




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 			_a   (		_lowerCAmelCase							:       Optional[Any]   ,   _lowerCAmelCase							:       Optional[Any]=False  ):
					__lowerCAmelCase            = []
					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"
										__lowerCAmelCase            = [(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							:       Optional[Any]   ,   _lowerCAmelCase							:       List[Any]   ,   _lowerCAmelCase							:       List[Any]=False  ):




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







def 			_a   (		_lowerCAmelCase							:       Dict  ):
					__lowerCAmelCase            = ['head.weight', 'head.bias']
					for k in ignore_keys:
										state_dict.pop(__lowercase   ,   __lowercase  )







def 			_a   (		_lowerCAmelCase							:       Union[str, Any]   ,   _lowerCAmelCase							:       Tuple   ,   _lowerCAmelCase							:       Optional[int]  ):
					__lowerCAmelCase            = dct.pop(__lowercase  )
					__lowerCAmelCase            = val







def 			_a   (		):
					__lowerCAmelCase            = 'http://images.cocodataset.org/val2017/000000039769.jpg'
					__lowerCAmelCase            = Image.open(requests.get(__lowercase   ,   stream=__lowercase  ).raw  )
					return im







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

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

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

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

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

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

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


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 json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class     a__					(      snake_case__   ):






						def __init__(     self	,    _A = "▁"	,    _A = True	,    _A = "<unk>"	,    _A = "</s>"	,    _A = "<pad>"	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = {
											    "pad": {"id": 0, "token": pad_token},
											    "eos": {"id": 1, "token": eos_token},
											    "unk": {"id": 2, "token": unk_token},
											}

											__lowerCAmelCase            = [None] * len(self.special_tokens    )
											for token_dict in self.special_tokens.values():
																__lowerCAmelCase            = token_dict["token"]

											__lowerCAmelCase            = Tokenizer(Unigram()    )

											__lowerCAmelCase            = normalizers.Sequence(
											    [
											        normalizers.Nmt(),
											        normalizers.NFKC(),
											        normalizers.Replace(Regex(" {2,}"    )	,    " "    ),
											        normalizers.Lowercase(),
											    ]    )
											__lowerCAmelCase            = pre_tokenizers.Sequence(
											    [
											        pre_tokenizers.Metaspace(replacement=_A	,    add_prefix_space=_A    ),
											        pre_tokenizers.Digits(individual_digits=_A    ),
											        pre_tokenizers.Punctuation(),
											    ]    )
											__lowerCAmelCase            = decoders.Metaspace(replacement=_A	,    add_prefix_space=_A    )

											__lowerCAmelCase            = TemplateProcessing(
											    single=f"""$A {self.special_tokens['eos']['token']}"""	,    special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]	,    )

											__lowerCAmelCase            = {
											    "model": "SentencePieceUnigram",
											    "replacement": replacement,
											    "add_prefix_space": add_prefix_space,
											}

											super().__init__(_A	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = 8_0_0_0	,    _A = True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = trainers.UnigramTrainer(
											    vocab_size=_A	,    special_tokens=self.special_tokens_list	,    show_progress=_A	,    )

											if isinstance(_A	,    _A    ):
																__lowerCAmelCase            = [files]
											self._tokenizer.train(_A	,    trainer=_A    )

											self.add_unk_id()






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = 8_0_0_0	,    _A = True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = trainers.UnigramTrainer(
											    vocab_size=_A	,    special_tokens=self.special_tokens_list	,    show_progress=_A	,    )

											self._tokenizer.train_from_iterator(_A	,    trainer=_A    )

											self.add_unk_id()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = json.loads(self._tokenizer.to_str()    )

											__lowerCAmelCase            = self.special_tokens["unk"]["id"]

											__lowerCAmelCase            = Tokenizer.from_str(json.dumps(_A    )    )




