



'''simple docstring'''

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()
__lowerCAmelCase									=   logging.get_logger(__name__)



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      List[Any]:
		_a     :      List[str]        =							[]
		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     :      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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Dict:
		for i in range(config.num_hidden_layers  ):
				if base_model:
						_a     :      Union[str, Any]        =							''
				else:
						_a     :      Optional[Any]        =							'vit.'
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				_a     :      Dict        =							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     :      Union[str, Any]        =							in_proj_weight[
				    : config.hidden_size, :
				]
				_a     :      Union[str, Any]        =							in_proj_bias[: config.hidden_size]
				_a     :      Union[str, Any]        =							in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				_a     :      int        =							in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				_a     :      Dict        =							in_proj_weight[
				    -config.hidden_size :, :
				]
				_a     :      Optional[Any]        =							in_proj_bias[-config.hidden_size :]



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[Any]:
		_a     :      int        =							['head.weight', 'head.bias']
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase_						,			lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[str]:
		_a     :      Tuple        =							dct.pop(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							val



def      __lowerCamelCase     (      )			->      Any:
		_a     :      Tuple        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
		_a     :      List[Any]        =							Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_  ).raw  )
		return im



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      Any        =							ViTConfig()
		_a     :      List[Any]        =							False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				_a     :      List[Any]        =							True
				_a     :      str        =							int(vit_name[-12:-10]  )
				_a     :      Any        =							int(vit_name[-9:-6]  )
		else:
				_a     :      str        =							1000
				_a     :      List[Any]        =							'huggingface/label-files'
				_a     :      int        =							'imagenet-1k-id2label.json'
				_a     :      Any        =							json.load(open(hf_hub_download(lowerCAmelCase_						,			lowerCAmelCase_						,			repo_type='dataset'  )						,			'r'  )  )
				_a     :      Optional[int]        =							{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
				_a     :      str        =							idalabel
				_a     :      int        =							{v: k for k, v in idalabel.items()}
				_a     :      List[Any]        =							int(vit_name[-6:-4]  )
				_a     :      Optional[int]        =							int(vit_name[-3:]  )
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith('tiny'  ):
						_a     :      Optional[int]        =							192
						_a     :      Dict        =							768
						_a     :      List[Any]        =							12
						_a     :      Union[str, Any]        =							3
				elif vit_name[9:].startswith('small'  ):
						_a     :      Optional[Any]        =							384
						_a     :      str        =							1536
						_a     :      str        =							12
						_a     :      Union[str, Any]        =							6
				else:
						pass
		else:
				if vit_name[4:].startswith('small'  ):
						_a     :      int        =							768
						_a     :      str        =							2304
						_a     :      List[str]        =							8
						_a     :      Optional[int]        =							8
				elif vit_name[4:].startswith('base'  ):
						pass
				elif vit_name[4:].startswith('large'  ):
						_a     :      Any        =							1024
						_a     :      Optional[int]        =							4096
						_a     :      Union[str, Any]        =							24
						_a     :      Any        =							16
				elif vit_name[4:].startswith('huge'  ):
						_a     :      str        =							1280
						_a     :      Dict        =							5120
						_a     :      str        =							32
						_a     :      str        =							16

    # load original model from timm
		_a     :      Union[str, Any]        =							timm.create_model(lowerCAmelCase_						,			pretrained=lowerCAmelCase_  )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		_a     :      str        =							timm_model.state_dict()
		if base_model:
				remove_classification_head_(lowerCAmelCase_  )
		_a     :      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":
				_a     :      Union[str, Any]        =							ViTModel(lowerCAmelCase_  ).eval()
		else:
				_a     :      str        =							ViTForImageClassification(lowerCAmelCase_  ).eval()
		model.load_state_dict(lowerCAmelCase_  )

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

		if base_model:
				_a     :      Optional[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:
				_a     :      Union[str, Any]        =							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__":
							__lowerCAmelCase									=   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.'''
							)

							__lowerCAmelCase									=   parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase           =		'''▁'''

lowerCAmelCase           =		{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase           =		{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

lowerCAmelCase           =		{
    '''google/pegasus-xsum''': 5_1_2,
}


lowerCAmelCase           =		logging.get_logger(__name__)





class       A    (   A_ ):
							UpperCamelCase_   :							Union[str, Any]       =VOCAB_FILES_NAMES

							UpperCamelCase_   :							List[Any]       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							int       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							Tuple       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							int       =['''input_ids''', '''attention_mask''']


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<mask_2>"      ,				lowerCAmelCase="<mask_1>"      ,				lowerCAmelCase=None      ,				lowerCAmelCase=1_0_3      ,				lowerCAmelCase = None      ,				**lowerCAmelCase      ,				):
									__lowercase=			offset
									if additional_special_tokens is not None:
											if not isinstance(lowerCAmelCase      ,				lowerCAmelCase					):
													raise TypeError(
													    f'additional_special_tokens should be of type {type(lowerCAmelCase					)}, but is'
													    f' {type(lowerCAmelCase					)}'					)

											__lowercase=			(
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    f'<unk_{i}>' for i in range(len(lowerCAmelCase					)      ,				self.offset - 1					)
											]

											if len(set(lowerCAmelCase					)					) != len(lowerCAmelCase					):
													raise ValueError(
													    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
													    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'					)
											__lowercase=			additional_special_tokens_extended
									else:
											__lowercase=			[mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [f'<unk_{i}>' for i in range(2      ,				self.offset					)]

									__lowercase=			{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=lowerCAmelCase      ,				unk_token=lowerCAmelCase      ,				mask_token=lowerCAmelCase      ,				pad_token=lowerCAmelCase      ,				mask_token_sent=lowerCAmelCase      ,				offset=lowerCAmelCase      ,				additional_special_tokens=lowerCAmelCase      ,				sp_model_kwargs=self.sp_model_kwargs      ,				**lowerCAmelCase      ,				)
									__lowercase=			mask_token_sent
									__lowercase=			vocab_file
									__lowercase=			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(lowerCAmelCase					)

									# add special tokens to encoder dict
									__lowercase=			{
									    0: self.pad_token,
									    1: self.eos_token,
									}

									if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    }					)

									if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1      ,				self.offset - 1					)}					)

									__lowercase=			{v: k for k, v in self.encoder.items()}


							@property
							def 			_A       (self					):
									return len(self.sp_model					) + self.offset


							def 			_A       (self					):
									__lowercase=			{self.convert_ids_to_tokens(lowerCAmelCase					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab


							def __getstate__(self					):
									__lowercase=			self.__dict__.copy()
									__lowercase=			None
									return state


							def __setstate__(self      ,				lowerCAmelCase					):
									__lowercase=			d

									# for backward compatibility
									if not hasattr(self      ,				'sp_model_kwargs'					):
											__lowercase=			{}

									__lowercase=			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(self.vocab_file					)


							def 			_A       (self      ,				lowerCAmelCase					):
									return self.sp_model.encode(lowerCAmelCase      ,				out_type=lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase					):
									if token in self.decoder:
											return self.decoder[token]
									elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
									__lowercase=			self.sp_model.piece_to_id(lowerCAmelCase					)
									return sp_id + self.offset


							def 			_A       (self      ,				lowerCAmelCase					):
									if index in self.encoder:
											return self.encoder[index]
									elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
									else:
											__lowercase=			self.sp_model.IdToPiece(index - self.offset					)
									return token


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			[]
									__lowercase=			''
									for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
													out_string += self.sp_model.decode(lowerCAmelCase					) + token
													__lowercase=			[]
											else:
													current_sub_tokens.append(lowerCAmelCase					)
									out_string += self.sp_model.decode(lowerCAmelCase					)
									return out_string.strip()


							def 			_A       (self      ,				lowerCAmelCase=False					):
									return 1


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			set(self.all_special_ids					)  # call it once instead of inside list comp
									all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special

									return [1 if x in all_special_ids else 0 for x in seq]


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None      ,				lowerCAmelCase = False					):
									if already_has_special_tokens:
											return self._special_token_mask(lowerCAmelCase					)
									elif token_ids_a is None:
											return self._special_token_mask(lowerCAmelCase					) + [1]
									else:
											return self._special_token_mask(token_ids_a + token_ids_a					) + [1]


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=None					):
									if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return token_ids_a + token_ids_a + [self.eos_token_id]







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									if not os.path.isdir(lowerCAmelCase					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
									__lowercase=			os.path.join(
									    lowerCAmelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					) and os.path.isfile(self.vocab_file					):
											copyfile(self.vocab_file      ,				lowerCAmelCase					)
									elif not os.path.isfile(self.vocab_file					):
											with open(lowerCAmelCase      ,				'wb'					) as fi:
													__lowercase=			self.sp_model.serialized_model_proto()
													fi.write(lowerCAmelCase					)

									return (out_vocab_file,)






