





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()
UpperCamelCase__											= logging.get_logger(__name__)





def     _UpperCamelCase     (a__   :Dict   ,			a__   :Any=False       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]
							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"
														UpperCamelCase__				     =			[(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     _UpperCamelCase     (a__   :Union[str, Any]   ,			a__   :Union[str, Any]   ,			a__   :int=False       ):






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :Dict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(a__   ,			a__       )





def     _UpperCamelCase     (a__   :Tuple   ,			a__   :Tuple   ,			a__   :Optional[int]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			dct.pop(a__       )
							UpperCamelCase__				     =			val





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCamelCase__				     =			Image.open(requests.get(a__   ,			stream=a__       ).raw       )
							return im





@torch.no_grad()
def     _UpperCamelCase     (a__   :str   ,			a__   :int       ):






							"""simple docstring"""





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

    # load original model from timm
							UpperCamelCase__				     =			timm.create_model(a__   ,			pretrained=a__       )
							timm_model.eval()

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

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														UpperCamelCase__				     =			ViTModel(a__       ).eval()
							else:
														UpperCamelCase__				     =			ViTForImageClassification(a__       ).eval()
							model.load_state_dict(a__       )

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

							if base_model:
														UpperCamelCase__				     =			timm_model.forward_features(a__       )
														assert timm_pooled_output.shape == outputs.pooler_output.shape
														assert torch.allclose(a__   ,			outputs.pooler_output   ,			atol=1e-3       )
							else:
														UpperCamelCase__				     =			timm_model(a__       )
														assert timm_logits.shape == outputs.logits.shape
														assert torch.allclose(a__   ,			outputs.logits   ,			atol=1e-3       )

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


if __name__ == "__main__":
					UpperCamelCase__											= 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."
					)

					UpperCamelCase__											= 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


UpperCamelCase__											= "▁"

UpperCamelCase__											= {"vocab_file": "spiece.model"}

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

UpperCamelCase__											= {
    "google/pegasus-xsum": 512,
}


UpperCamelCase__											= logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      Union[str, Any]						     =       VOCAB_FILES_NAMES

							snake_case     :      str						     =       VOCAB_FILES_NAMES
							snake_case     :      Any						     =       PRETRAINED_VOCAB_FILES_MAP
							snake_case     :      int						     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case     :      Optional[int]						     =       ["""input_ids""", """attention_mask"""]






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase="<pad>"  , __lowerCAmelCase="</s>"  , __lowerCAmelCase="<unk>"  , __lowerCAmelCase="<mask_2>"  , __lowerCAmelCase="<mask_1>"  , __lowerCAmelCase=None  , __lowerCAmelCase=103  , __lowerCAmelCase = None  , **__lowerCAmelCase  , ):
														UpperCamelCase__				     =			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  )}"""  )

																					UpperCamelCase__				     =			(
																					    ([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}."""  )
																					UpperCamelCase__				     =			additional_special_tokens_extended
														else:
																					UpperCamelCase__				     =			[mask_token_sent] if mask_token_sent is not None else []
																					additional_special_tokens += [f"""<unk_{i}>""" for i in range(2  , self.offset  )]

														UpperCamelCase__				     =			{} 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  , )
														UpperCamelCase__				     =			mask_token_sent
														UpperCamelCase__				     =			vocab_file
														UpperCamelCase__				     =			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
														self.sp_model.Load(__lowerCAmelCase  )

														# add special tokens to encoder dict
														UpperCamelCase__				     =			{
														    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  )}  )

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






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






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			{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  ):
														UpperCamelCase__				     =			self.__dict__.copy()
														UpperCamelCase__				     =			None
														return state






							def __setstate__(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			d

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

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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														return self.sp_model.encode(__lowerCAmelCase  , out_type=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														if token in self.decoder:
																					return self.decoder[token]
														elif token in self.added_tokens_decoder:
																					return self.added_tokens_decoder[token]
														UpperCamelCase__				     =			self.sp_model.piece_to_id(__lowerCAmelCase  )
														return sp_id + self.offset






							def 				_lowerCamelCase		(						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:
																					UpperCamelCase__				     =			self.sp_model.IdToPiece(index - self.offset  )
														return token






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			[]
														UpperCamelCase__				     =			""""""
														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
																												UpperCamelCase__				     =			[]
																					else:
																												current_sub_tokens.append(__lowerCAmelCase  )
														out_string += self.sp_model.decode(__lowerCAmelCase  )
														return out_string.strip()






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase=False  ):
														return 1






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			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 				_lowerCamelCase		(						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 				_lowerCamelCase		(						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 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase = None  ):
														if not os.path.isdir(__lowerCAmelCase  ):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
																					return
														UpperCamelCase__				     =			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:
																												UpperCamelCase__				     =			self.sp_model.serialized_model_proto()
																												fi.write(__lowerCAmelCase  )

														return (out_vocab_file,)




