





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
				from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
				UpperCAmelCase		:  Optional[Any]         =	get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
				from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase		:  Dict         =	12_80_22
UpperCAmelCase		:  Any         =	12_80_28





@require_sentencepiece
class 		_A(   snake_case__							,					unittest.TestCase  ):


				"""simple docstring"""


				UpperCamelCase       :    str          =      MaMaaaTokenizer
				UpperCamelCase       :    Union[str, Any]          =      False
				UpperCamelCase       :    Optional[int]          =      False
				UpperCamelCase       :    Union[str, Any]          =      True



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

									__A      :		List[Any]         =							['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
									__A      :		Dict         =							dict(zip(__UpperCamelCase	,   range(len(__UpperCamelCase	)	)	)	)
									__A      :		Union[str, Any]         =							Path(self.tmpdirname	)
									save_json(__UpperCamelCase	,   save_dir / VOCAB_FILES_NAMES['vocab_file']	)
									if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
														copyfile(__UpperCamelCase	,   save_dir / VOCAB_FILES_NAMES['spm_file']	)

									__A      :		str         =							MaMaaaTokenizer.from_pretrained(self.tmpdirname	)
									tokenizer.save_pretrained(self.tmpdirname	)



				def    UpperCAmelCase_						(     self	,   **_A	):
									return MaMaaaTokenizer.from_pretrained(self.tmpdirname	,   **__UpperCamelCase	)



				def    UpperCAmelCase_						(     self	,   _A	):
									return (
									    "This is a test",
									    "This is a test",
									)



				def    UpperCAmelCase_						(     self	):
									__A      :		Union[str, Any]         =							'</s>'
									__A      :		Any         =							0

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase	)	,   __UpperCamelCase	)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase	)	,   __UpperCamelCase	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							self.get_tokenizer()
									__A      :		int         =							list(tokenizer.get_vocab().keys()	)

									self.assertEqual(vocab_keys[0]	,   '</s>'	)
									self.assertEqual(vocab_keys[1]	,   '<unk>'	)
									self.assertEqual(vocab_keys[-1]	,   '<s>'	)
									self.assertEqual(len(__UpperCamelCase	)	,   tokenizer.vocab_size + len(tokenizer.get_added_vocab()	)	)



				@unittest.skip('Skip this test while all models are still to be uploaded.'	)
				def    UpperCAmelCase_						(     self	):
									pass



				def    UpperCAmelCase_						(     self	):
									__A      :		int         =							self.get_tokenizer()

									__A      :		Tuple         =							tokenizer.tokenize('This is a test'	)
									self.assertListEqual(__UpperCamelCase	,   ['▁This', '▁is', '▁a', '▁t', 'est']	)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(__UpperCamelCase	)	,   [2, 3, 4, 5, 6]	,   )

									__A      :		List[Any]         =							tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]	)
									self.assertListEqual(__UpperCamelCase	,   ['▁This', '▁is', '▁a', '▁t', 'est']	)

									__A      :		Optional[int]         =							tokenizer.convert_tokens_to_string(__UpperCamelCase	)
									self.assertEqual(__UpperCamelCase	,   'This is a test'	)





				@slow
				def    UpperCAmelCase_						(     self	):
									# fmt: off
									__A      :		Tuple         =							{'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], '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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=__UpperCamelCase	,   model_name='facebook/m2m100_418M'	,   revision='c168bae485c864188cf9aa0e4108b0b6934dc91e'	,   )





@require_torch
@require_sentencepiece
@require_tokenizers
class 		_A(   unittest.TestCase  ):


				"""simple docstring"""


				UpperCamelCase       :    Optional[int]          =      '''facebook/m2m100_418M'''
				UpperCamelCase       :    List[str]          =      [
				    '''In my opinion, there are two levels of response from the French government.''',
				    '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
				]
				UpperCamelCase       :    Optional[int]          =      [
				    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
				    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
				]

				# fmt: off
				UpperCamelCase       :    List[Any]          =      [EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]



				@classmethod
				def    UpperCAmelCase_						(     cls	):
									__A      :		Optional[int]         =							MaMaaaTokenizer.from_pretrained(
									    cls.checkpoint_name	,   src_lang='en'	,   tgt_lang='fr'	)
									__A      :		Tuple         =							1
									return cls



				def    UpperCAmelCase_						(     self	):
									self.assertEqual(self.tokenizer.get_lang_id('ar'	)	,   128006	)
									self.assertEqual(self.tokenizer.get_lang_id('en'	)	,   128022	)
									self.assertEqual(self.tokenizer.get_lang_id('ro'	)	,   128076	)
									self.assertEqual(self.tokenizer.get_lang_id('mr'	)	,   128063	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Optional[int]         =							self.tokenizer.get_vocab()
									self.assertEqual(len(__UpperCamelCase	)	,   self.tokenizer.vocab_size	)
									self.assertEqual(vocab['<unk>']	,   3	)
									self.assertIn(self.tokenizer.get_lang_token('en'	)	,   __UpperCamelCase	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							'en'
									__A      :		Any         =							self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
									self.assertListEqual(self.expected_src_tokens	,   __UpperCamelCase	)



				def    UpperCAmelCase_						(     self	):
									self.assertIn(__UpperCamelCase	,   self.tokenizer.all_special_ids	)
									# fmt: off
									__A      :		int         =							[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
									# fmt: on
									__A      :		Optional[Any]         =							self.tokenizer.decode(__UpperCamelCase	,   skip_special_tokens=__UpperCamelCase	)
									__A      :		Optional[int]         =							self.tokenizer.decode(generated_ids[1:]	,   skip_special_tokens=__UpperCamelCase	)
									self.assertEqual(__UpperCamelCase	,   __UpperCamelCase	)
									self.assertNotIn(self.tokenizer.eos_token	,   __UpperCamelCase	)



				def    UpperCAmelCase_						(     self	):
									__A      :		Any         =							tempfile.mkdtemp()
									__A      :		List[str]         =							self.tokenizer.lang_token_to_id
									self.tokenizer.save_pretrained(__UpperCamelCase	)
									__A      :		List[Any]         =							MaMaaaTokenizer.from_pretrained(__UpperCamelCase	)
									self.assertDictEqual(new_tok.lang_token_to_id	,   __UpperCamelCase	)



				@require_torch
				def    UpperCAmelCase_						(     self	):
									__A      :		Dict         =							'en'
									__A      :		Optional[Any]         =							'fr'

									__A      :		Any         =							self.tokenizer(self.src_text	,   text_target=self.tgt_text	,   padding=__UpperCamelCase	,   return_tensors='pt'	)

									__A      :		Tuple         =							shift_tokens_right(
									    batch['labels']	,   self.tokenizer.pad_token_id	,   self.tokenizer.eos_token_id	)

									for k in batch:
														__A      :		Optional[Any]         =							batch[k].tolist()
									# batch = {k: v.tolist() for k,v in batch.items()}
									# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
									# batch.decoder_inputs_ids[0][0] ==
									assert batch.input_ids[1][0] == EN_CODE
									assert batch.input_ids[1][-1] == 2
									assert batch.labels[1][0] == FR_CODE
									assert batch.labels[1][-1] == 2
									assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]



				@require_torch
				def    UpperCAmelCase_						(     self	):
									__A      :		Optional[Any]         =							'mr'
									self.assertListEqual(self.tokenizer.prefix_tokens	,   [self.tokenizer.get_lang_id('mr'	)]	)
									self.assertListEqual(self.tokenizer.suffix_tokens	,   [self.tokenizer.eos_token_id]	)

									__A      :		str         =							'zh'
									self.assertListEqual(self.tokenizer.prefix_tokens	,   [self.tokenizer.get_lang_id('zh'	)]	)
									self.assertListEqual(self.tokenizer.suffix_tokens	,   [self.tokenizer.eos_token_id]	)



				@require_torch
				def    UpperCAmelCase_						(     self	):
									__A      :		str         =							'mr'
									self.tokenizer._switch_to_target_mode()
									self.assertListEqual(self.tokenizer.prefix_tokens	,   [self.tokenizer.get_lang_id('mr'	)]	)
									self.assertListEqual(self.tokenizer.suffix_tokens	,   [self.tokenizer.eos_token_id]	)
									self.tokenizer._switch_to_input_mode()
									self.assertListEqual(self.tokenizer.prefix_tokens	,   [self.tokenizer.get_lang_id(self.tokenizer.src_lang	)]	)

									__A      :		Any         =							'zh'
									self.tokenizer._switch_to_target_mode()
									self.assertListEqual(self.tokenizer.prefix_tokens	,   [self.tokenizer.get_lang_id('zh'	)]	)
									self.assertListEqual(self.tokenizer.suffix_tokens	,   [self.tokenizer.eos_token_id]	)
									self.tokenizer._switch_to_input_mode()
									self.assertListEqual(self.tokenizer.prefix_tokens	,   [self.tokenizer.get_lang_id(self.tokenizer.src_lang	)]	)





				@require_torch
				def    UpperCAmelCase_						(     self	):
									__A      :		Optional[Any]         =							self.tokenizer._build_translation_inputs('A test'	,   return_tensors='pt'	,   src_lang='en'	,   tgt_lang='ar'	)

									self.assertEqual(
									    nested_simplify(__UpperCamelCase	)	,   {
									        # en_XX, A, test, EOS
									        'input_ids': [[128022, 58, 4183, 2]],
									        'attention_mask': [[1, 1, 1, 1]],
									        # ar_AR
									        'forced_bos_token_id': 128006,
									    }	,   )






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase   =	logging.get_logger(__name__)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Tuple=False							)	->     Optional[Any]:
							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"""),
														    ]							)

														# 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   __lowerCAmelCase    (				UpperCAmelCase__					:   List[str]      ,					UpperCAmelCase__					:   Optional[Any]      ,					UpperCAmelCase__					:   Any=False							)	->     int:
							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   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     str:
							lowerCamelCase_					=       ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(UpperCAmelCase__      ,					UpperCAmelCase__							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Dict      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   int							)	->     Tuple:
							lowerCamelCase_					=       dct.pop(UpperCAmelCase__							)
							lowerCamelCase_					=       val



def   __lowerCAmelCase    (				)	->     Any:
							lowerCamelCase_					=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_					=       Image.open(requests.get(UpperCAmelCase__      ,					stream=UpperCAmelCase__							).raw							)
							return im



@torch.no_grad()
def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Tuple=True							)	->     Dict:
							lowerCamelCase_					=       ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														lowerCamelCase_					=       8
							# set labels if required
							if not base_model:
														lowerCamelCase_					=       1_0_0_0
														lowerCamelCase_					=       """huggingface/label-files"""
														lowerCamelCase_					=       """imagenet-1k-id2label.json"""
														lowerCamelCase_					=       json.load(open(hf_hub_download(UpperCAmelCase__      ,					UpperCAmelCase__      ,					repo_type="""dataset"""							)      ,					"""r"""							)							)
														lowerCamelCase_					=       {int(UpperCAmelCase__							): v for k, v in idalabel.items()}
														lowerCamelCase_					=       idalabel
														lowerCamelCase_					=       {v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														lowerCamelCase_					=       3_8_4
														lowerCamelCase_					=       1_5_3_6
														lowerCamelCase_					=       1_2
														lowerCamelCase_					=       6

							# load original model from torch hub
							lowerCamelCase_					=       torch.hub.load("""facebookresearch/dino:main"""      ,					UpperCAmelCase__							)
							original_model.eval()

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

							# load HuggingFace model
							if base_model:
														lowerCamelCase_					=       ViTModel(UpperCAmelCase__      ,					add_pooling_layer=UpperCAmelCase__							).eval()
							else:
														lowerCamelCase_					=       ViTForImageClassification(UpperCAmelCase__							).eval()
							model.load_state_dict(UpperCAmelCase__							)

							# Check outputs on an image, prepared by ViTImageProcessor
							lowerCamelCase_					=       ViTImageProcessor()
							lowerCamelCase_					=       image_processor(images=prepare_img()      ,					return_tensors="""pt"""							)
							lowerCamelCase_					=       encoding["""pixel_values"""]
							lowerCamelCase_					=       model(UpperCAmelCase__							)

							if base_model:
														lowerCamelCase_					=       original_model(UpperCAmelCase__							)
														assert torch.allclose(UpperCAmelCase__      ,					outputs.last_hidden_state[:, 0, :]      ,					atol=1e-1							)
							else:
														lowerCamelCase_					=       original_model(UpperCAmelCase__							)
														assert logits.shape == outputs.logits.shape
														assert torch.allclose(UpperCAmelCase__      ,					outputs.logits      ,					atol=1e-3							)

							Path(UpperCAmelCase__							).mkdir(exist_ok=UpperCAmelCase__							)
							print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
							model.save_pretrained(UpperCAmelCase__							)
							print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
							image_processor.save_pretrained(UpperCAmelCase__							)


if __name__ == "__main__":
		lowercase   =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''dino_vitb16''',
		    type=str,
		    help='''Name of the model trained with DINO 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(
		    '''--base_model''',
		    action='''store_true''',
		    help='''Whether to only convert the base model (no projection head weights).''',
		)

		parser.set_defaults(base_model=True)
		lowercase   =	parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





