





'''simple docstring'''

import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase							:     Dict		     =get_tests_dir("fixtures/spiece.model")






@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    DebertaVaTokenizer
 __lowercase       		=    DebertaVaTokenizerFast
 __lowercase       		=    True
 __lowercase       		=    True
 def 					UpperCAmelCase_							(    self				:int							)->							Optional[Any]:
     super().setUp()

     # We have a SentencePiece fixture for testing
     A__    =      DebertaVaTokenizer(lowercase_						,	unk_token="<unk>"							)
     tokenizer.save_pretrained(self.tmpdirname							)
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:Dict							)->							int:
     A__    =      "this is a test"
     A__    =      "this is a test"
     return input_text, output_text
 def 					UpperCAmelCase_							(    self				:str							)->							Dict:
     A__    =      "<pad>"
     A__    =      0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_							)						,	lowercase_							)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_							)						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[str]:
     A__    =      list(self.get_tokenizer().get_vocab().keys()							)
     self.assertEqual(vocab_keys[0]						,	"<pad>"							)
     self.assertEqual(vocab_keys[1]						,	"<unk>"							)
     self.assertEqual(vocab_keys[-1]						,	"[PAD]"							)
     self.assertEqual(len(lowercase_							)						,	3_00_01							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							str:
     self.assertEqual(self.get_tokenizer().vocab_size						,	3_00_00							)
 def 					UpperCAmelCase_							(    self				:Any							)->							List[Any]:
     # fmt: off
     A__    =      " \tHeLLo!how  \n Are yoU?  "
     A__    =      ["▁hello", "!", "how", "▁are", "▁you", "?"]
     # fmt: on

     A__    =      DebertaVaTokenizer(lowercase_						,	do_lower_case=lowercase_							)
     A__    =      tokenizer.convert_ids_to_tokens(tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      DebertaVaTokenizerFast(lowercase_						,	do_lower_case=lowercase_							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)
 @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."							)
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     pass
 @unittest.skip("There is an inconsistency between slow and fast tokenizer due to a bug in the fast one."							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[Any]:
     pass
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Any:
     # fmt: off
     A__    =      "I was born in 92000, and this is falsé."
     A__    =      ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
     # fmt: on

     A__    =      DebertaVaTokenizer(lowercase_						,	split_by_punct=lowercase_							)
     A__    =      tokenizer.convert_ids_to_tokens(tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      DebertaVaTokenizerFast(lowercase_						,	split_by_punct=lowercase_							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Optional[int]:
     # fmt: off
     A__    =      "I was born in 92000, and this is falsé."
     A__    =      ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
     # fmt: on

     A__    =      DebertaVaTokenizer(lowercase_						,	do_lower_case=lowercase_						,	split_by_punct=lowercase_							)
     A__    =      tokenizer.convert_ids_to_tokens(tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      DebertaVaTokenizerFast(lowercase_						,	do_lower_case=lowercase_						,	split_by_punct=lowercase_							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)
     self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     # fmt: off
     A__    =      "I was born in 92000, and this is falsé."
     A__    =      ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
     # fmt: on

     A__    =      DebertaVaTokenizer(lowercase_						,	do_lower_case=lowercase_						,	split_by_punct=lowercase_							)
     A__    =      tokenizer.convert_ids_to_tokens(tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      DebertaVaTokenizerFast(lowercase_						,	do_lower_case=lowercase_						,	split_by_punct=lowercase_							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
     # fmt: off
     A__    =      "I was born in 92000, and this is falsé."
     A__    =      ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", "▁", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "▁", ".", ]
     # fmt: on

     A__    =      DebertaVaTokenizer(lowercase_						,	do_lower_case=lowercase_						,	split_by_punct=lowercase_							)
     A__    =      tokenizer.convert_ids_to_tokens(tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      DebertaVaTokenizerFast(lowercase_						,	do_lower_case=lowercase_						,	split_by_punct=lowercase_							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:int							)->							Dict:
     # fmt: off
     A__    =      " \tHeLLo!how  \n Are yoU?  "
     A__    =      ["▁", "<unk>", "e", "<unk>", "o", "!", "how", "▁", "<unk>", "re", "▁yo", "<unk>", "?"]
     # fmt: on

     A__    =      DebertaVaTokenizer(lowercase_						,	do_lower_case=lowercase_						,	split_by_punct=lowercase_							)
     A__    =      tokenizer.convert_ids_to_tokens(tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      DebertaVaTokenizerFast(lowercase_						,	do_lower_case=lowercase_						,	split_by_punct=lowercase_							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)

     self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[Any]:
     A__    =      self.get_tokenizer()
     A__    =      self.get_rust_tokenizer()

     A__    =      "I was born in 92000, and this is falsé."

     A__    =      tokenizer.convert_ids_to_tokens(tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)
     A__    =      rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      self.get_rust_tokenizer()
     A__    =      tokenizer.encode(lowercase_							)
     A__    =      rust_tokenizer.encode(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Any:
     A__    =      "This is a test"
     A__    =      [13, 1, 43_98, 25, 21, 12_89]
     A__    =      ["▁", "T", "his", "▁is", "▁a", "▁test"]
     A__    =      ["▁", "<unk>", "his", "▁is", "▁a", "▁test"]

     A__    =      DebertaVaTokenizer(lowercase_						,	keep_accents=lowercase_							)
     A__    =      DebertaVaTokenizerFast(lowercase_						,	keep_accents=lowercase_							)

     A__    =      tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
     A__    =      tokenizer.tokenize(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
     A__    =      tokenizer.convert_ids_to_tokens(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
     A__    =      rust_tokenizer.tokenize(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     # fmt: off
     A__    =      "I was born in 92000, and this is falsé."
     A__    =      [13, 1, 23, 3_86, 19, 5_61, 30_50, 15, 17, 48, 25, 82_56, 18, 1, 9]
     A__    =      ["▁", "I", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", ".", ]
     A__    =      ["▁", "<unk>", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", ".", ]
     # fmt: on

     A__    =      tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
     A__    =      tokenizer.tokenize(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
     A__    =      tokenizer.convert_ids_to_tokens(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
     A__    =      rust_tokenizer.tokenize(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
     A__    =      rust_tokenizer.convert_ids_to_tokens(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							List[Any]:
     A__    =      DebertaVaTokenizer(lowercase_							)

     A__    =      tokenizer.encode("sequence builders"							)
     A__    =      tokenizer.encode("multi-sequence build"							)

     A__    =      tokenizer.build_inputs_with_special_tokens(lowercase_							)
     A__    =      tokenizer.build_inputs_with_special_tokens(lowercase_						,	lowercase_							)

     self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]						,	lowercase_							)
     self.assertEqual(
         [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]						,	lowercase_						,	)



 @slow
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     # fmt: off
     A__    =      {"input_ids": [[1, 3_98_67, 36, 1_93_90, 4_86, 27, 3_50_52, 8_14_36, 18, 6_06_85, 12_25, 7, 3_50_52, 8_14_36, 18, 93_67, 1_68_99, 18, 1_59_37, 53, 5_94, 7_73, 18, 1_62_87, 3_04_65, 36, 1_59_37, 6, 4_11_39, 38, 3_69_79, 6_07_63, 1_91, 6, 3_41_32, 99, 6, 5_05_38, 3_90, 4_32_30, 6, 3_41_32, 27_79, 2_08_50, 14, 6_99, 10_72, 11_94, 36, 3_82, 1_09_01, 53, 7, 6_99, 10_72, 20_84, 36, 2_04_22, 6_30, 53, 19, 1_05, 30_49, 18_96, 10_53, 1_68_99, 15_06, 11, 3_79_78, 42_43, 7, 12_37, 3_18_69, 2_00, 1_65_66, 6_54, 6, 3_50_52, 8_14_36, 7, 5_56_30, 1_35_93, 4, 2], [1, 26, 1_50_11, 13, 6_67, 8, 10_53, 18, 2_36_11, 12_37, 7_23_56, 1_28_20, 34, 10_41_34, 12_09, 35, 1_33_13, 66_27, 21, 2_02, 3_47, 7, 1_64, 23_99, 11, 46, 44_85, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 12_32, 28_64, 1_57_85, 1_49_51, 1_05, 5, 85_81, 12_50, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowercase_						,	model_name="microsoft/deberta-v2-xlarge"						,	revision="ad6e42c1532ddf3a15c39246b63f5559d558b670"						,	)










'''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 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()
__lowerCAmelCase							:     Any		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple=False	):
    A__    =      []

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





def 						UpperCamelCase    (							_lowerCamelCase : Any	):
    A__    =      ["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(_lowerCamelCase						,	_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : Optional[int]						,	_lowerCamelCase : Union[str, Any]	):
    A__    =      dct.pop(_lowerCamelCase	)
    A__    =      val





def 						UpperCamelCase    (							):
    A__    =      "http://images.cocodataset.org/val2017/000000039769.jpg"
    A__    =      Image.open(requests.get(_lowerCamelCase						,	stream=_lowerCamelCase	).raw	)
    return im





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : int						,	_lowerCamelCase : int=False	):
    A__    =      BitConfig(
        global_padding="same"						,	layer_type="bottleneck"						,	depths=(3, 4, 9)						,	out_features=["stage3"]						,	embedding_dynamic_padding=_lowerCamelCase						,	)
    A__    =      ViTHybridConfig(backbone_config=_lowerCamelCase						,	image_size=3_84						,	num_labels=10_00	)
    A__    =      False

    # load original model from timm
    A__    =      timm.create_model(_lowerCamelCase						,	pretrained=_lowerCamelCase	)
    timm_model.eval()

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

    A__    =      "huggingface/label-files"
    A__    =      "imagenet-1k-id2label.json"
    A__    =      json.load(open(hf_hub_download(_lowerCamelCase						,	_lowerCamelCase						,	repo_type="dataset"	)						,	"r"	)	)
    A__    =      {int(_lowerCamelCase	): v for k, v in idalabel.items()}
    A__    =      idalabel
    A__    =      {v: k for k, v in idalabel.items()}

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        A__    =      ViTHybridModel(_lowerCamelCase	).eval()
    else:
        A__    =      ViTHybridForImageClassification(_lowerCamelCase	).eval()
    model.load_state_dict(_lowerCamelCase	)

    # create image processor
    A__    =      create_transform(**resolve_data_config({}						,	model=_lowerCamelCase	)	)
    A__    =      transform.transforms

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

    A__    =      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()						,	)

    A__    =      prepare_img()
    A__    =      transform(_lowerCamelCase	).unsqueeze(0	)
    A__    =      processor(_lowerCamelCase						,	return_tensors="pt"	).pixel_values

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

    # verify logits
    with torch.no_grad():
        A__    =      model(_lowerCamelCase	)
        A__    =      outputs.logits

    print("Predicted class:"						,	logits.argmax(-1	).item()	)
    if base_model:
        A__    =      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__    =      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__":
  __lowerCAmelCase							:     Dict		     =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."
  )

  __lowerCAmelCase							:     Optional[Any]		     =parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



