
"""simple docstring"""







import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__           =	get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCamelCase__           =	250_004
lowerCamelCase__           =	250_020




@require_sentencepiece
@require_tokenizers
class 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: List[str]            =			MBartTokenizer
		A_		: Union[str, Any]            =			MBartTokenizerFast
		A_		: Any            =			True
		A_		: int            =			True

		def 							__lowerCamelCase							(   self				):
			super().setUp()

			# We have a SentencePiece fixture for testing
			__lowerCAmelCase    :		Tuple		    =   MBartTokenizer(_SCREAMING_SNAKE_CASE ,	keep_accents=_SCREAMING_SNAKE_CASE				)
			tokenizer.save_pretrained(self.tmpdirname				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   MBartTokenizer(_SCREAMING_SNAKE_CASE ,	keep_accents=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   tokenizer.tokenize('This is a test'				)
			self.assertListEqual(_SCREAMING_SNAKE_CASE ,	['▁This', '▁is', '▁a', '▁t', 'est']				)

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				) ,	[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,	)

			__lowerCAmelCase    :		Any		    =   tokenizer.tokenize('I was born in 92000, and this is falsé.'				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[
			        SPIECE_UNDERLINE + 'I',
			        SPIECE_UNDERLINE + 'was',
			        SPIECE_UNDERLINE + 'b',
			        'or',
			        'n',
			        SPIECE_UNDERLINE + 'in',
			        SPIECE_UNDERLINE + '',
			        '9',
			        '2',
			        '0',
			        '0',
			        '0',
			        ',',
			        SPIECE_UNDERLINE + 'and',
			        SPIECE_UNDERLINE + 'this',
			        SPIECE_UNDERLINE + 'is',
			        SPIECE_UNDERLINE + 'f',
			        'al',
			        's',
			        'é',
			        '.',
			    ] ,	)
			__lowerCAmelCase    :		str		    =   tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[
			        value + tokenizer.fairseq_offset
			        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
			        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
			    ] ,	)

			__lowerCAmelCase    :		List[str]		    =   tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				)
			self.assertListEqual(
			    _SCREAMING_SNAKE_CASE ,	[
			        SPIECE_UNDERLINE + 'I',
			        SPIECE_UNDERLINE + 'was',
			        SPIECE_UNDERLINE + 'b',
			        'or',
			        'n',
			        SPIECE_UNDERLINE + 'in',
			        SPIECE_UNDERLINE + '',
			        '<unk>',
			        '2',
			        '0',
			        '0',
			        '0',
			        ',',
			        SPIECE_UNDERLINE + 'and',
			        SPIECE_UNDERLINE + 'this',
			        SPIECE_UNDERLINE + 'is',
			        SPIECE_UNDERLINE + 'f',
			        'al',
			        's',
			        '<unk>',
			        '.',
			    ] ,	)







		def 							__lowerCamelCase							(   self				):
			if not self.test_slow_tokenizer:
				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
				return

			__lowerCAmelCase    :		Union[str, Any]		    =   (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
				with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
					__lowerCAmelCase    :		Dict		    =   self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		int		    =   self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		Optional[int]		    =   tempfile.mkdtemp()

					__lowerCAmelCase    :		int		    =   tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Dict		    =   tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

					# Checks it save with the same files + the tokenizer.json file for the fast one
					self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)
					__lowerCAmelCase    :		List[Any]		    =   tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f				)
					self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					# Checks everything loads correctly in the same way
					__lowerCAmelCase    :		Any		    =   tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Dict		    =   tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)
						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

					shutil.rmtree(_SCREAMING_SNAKE_CASE				)

					# Save tokenizer rust, legacy_format=True
					__lowerCAmelCase    :		List[str]		    =   tempfile.mkdtemp()

					__lowerCAmelCase    :		Tuple		    =   tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,	legacy_format=_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Optional[Any]		    =   tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

					# Checks it save with the same files
					self.assertSequenceEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

					# Checks everything loads correctly in the same way
					__lowerCAmelCase    :		Any		    =   tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		str		    =   tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

					shutil.rmtree(_SCREAMING_SNAKE_CASE				)

					# Save tokenizer rust, legacy_format=False
					__lowerCAmelCase    :		List[Any]		    =   tempfile.mkdtemp()

					__lowerCAmelCase    :		Optional[Any]		    =   tokenizer_r.save_pretrained(_SCREAMING_SNAKE_CASE ,	legacy_format=_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Union[str, Any]		    =   tokenizer_p.save_pretrained(_SCREAMING_SNAKE_CASE				)

					# Checks it saved the tokenizer.json file
					self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files				)				)

					# Checks everything loads correctly in the same way
					__lowerCAmelCase    :		Union[str, Any]		    =   tokenizer_r.from_pretrained(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Any		    =   tokenizer_p.from_pretrained(_SCREAMING_SNAKE_CASE				)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)				)

					shutil.rmtree(_SCREAMING_SNAKE_CASE				)




@require_torch
@require_sentencepiece
@require_tokenizers
class 				A__ (			unittest.TestCase):
		A_		: Union[str, Any]            =			'facebook/mbart-large-en-ro'
		A_		: List[Any]            =			[
		    ' UN Chief Says There Is No Military Solution in Syria',
		    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
		]
		A_		: Optional[int]            =			[
		    'Şeful ONU declară că nu există o soluţie militară în Siria',
		    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
		    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
		    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
		]
		A_		: Dict            =			[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]

		@classmethod
		def 							__lowerCamelCase							(   cls				):
			__lowerCAmelCase    :		MBartTokenizer		    =   MBartTokenizer.from_pretrained(
			    cls.checkpoint_name ,	src_lang='en_XX' ,	tgt_lang='ro_RO'				)
			__lowerCAmelCase    :		List[Any]		    =   1
			return cls

		def 							__lowerCamelCase							(   self				):
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] ,	25_00_01				)
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] ,	25_00_04				)
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] ,	25_00_20				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
			self.assertListEqual(self.expected_src_tokens ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self.assertIn(_SCREAMING_SNAKE_CASE ,	self.tokenizer.all_special_ids				)
			__lowerCAmelCase    :		Union[str, Any]		    =   [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
			__lowerCAmelCase    :		List[str]		    =   self.tokenizer.decode(_SCREAMING_SNAKE_CASE ,	skip_special_tokens=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.tokenizer.decode(generated_ids[1:] ,	skip_special_tokens=_SCREAMING_SNAKE_CASE				)
			self.assertEqual(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			self.assertNotIn(self.tokenizer.eos_token ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   ['this is gunna be a long sentence ' * 20]
			assert isinstance(src_text[0] ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		int		    =   10
			__lowerCAmelCase    :		int		    =   self.tokenizer(_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE				).input_ids[0]
			self.assertEqual(ids[-2] ,	2				)
			self.assertEqual(ids[-1] ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(len(_SCREAMING_SNAKE_CASE				) ,	_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']				) ,	[25_00_26, 25_00_01]				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   tempfile.mkdtemp()
			__lowerCAmelCase    :		Tuple		    =   self.tokenizer.fairseq_tokens_to_ids
			self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   MBartTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
			self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,	_SCREAMING_SNAKE_CASE				)

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.tokenizer(self.src_text ,	text_target=self.tgt_text ,	padding=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				)
			__lowerCAmelCase    :		Optional[int]		    =   shift_tokens_right(batch['labels'] ,	self.tokenizer.pad_token_id				)

			# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
			assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
			assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
			assert batch.decoder_input_ids[1][-1] == 2
			assert batch.labels[1][-2:].tolist() == [2, RO_CODE]

		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.tokenizer(
			    self.src_text ,	text_target=self.tgt_text ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=len(self.expected_src_tokens				) ,	return_tensors='pt' ,	)

			__lowerCAmelCase    :		Dict		    =   shift_tokens_right(batch['labels'] ,	self.tokenizer.pad_token_id				)

			self.assertIsInstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			self.assertEqual((2, 14) ,	batch.input_ids.shape				)
			self.assertEqual((2, 14) ,	batch.attention_mask.shape				)
			__lowerCAmelCase    :		Union[str, Any]		    =   batch.input_ids.tolist()[0]
			self.assertListEqual(self.expected_src_tokens ,	_SCREAMING_SNAKE_CASE				)
			self.assertEqual(2 ,	batch.decoder_input_ids[0, -1]				)  # EOS
			# Test that special tokens are reset
			self.assertEqual(self.tokenizer.prefix_tokens ,	[]				)
			self.assertEqual(self.tokenizer.suffix_tokens ,	[self.tokenizer.eos_token_id, EN_CODE]				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   self.tokenizer(self.src_text ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=3 ,	return_tensors='pt'				)
			__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
			    text_target=self.tgt_text ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=10 ,	return_tensors='pt'				)
			__lowerCAmelCase    :		Dict		    =   targets['input_ids']
			__lowerCAmelCase    :		Optional[Any]		    =   shift_tokens_right(_SCREAMING_SNAKE_CASE ,	self.tokenizer.pad_token_id				)

			self.assertEqual(batch.input_ids.shape[1] ,	3				)
			self.assertEqual(batch.decoder_input_ids.shape[1] ,	10				)







		@require_torch
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.tokenizer._build_translation_inputs(
			    'A test' ,	return_tensors='pt' ,	src_lang='en_XX' ,	tgt_lang='ar_AR'				)

			self.assertEqual(
			    nested_simplify(_SCREAMING_SNAKE_CASE				) ,	{
			        # A, test, EOS, en_XX
			        'input_ids': [[62, 30_34, 2, 25_00_04]],
			        'attention_mask': [[1, 1, 1, 1]],
			        # ar_AR
			        'forced_bos_token_id': 25_00_01,
			    } ,	)

"""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__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[Any]		    =   []

	# 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"
		__lowerCAmelCase    :		Optional[Any]		    =   [(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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	for i in range(config.num_hidden_layers	):
		if base_model:
			__lowerCAmelCase    :		Tuple		    =   ''
		else:
			__lowerCAmelCase    :		int		    =   'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__lowerCAmelCase    :		int		    =   state_dict.pop(F"blocks.{i}.attn.qkv.weight"	)
		__lowerCAmelCase    :		Dict		    =   state_dict.pop(F"blocks.{i}.attn.qkv.bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		Tuple		    =   in_proj_weight[
		    : config.hidden_size, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[: config.hidden_size]
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__lowerCAmelCase    :		str		    =   in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_weight[
		    -config.hidden_size :, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[-config.hidden_size :]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(_UpperCamelCase ,						_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   dct.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		List[Any]		    =   val
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Optional[int]		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase    :		Optional[Any]		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)
	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[str]		    =   BitConfig(
	    global_padding='same' ,						layer_type='bottleneck' ,						depths=(3, 4, 9) ,						out_features=['stage3'] ,						embedding_dynamic_padding=_UpperCamelCase ,						)
	__lowerCAmelCase    :		Tuple		    =   ViTHybridConfig(backbone_config=_UpperCamelCase ,						image_size=384 ,						num_labels=1000	)
	__lowerCAmelCase    :		Optional[Any]		    =   False

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

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

	__lowerCAmelCase    :		int		    =   'huggingface/label-files'
	__lowerCAmelCase    :		str		    =   'imagenet-1k-id2label.json'
	__lowerCAmelCase    :		Any		    =   json.load(open(hf_hub_download(_UpperCamelCase ,						_UpperCamelCase ,						repo_type='dataset'	) ,						'r'	)	)
	__lowerCAmelCase    :		int		    =   {int(_UpperCamelCase	): v for k, v in idalabel.items()}
	__lowerCAmelCase    :		List[str]		    =   idalabel
	__lowerCAmelCase    :		Optional[Any]		    =   {v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__lowerCAmelCase    :		str		    =   ViTHybridModel(_UpperCamelCase	).eval()
	else:
		__lowerCAmelCase    :		Optional[Any]		    =   ViTHybridForImageClassification(_UpperCamelCase	).eval()
	model.load_state_dict(_UpperCamelCase	)

	# create image processor
	__lowerCAmelCase    :		Union[str, Any]		    =   create_transform(**resolve_data_config({} ,						model=_UpperCamelCase	)	)
	__lowerCAmelCase    :		Optional[int]		    =   transform.transforms

	__lowerCAmelCase    :		List[str]		    =   {
	    'bilinear': PILImageResampling.BILINEAR,
	    'bicubic': PILImageResampling.BICUBIC,
	    'nearest': PILImageResampling.NEAREST,
	}

	__lowerCAmelCase    :		List[str]		    =   ViTHybridImageProcessor(
	    do_resize=_UpperCamelCase ,						size={'shortest_edge': timm_transforms[0].size} ,						resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,						do_center_crop=_UpperCamelCase ,						crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} ,						do_normalize=_UpperCamelCase ,						image_mean=timm_transforms[-1].mean.tolist() ,						image_std=timm_transforms[-1].std.tolist() ,						)

	__lowerCAmelCase    :		Optional[Any]		    =   prepare_img()
	__lowerCAmelCase    :		Union[str, Any]		    =   transform(_UpperCamelCase	).unsqueeze(0	)
	__lowerCAmelCase    :		Union[str, Any]		    =   processor(_UpperCamelCase ,						return_tensors='pt'	).pixel_values

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

	# verify logits
	with torch.no_grad():
		__lowerCAmelCase    :		Any		    =   model(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   outputs.logits

	print('Predicted class:' ,						logits.argmax(-1	).item()	)
	if base_model:
		__lowerCAmelCase    :		Optional[int]		    =   timm_model.forward_features(_UpperCamelCase	)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(_UpperCamelCase ,						outputs.pooler_output ,						atol=1e-3	)
	else:
		__lowerCAmelCase    :		List[str]		    =   timm_model(_UpperCamelCase	)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_UpperCamelCase ,						outputs.logits ,						atol=1e-3	)
	print('Looks ok!'	)

	if pytorch_dump_folder_path is not None:
		Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
		print(F"Saving model {vit_name} to {pytorch_dump_folder_path}"	)
		model.save_pretrained(_UpperCamelCase	)
		print(F"Saving processor to {pytorch_dump_folder_path}"	)
		processor.save_pretrained(_UpperCamelCase	)

	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__           =	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__           =	parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)