')	)
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')	)
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')	)

								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')	)
								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')	)
								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')	)

				# transformer encoder
				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')	)

				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"
								_UpperCAmelCase    :						Union[str, Any]							=      [(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"),
								    ]	)
				# fmt: on

				return rename_keys

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    Optional[int]:
				_UpperCAmelCase    :						Dict							=      ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(lowerCAmelCase					, lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any					, lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					Dict	)				->    Tuple:
				_UpperCAmelCase    :						str							=      dct.pop(lowerCAmelCase	)
				_UpperCAmelCase    :						Any							=      val

def 							__SCREAMING_SNAKE_CASE							(			)				->    List[Any]:
				_UpperCAmelCase    :						Tuple							=      "http://images.cocodataset.org/val2017/000000039769.jpg"
				_UpperCAmelCase    :						Tuple							=      Image.open(requests.get(lowerCAmelCase					, stream=lowerCAmelCase	).raw	)
				return im

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Tuple					, lowerCAmelCase:					int					, lowerCAmelCase:					List[Any]=False	)				->    Any:
				_UpperCAmelCase    :						List[Any]							=      BitConfig(
				    global_padding="same"					, layer_type="bottleneck"					, depths=(3, 4, 9)					, out_features=["stage3"]					, embedding_dynamic_padding=lowerCAmelCase					, )
				_UpperCAmelCase    :						Optional[Any]							=      ViTHybridConfig(backbone_config=lowerCAmelCase					, image_size=384					, num_labels=1000	)
				_UpperCAmelCase    :						str							=      False

				# load original model from timm
				_UpperCAmelCase    :						Optional[Any]							=      timm.create_model(lowerCAmelCase					, pretrained=lowerCAmelCase	)
				timm_model.eval()

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

				_UpperCAmelCase    :						str							=      "huggingface/label-files"
				_UpperCAmelCase    :						Tuple							=      "imagenet-1k-id2label.json"
				_UpperCAmelCase    :						Union[str, Any]							=      json.load(open(hf_hub_download(lowerCAmelCase					, lowerCAmelCase					, repo_type="dataset"	)					, "r"	)	)
				_UpperCAmelCase    :						Any							=      {int(lowerCAmelCase	): v for k, v in idalabel.items()}
				_UpperCAmelCase    :						Dict							=      idalabel
				_UpperCAmelCase    :						Dict							=      {v: k for k, v in idalabel.items()}

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								_UpperCAmelCase    :						Union[str, Any]							=      ViTHybridModel(lowerCAmelCase	).eval()
				else:
								_UpperCAmelCase    :						Optional[Any]							=      ViTHybridForImageClassification(lowerCAmelCase	).eval()
				model.load_state_dict(lowerCAmelCase	)

				# create image processor
				_UpperCAmelCase    :						Any							=      create_transform(**resolve_data_config({}					, model=lowerCAmelCase	)	)
				_UpperCAmelCase    :						Tuple							=      transform.transforms

				_UpperCAmelCase    :						Tuple							=      {
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

				_UpperCAmelCase    :						Any							=      ViTHybridImageProcessor(
				    do_resize=lowerCAmelCase					, size={"shortest_edge": timm_transforms[0].size}					, resample=pillow_resamplings[timm_transforms[0].interpolation.value]					, do_center_crop=lowerCAmelCase					, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}					, do_normalize=lowerCAmelCase					, image_mean=timm_transforms[-1].mean.tolist()					, image_std=timm_transforms[-1].std.tolist()					, )

				_UpperCAmelCase    :						List[str]							=      prepare_img()
				_UpperCAmelCase    :						List[Any]							=      transform(lowerCAmelCase	).unsqueeze(0	)
				_UpperCAmelCase    :						Optional[Any]							=      processor(lowerCAmelCase					, return_tensors="pt"	).pixel_values

				# verify pixel values
				assert torch.allclose(lowerCAmelCase					, lowerCAmelCase	)

				# verify logits
				with torch.no_grad():
								_UpperCAmelCase    :						Union[str, Any]							=      model(lowerCAmelCase	)
								_UpperCAmelCase    :						Optional[Any]							=      outputs.logits

				print("Predicted class:"					, logits.argmax(-1	).item()	)
				if base_model:
								_UpperCAmelCase    :						List[Any]							=      timm_model.forward_features(lowerCAmelCase	)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(lowerCAmelCase					, outputs.pooler_output					, atol=1E-3	)
				else:
								_UpperCAmelCase    :						Any							=      timm_model(lowerCAmelCase	)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(lowerCAmelCase					, outputs.logits					, atol=1E-3	)
				print("Looks ok!"	)

				if pytorch_dump_folder_path is not None:
								Path(lowerCAmelCase	).mkdir(exist_ok=lowerCAmelCase	)
								print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'	)
								model.save_pretrained(lowerCAmelCase	)
								print(F'Saving processor to {pytorch_dump_folder_path}'	)
								processor.save_pretrained(lowerCAmelCase	)

				if push_to_hub:
								print(F'Pushing model and processor to the hub {vit_name}'	)
								model.push_to_hub(F'ybelkada/{vit_name}'	)
								processor.push_to_hub(F'ybelkada/{vit_name}'	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--vit_name',
					    default='vit_base_r50_s16_384',
					    type=str,
					    help='Name of the hybrid 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.'
					)
					parser.add_argument(
					    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
					)

					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)


