





'''simple docstring'''




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
 _SCREAMING_SNAKE_CASE							     =			"pt"
elif is_tf_available():
 _SCREAMING_SNAKE_CASE							     =			"tf"
else:
 _SCREAMING_SNAKE_CASE							     =			"jax"
class 			_lowerCAmelCase		(			__A							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   ByTaTokenizer
     snake_case_						      =   False



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	str:
        super().setUp()
        snake_case 						=   ByTaTokenizer()
        tokenizer.save_pretrained(self.tmpdirname						)



     @cached_property
     def      lowerCAmelCase			(   self							:			Dict						)->	Optional[Any]:
        return ByTaTokenizer.from_pretrained("""google/byt5-small"""						)



     def      lowerCAmelCase			(   self							:			str   ,       **__snake_case							:			Optional[int]						)->	Optional[int]:
        return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **snake_case_						)



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			Dict   ,       __snake_case							:			List[Any]=False   ,       __snake_case							:			Dict=20   ,       __snake_case							:			List[str]=5						)->	List[Any]:
        snake_case 						=   []
        for i in range(len(snake_case_						)						):
           try:
              snake_case 						=   tokenizer.decode([i]   ,       clean_up_tokenization_spaces=snake_case_						)
           except UnicodeDecodeError:
              pass
           toks.append((i, tok)						)

        snake_case 						=   list(filter(lambda __snake_case						: re.match(r"""^[ a-zA-Z]+$"""   ,       t[1]						)   ,       snake_case_						)						)
        snake_case 						=   list(filter(lambda __snake_case						: [t[0]] == tokenizer.encode(t[1]   ,       add_special_tokens=snake_case_						)   ,       snake_case_						)						)
        if max_length is not None and len(snake_case_						) > max_length:
           snake_case 						=   toks[:max_length]
        if min_length is not None and len(snake_case_						) < min_length and len(snake_case_						) > 0:
           while len(snake_case_						) < min_length:
              snake_case 						=   toks + toks
        # toks_str = [t[1] for t in toks]
        snake_case 						=   [t[0] for t in toks]

        # Ensure consistency
        snake_case 						=   tokenizer.decode(snake_case_   ,       clean_up_tokenization_spaces=snake_case_						)
        if " " not in output_txt and len(snake_case_						) > 1:
           snake_case 						=   (
               tokenizer.decode([toks_ids[0]]   ,       clean_up_tokenization_spaces=snake_case_						)
               + """ """
               + tokenizer.decode(toks_ids[1:]   ,       clean_up_tokenization_spaces=snake_case_						)
           )
        if with_prefix_space:
           snake_case 						=   """ """ + output_txt
        snake_case 						=   tokenizer.encode(snake_case_   ,       add_special_tokens=snake_case_						)
        return output_txt, output_ids



     def      lowerCAmelCase			(   self							:			List[Any]						)->	Tuple:
        snake_case 						=   self.ta_base_tokenizer
        snake_case 						=   tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]						)
        snake_case 						=   tokenizer(["""hi""", """I went to the gym""", """"""]						)
        self.assertListEqual(batch_with_eos_added["""input_ids"""]   ,       batch_without_eos_added["""input_ids"""]						)



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Dict:
        snake_case 						=   self.ta_base_tokenizer
        snake_case 						=   """Unicode €."""
        snake_case 						=   tokenizer(snake_case_						)
        snake_case 						=   [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
        self.assertEqual(encoded["""input_ids"""]   ,       snake_case_						)

        # decoding
        snake_case 						=   tokenizer.decode(snake_case_						)
        self.assertEqual(snake_case_   ,       """Unicode €.</s>"""						)

        snake_case 						=   tokenizer("""e è é ê ë"""						)
        snake_case 						=   [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
        self.assertEqual(encoded["""input_ids"""]   ,       snake_case_						)
        # decoding
        snake_case 						=   tokenizer.decode(snake_case_						)
        self.assertEqual(snake_case_   ,       """e è é ê ë</s>"""						)

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""						)						)   ,       """e è é ê ë</s>"""						)



     def      lowerCAmelCase			(   self							:			List[str]						)->	Dict:
        snake_case 						=   self.ta_base_tokenizer
        snake_case 						=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        # fmt: off
        snake_case 						=   [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
        # fmt: on
        snake_case 						=   tokenizer(snake_case_   ,       padding=snake_case_   ,       return_tensors=snake_case_						)
        self.assertIsInstance(snake_case_   ,       snake_case_						)

        if FRAMEWORK != "jax":
           snake_case 						=   list(batch.input_ids.numpy()[0]						)
        else:
           snake_case 						=   list(batch.input_ids.tolist()[0]						)

        self.assertListEqual(snake_case_   ,       snake_case_						)

        self.assertEqual((2, 37)   ,       batch.input_ids.shape						)
        self.assertEqual((2, 37)   ,       batch.attention_mask.shape						)



     def      lowerCAmelCase			(   self							:			str						)->	Dict:
        snake_case 						=   self.ta_base_tokenizer
        snake_case 						=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
        snake_case 						=   tokenizer(snake_case_   ,       padding=snake_case_   ,       return_tensors=snake_case_						)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn("""input_ids"""   ,       snake_case_						)
        self.assertIn("""attention_mask"""   ,       snake_case_						)
        self.assertNotIn("""decoder_input_ids"""   ,       snake_case_						)
        self.assertNotIn("""decoder_attention_mask"""   ,       snake_case_						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Dict:
        snake_case 						=   self.ta_base_tokenizer
        snake_case 						=   [
            """Summary of the text.""",
            """Another summary.""",
        ]
        snake_case 						=   tokenizer(
            text_target=snake_case_   ,       max_length=32   ,       padding="""max_length"""   ,       truncation=snake_case_   ,       return_tensors=snake_case_						)
        self.assertEqual(32   ,       targets["""input_ids"""].shape[1]						)



     def      lowerCAmelCase			(   self							:			int						)->	int:
        snake_case 						=   self.ta_base_tokenizer
        snake_case 						=   ["""A long paragraph for summarization. </s>"""]
        snake_case 						=   ["""Summary of the text. </s>"""]
        # fmt: off
        snake_case 						=   [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
        snake_case 						=   [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
        # fmt: on

        snake_case 						=   tokenizer(snake_case_   ,       text_target=snake_case_						)

        self.assertEqual(snake_case_   ,       batch["""input_ids"""][0]						)
        self.assertEqual(snake_case_   ,       batch["""labels"""][0]						)



     def      lowerCAmelCase			(   self							:			str						)->	List[str]:
        snake_case 						=   self.get_tokenizers()
        for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
              self.assertNotEqual(tokenizer.model_max_length   ,       42						)

        # Now let's start the test
        snake_case 						=   self.get_tokenizers()
        for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
              # Isolate this from the other tests because we save additional tokens/etc
              snake_case 						=   tempfile.mkdtemp()

              snake_case 						=   """ He is very happy, UNwant\u00E9d,running"""
              snake_case 						=   tokenizer.encode(snake_case_   ,       add_special_tokens=snake_case_						)
              tokenizer.save_pretrained(snake_case_						)

              snake_case 						=   tokenizer.__class__.from_pretrained(snake_case_						)
              snake_case 						=   after_tokenizer.encode(snake_case_   ,       add_special_tokens=snake_case_						)
              self.assertListEqual(snake_case_   ,       snake_case_						)

              shutil.rmtree(snake_case_						)

        snake_case 						=   self.get_tokenizers(model_max_length=42						)
        for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
              # Isolate this from the other tests because we save additional tokens/etc
              snake_case 						=   tempfile.mkdtemp()

              snake_case 						=   """ He is very happy, UNwant\u00E9d,running"""
              tokenizer.add_tokens(["""bim""", """bambam"""]						)
              snake_case 						=   tokenizer.additional_special_tokens
              additional_special_tokens.append("""new_additional_special_token"""						)
              tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}						)
              snake_case 						=   tokenizer.encode(snake_case_   ,       add_special_tokens=snake_case_						)
              tokenizer.save_pretrained(snake_case_						)

              snake_case 						=   tokenizer.__class__.from_pretrained(snake_case_						)
              snake_case 						=   after_tokenizer.encode(snake_case_   ,       add_special_tokens=snake_case_						)
              self.assertListEqual(snake_case_   ,       snake_case_						)
              self.assertIn("""new_additional_special_token"""   ,       after_tokenizer.additional_special_tokens						)
              self.assertEqual(after_tokenizer.model_max_length   ,       42						)

              snake_case 						=   tokenizer.__class__.from_pretrained(snake_case_   ,       model_max_length=43						)
              self.assertEqual(tokenizer.model_max_length   ,       43						)

              shutil.rmtree(snake_case_						)



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	int:
        snake_case 						=   []
        if self.test_slow_tokenizer:
           tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

        if self.test_rust_tokenizer:
           tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
           with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer_utils.save_pretrained(snake_case_						)

              with open(os.path.join(snake_case_   ,       """special_tokens_map.json"""						)   ,       encoding="""utf-8"""						) as json_file:
                 snake_case 						=   json.load(snake_case_						)

              with open(os.path.join(snake_case_   ,       """tokenizer_config.json"""						)   ,       encoding="""utf-8"""						) as json_file:
                 snake_case 						=   json.load(snake_case_						)

              snake_case 						=   [f'''<extra_id_{i}>''' for i in range(1_25						)]

              snake_case 						=   added_tokens_extra_ids + [
                  """an_additional_special_token"""
              ]
              snake_case 						=   added_tokens_extra_ids + [
                  """an_additional_special_token"""
              ]

              with open(os.path.join(snake_case_   ,       """special_tokens_map.json"""						)   ,       """w"""   ,       encoding="""utf-8"""						) as outfile:
                 json.dump(snake_case_   ,       snake_case_						)
              with open(os.path.join(snake_case_   ,       """tokenizer_config.json"""						)   ,       """w"""   ,       encoding="""utf-8"""						) as outfile:
                 json.dump(snake_case_   ,       snake_case_						)

              # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
              # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
              # "special_tokens_map.json" files
              snake_case 						=   tokenizer_class.from_pretrained(
                  snake_case_   ,       )
              self.assertIn(
                  """an_additional_special_token"""   ,       tokenizer_without_change_in_init.additional_special_tokens						)
              # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
              self.assertEqual(
                  ["""an_additional_special_token"""]   ,       tokenizer_without_change_in_init.convert_ids_to_tokens(
                      tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]						)						)   ,       )

              # Now we test that we can change the value of additional_special_tokens in the from_pretrained
              snake_case 						=   added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""   ,       lstrip=snake_case_						)]
              snake_case 						=   tokenizer_class.from_pretrained(
                  snake_case_   ,       additional_special_tokens=snake_case_   ,       )

              self.assertIn("""a_new_additional_special_token"""   ,       tokenizer.additional_special_tokens						)
              self.assertEqual(
                  ["""a_new_additional_special_token"""]   ,       tokenizer.convert_ids_to_tokens(
                      tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]						)						)   ,       )



     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   []
        if self.test_slow_tokenizer:
           tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

        if self.test_rust_tokenizer:
           tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

        for tokenizer_class, tokenizer_utils in tokenizer_list:
           with tempfile.TemporaryDirectory() as tmp_dir:
              tokenizer_utils.save_pretrained(snake_case_						)

              snake_case 						=   tokenizer_class.from_pretrained(snake_case_						)

              self.assertTrue(tokenizer.decode([2_55]						) == """"""						)



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Union[str, Any]:
        pass



     def      lowerCAmelCase			(   self							:			Tuple						)->	Dict:
        pass



     def      lowerCAmelCase			(   self							:			Tuple						)->	Optional[int]:
        pass



     def      lowerCAmelCase			(   self							:			Dict						)->	Dict:
        pass



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Union[str, Any]:
        snake_case 						=   self.get_tokenizers(fast=snake_case_   ,       do_lower_case=snake_case_						)
        for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
              snake_case 						=   ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
              snake_case 						=   tokenizer.convert_tokens_to_string(snake_case_						)

              self.assertIsInstance(snake_case_   ,       snake_case_						)



     def      lowerCAmelCase			(   self							:			Tuple						)->	Union[str, Any]:
        snake_case 						=   self.get_tokenizers()
        for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''						):
              snake_case 						=   [
                  """bos_token""",
                  """eos_token""",
                  """unk_token""",
                  """sep_token""",
                  """pad_token""",
                  """cls_token""",
                  """mask_token""",
              ]

              snake_case 						=   0
              snake_case 						=   tokenizer.convert_ids_to_tokens(
                  snake_case_   ,       skip_special_tokens=snake_case_						)

              for attr in attributes_list:
                 setattr(snake_case_   ,       attr + """_id"""   ,       snake_case_						)
                 self.assertEqual(getattr(snake_case_   ,       snake_case_						)   ,       snake_case_						)
                 self.assertEqual(getattr(snake_case_   ,       attr + """_id"""						)   ,       snake_case_						)

                 setattr(snake_case_   ,       attr + """_id"""   ,       snake_case_						)
                 self.assertEqual(getattr(snake_case_   ,       snake_case_						)   ,       snake_case_						)
                 self.assertEqual(getattr(snake_case_   ,       attr + """_id"""						)   ,       snake_case_						)

              setattr(snake_case_   ,       """additional_special_tokens_ids"""   ,       []						)
              self.assertListEqual(getattr(snake_case_   ,       """additional_special_tokens"""						)   ,       []						)
              self.assertListEqual(getattr(snake_case_   ,       """additional_special_tokens_ids"""						)   ,       []						)

              setattr(snake_case_   ,       """additional_special_tokens_ids"""   ,       [token_id_to_test_setters]						)
              self.assertListEqual(getattr(snake_case_   ,       """additional_special_tokens"""						)   ,       [token_to_test_setters]						)
              self.assertListEqual(getattr(snake_case_   ,       """additional_special_tokens_ids"""						)   ,       [token_id_to_test_setters]						)


"""simple docstring"""



import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)

def  _A(		lowerCAmelCase			,  lowerCAmelCase=False							):
							A__       :     Dict								=			[]
							for i in range(config.num_hidden_layers							):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F'''module.blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')							)
														rename_keys.append(
														    (F'''module.blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')							)
														rename_keys.append((F'''module.blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')							)
														rename_keys.append((F'''module.blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')							)
														rename_keys.append((F'''module.blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')							)
														rename_keys.append((F'''module.blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')							)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""module.cls_token""", """vit.embeddings.cls_token"""),
							        ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
							        ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
							        ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
							    ]							)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""module.norm.weight""", """layernorm.weight"""),
														        ("""module.norm.bias""", """layernorm.bias"""),
														    ]							)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														A__       :     Dict								=			[(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			,  lowerCAmelCase			,  lowerCAmelCase=False							):
							for i in range(config.num_hidden_layers							):
														if base_model:
																					A__       :     List[str]								=			""""""
														else:
																					A__       :     Dict								=			"""vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														A__       :     List[str]								=			state_dict.pop(F'''module.blocks.{i}.attn.qkv.weight'''							)
														A__       :     Tuple								=			state_dict.pop(F'''module.blocks.{i}.attn.qkv.bias'''							)
														# next, add query, keys and values (in that order) to the state dict
														A__       :     Tuple								=			in_proj_weight[
														    : config.hidden_size, :
														]
														A__       :     Union[str, Any]								=			in_proj_bias[: config.hidden_size]
														A__       :     List[str]								=			in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														A__       :     int								=			in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														A__       :     int								=			in_proj_weight[
														    -config.hidden_size :, :
														]
														A__       :     List[Any]								=			in_proj_bias[-config.hidden_size :]

def  _A(		lowerCAmelCase							):
							A__       :     str								=			["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(lowerCAmelCase			,  lowerCAmelCase							)

def  _A(		lowerCAmelCase							):
							# projection head is used in the self-supervised pre-training in MSN,
							# for downstream task it's not needed.
							A__       :     Dict								=			[
							    """module.fc.fc1.weight""",
							    """module.fc.fc1.bias""",
							    """module.fc.bn1.weight""",
							    """module.fc.bn1.bias""",
							    """module.fc.bn1.running_mean""",
							    """module.fc.bn1.running_var""",
							    """module.fc.bn1.num_batches_tracked""",
							    """module.fc.fc2.weight""",
							    """module.fc.fc2.bias""",
							    """module.fc.bn2.weight""",
							    """module.fc.bn2.bias""",
							    """module.fc.bn2.running_mean""",
							    """module.fc.bn2.running_var""",
							    """module.fc.bn2.num_batches_tracked""",
							    """module.fc.fc3.weight""",
							    """module.fc.fc3.bias""",
							]
							for k in ignore_keys:
														state_dict.pop(lowerCAmelCase			,  lowerCAmelCase							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[Any]								=			dct.pop(lowerCAmelCase							)
							A__       :     List[Any]								=			val

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			ViTMSNConfig()
							A__       :     List[str]								=			1000

							A__       :     Optional[int]								=			"""datasets/huggingface/label-files"""
							A__       :     Optional[int]								=			"""imagenet-1k-id2label.json"""
							A__       :     str								=			json.load(open(hf_hub_download(lowerCAmelCase			,  lowerCAmelCase							)			,  """r"""							)							)
							A__       :     Tuple								=			{int(lowerCAmelCase							): v for k, v in idalabel.items()}
							A__       :     Optional[Any]								=			idalabel
							A__       :     List[Any]								=			{v: k for k, v in idalabel.items()}

							if "s16" in checkpoint_url:
														A__       :     Tuple								=			384
														A__       :     int								=			1536
														A__       :     Union[str, Any]								=			6
							elif "l16" in checkpoint_url:
														A__       :     int								=			1024
														A__       :     int								=			4096
														A__       :     Any								=			24
														A__       :     int								=			16
														A__       :     Union[str, Any]								=			0.1
							elif "b4" in checkpoint_url:
														A__       :     Dict								=			4
							elif "l7" in checkpoint_url:
														A__       :     List[str]								=			7
														A__       :     Optional[int]								=			1024
														A__       :     Optional[int]								=			4096
														A__       :     Any								=			24
														A__       :     Union[str, Any]								=			16
														A__       :     Tuple								=			0.1

							A__       :     List[str]								=			ViTMSNModel(lowerCAmelCase							)

							A__       :     Any								=			torch.hub.load_state_dict_from_url(lowerCAmelCase			,  map_location="""cpu"""							)["""target_encoder"""]

							A__       :     List[str]								=			ViTImageProcessor(size=config.image_size							)

							remove_projection_head(lowerCAmelCase							)
							A__       :     List[str]								=			create_rename_keys(lowerCAmelCase			,  base_model=lowerCAmelCase							)

							for src, dest in rename_keys:
														rename_key(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
							read_in_q_k_v(lowerCAmelCase			,  lowerCAmelCase			,  base_model=lowerCAmelCase							)

							model.load_state_dict(lowerCAmelCase							)
							model.eval()

							A__       :     str								=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""

							A__       :     int								=			Image.open(requests.get(lowerCAmelCase			,  stream=lowerCAmelCase							).raw							)
							A__       :     List[Any]								=			ViTImageProcessor(
							    size=config.image_size			,  image_mean=lowerCAmelCase			,  image_std=lowerCAmelCase							)
							A__       :     Any								=			image_processor(images=lowerCAmelCase			,  return_tensors="""pt"""							)

							# forward pass
							torch.manual_seed(2							)
							A__       :     Tuple								=			model(**lowerCAmelCase							)
							A__       :     Union[str, Any]								=			outputs.last_hidden_state

							# The following Colab Notebook was used to generate these outputs:
							# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
							if "s16" in checkpoint_url:
														A__       :     Union[str, Any]								=			torch.tensor([[-1.0915, -1.4876, -1.1809]]							)
							elif "b16" in checkpoint_url:
														A__       :     int								=			torch.tensor([[14.2889, -18.9045, 11.7281]]							)
							elif "l16" in checkpoint_url:
														A__       :     List[Any]								=			torch.tensor([[41.5028, -22.8681, 45.6475]]							)
							elif "b4" in checkpoint_url:
														A__       :     Optional[Any]								=			torch.tensor([[-4.3868, 5.2932, -0.4137]]							)
							else:
														A__       :     str								=			torch.tensor([[-0.1792, -0.6465, 2.4263]]							)

							# verify logits
							assert torch.allclose(last_hidden_state[:, 0, :3]			,  lowerCAmelCase			,  atol=1E-4							)

							print(F'''Saving model to {pytorch_dump_folder_path}'''							)
							model.save_pretrained(lowerCAmelCase							)

							print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
							image_processor.save_pretrained(lowerCAmelCase							)


if __name__ == "__main__":
				_UpperCamelCase								=		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_url",
				    default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
				    type=str,
				    help="URL of the checkpoint 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_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





