





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()
lowercase_		:				Optional[int]				=						logging.get_logger(__name__)

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

def       A__(		__lowerCAmelCase )     ->   List[str]:
				_snake_case :			List[Any]  						=   ['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(__lowerCAmelCase	,			__lowerCAmelCase )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )     ->   Optional[int]:
				_snake_case :			Union[str, Any]  						=   dct.pop(__lowerCAmelCase )
				_snake_case :			Optional[Any]  						=   val

def       A__(		)     ->   List[str]:
				_snake_case :			int  						=   'http://images.cocodataset.org/val2017/000000039769.jpg'
				_snake_case :			Optional[Any]  						=   Image.open(requests.get(__lowerCAmelCase	,			stream=__lowerCAmelCase ).raw )
				return im

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

												 # load original model from timm
				_snake_case :			Any  						=   timm.create_model(__lowerCAmelCase	,			pretrained=__lowerCAmelCase )
				timm_model.eval()

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

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								_snake_case :			Dict  						=   ViTModel(__lowerCAmelCase ).eval()
				else:
								_snake_case :			Any  						=   ViTForImageClassification(__lowerCAmelCase ).eval()
				model.load_state_dict(__lowerCAmelCase )

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

				if base_model:
								_snake_case :			Tuple  						=   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:
								_snake_case :			Optional[int]  						=   timm_model(__lowerCAmelCase )
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(__lowerCAmelCase	,			outputs.logits	,			atol=1E-3 )

				Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
				print(F'''Saving model {vit_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__":
		lowercase_		:				Union[str, Any]				=						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.'''
		)

		lowercase_		:				Dict				=						parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)













import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase_		:				Tuple				=						logging.getLogger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Dict ,				lowerCamelCase_			:		Dict=-1					):





										'''simple docstring'''







										_snake_case :			str  						=   label_idx







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Optional[int] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			Union[str, Any]  						=   mode.value
										_snake_case :			Optional[int]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Dict  						=   1
										_snake_case :			List[str]  						=   []
										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														_snake_case :			List[Any]  						=   []
														_snake_case :			int  						=   []
														for line in f:
																		if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																						if words:
																										examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																										guid_index += 1
																										_snake_case :			Optional[Any]  						=   []
																										_snake_case :			Union[str, Any]  						=   []
																		else:
																						_snake_case :			Tuple  						=   line.split(' '					)
																						words.append(splits[0]					)
																						if len(lowerCamelCase_					) > 1:
																										labels.append(splits[self.label_idx].replace('\n' ,				''					)					)
																						else:
																										# Examples could have no label for mode = "test"
																										labels.append('O'					)
														if words:
																		examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
										return examples







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			str  						=   0
										for line in test_input_reader:
														if line.startswith('-DOCSTART-'					) or line == "" or line == "\n":
																		writer.write(lowerCamelCase_					)
																		if not preds_list[example_id]:
																						example_id += 1
														elif preds_list[example_id]:
																		_snake_case :			List[str]  						=   line.split()[0] + ' ' + preds_list[example_id].pop(0					) + '\n'
																		writer.write(lowerCamelCase_					)
														else:
																		logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.' ,				line.split()[0]					)







						def 		__UpperCAmelCase       (    self			:		List[str] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			Optional[int]  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Optional[int]  						=   ['O'] + labels
														return labels
										else:
														return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def __init__(    self			:		Optional[int]					):





										'''simple docstring'''







										super().__init__(label_idx=-2					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		_snake_case :			str  						=   f.read().splitlines()
														if "O" not in labels:
																		_snake_case :			Union[str, Any]  						=   ['O'] + labels
														return labels
										else:
														return [
														    "O",
														    "B-ADVP",
														    "B-INTJ",
														    "B-LST",
														    "B-PRT",
														    "B-NP",
														    "B-SBAR",
														    "B-VP",
														    "B-ADJP",
														    "B-CONJP",
														    "B-PP",
														    "I-ADVP",
														    "I-INTJ",
														    "I-LST",
														    "I-PRT",
														    "I-NP",
														    "I-SBAR",
														    "I-VP",
														    "I-ADJP",
														    "I-CONJP",
														    "I-PP",
														]



class 			lowercase				(							a_     ):



						"""simple docstring"""

						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any] ,				lowerCamelCase_			:		Union[Split, str]					):





										'''simple docstring'''







										if isinstance(lowerCamelCase_ ,				lowerCamelCase_					):
														_snake_case :			str  						=   mode.value
										_snake_case :			List[str]  						=   os.path.join(lowerCamelCase_ ,				f'''{mode}.txt'''					)
										_snake_case :			Tuple  						=   1
										_snake_case :			List[str]  						=   []

										with open(lowerCamelCase_ ,				encoding='utf-8'					) as f:
														for sentence in parse_incr(lowerCamelCase_					):
																		_snake_case :			List[str]  						=   []
																		_snake_case :			str  						=   []
																		for token in sentence:
																						words.append(token['form']					)
																						labels.append(token['upos']					)
																		assert len(lowerCamelCase_					) == len(lowerCamelCase_					)
																		if words:
																						examples.append(InputExample(guid=f'''{mode}-{guid_index}''' ,				words=lowerCamelCase_ ,				labels=lowerCamelCase_					)					)
																						guid_index += 1
										return examples







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		TextIO ,				lowerCamelCase_			:		List					):





										'''simple docstring'''







										_snake_case :			Dict  						=   0
										for sentence in parse_incr(lowerCamelCase_					):
														_snake_case :			Optional[int]  						=   preds_list[example_id]
														_snake_case :			List[Any]  						=   ''
														for token in sentence:
																		out += f'''{token['form']} ({token['upos']}|{s_p.pop(0					)}) '''
														out += "\n"
														writer.write(lowerCamelCase_					)
														example_id += 1







						def 		__UpperCAmelCase       (    self			:		Optional[Any] ,				lowerCamelCase_			:		str					):





										'''simple docstring'''







										if path:
														with open(lowerCamelCase_ ,				'r'					) as f:
																		return f.read().splitlines()
										else:
														return [
														    "ADJ",
														    "ADP",
														    "ADV",
														    "AUX",
														    "CCONJ",
														    "DET",
														    "INTJ",
														    "NOUN",
														    "NUM",
														    "PART",
														    "PRON",
														    "PROPN",
														    "PUNCT",
														    "SCONJ",
														    "SYM",
														    "VERB",
														    "X",
														]






