





"""simple docstring"""





import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
  from test_module.custom_tokenization_fast import CustomTokenizerFast




class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						0

      @slow
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
          __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained(__lowercase )
          self.assertIsNotNone(__lowercase )
          self.assertIsInstance(__lowercase						,				(BertTokenizer, BertTokenizerFast) )
          self.assertGreater(len(__lowercase )						,				0 )

        for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
          __UpperCAmelCase    :						List[Any]       =						AutoTokenizer.from_pretrained(__lowercase )
          self.assertIsNotNone(__lowercase )
          self.assertIsInstance(__lowercase						,				(GPTaTokenizer, GPTaTokenizerFast) )
          self.assertGreater(len(__lowercase )						,				0 )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						AutoTokenizer.from_pretrained(__lowercase )
        self.assertIsInstance(__lowercase						,				(BertTokenizer, BertTokenizerFast) )
        self.assertEqual(tokenizer.vocab_size						,				12 )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained(__lowercase )
        self.assertIsInstance(__lowercase						,				(RobertaTokenizer, RobertaTokenizerFast) )
        self.assertEqual(tokenizer.vocab_size						,				20 )

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						AutoConfig.from_pretrained(__lowercase )
        self.assertIsInstance(__lowercase						,				__lowercase )
        # Check that tokenizer_type ≠ model_type
        __UpperCAmelCase    :						Tuple       =						AutoTokenizer.from_pretrained(__lowercase						,				config=__lowercase )
        self.assertIsInstance(__lowercase						,				(BertTokenizer, BertTokenizerFast) )
        self.assertEqual(tokenizer.vocab_size						,				12 )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmp_dir:
          shutil.copy('''./tests/fixtures/vocab.txt'''						,				os.path.join(__lowercase						,				'''vocab.txt''' ) )

          __UpperCAmelCase    :						Dict       =						AutoTokenizer.from_pretrained(__lowercase						,				tokenizer_type='''bert'''						,				use_fast=__lowercase )
          self.assertIsInstance(__lowercase						,				__lowercase )

        with tempfile.TemporaryDirectory() as tmp_dir:
          shutil.copy('''./tests/fixtures/vocab.json'''						,				os.path.join(__lowercase						,				'''vocab.json''' ) )
          shutil.copy('''./tests/fixtures/merges.txt'''						,				os.path.join(__lowercase						,				'''merges.txt''' ) )

          __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained(__lowercase						,				tokenizer_type='''gpt2'''						,				use_fast=__lowercase )
          self.assertIsInstance(__lowercase						,				__lowercase )

      @require_tokenizers
      def       A_					(							self       :      str ):


        '''simple docstring'''




        with tempfile.TemporaryDirectory() as tmp_dir:
          shutil.copy('''./tests/fixtures/vocab.txt'''						,				os.path.join(__lowercase						,				'''vocab.txt''' ) )

          __UpperCAmelCase    :						Dict       =						AutoTokenizer.from_pretrained(__lowercase						,				tokenizer_type='''bert''' )
          self.assertIsInstance(__lowercase						,				__lowercase )

        with tempfile.TemporaryDirectory() as tmp_dir:
          shutil.copy('''./tests/fixtures/vocab.json'''						,				os.path.join(__lowercase						,				'''vocab.json''' ) )
          shutil.copy('''./tests/fixtures/merges.txt'''						,				os.path.join(__lowercase						,				'''merges.txt''' ) )

          __UpperCAmelCase    :						Optional[int]       =						AutoTokenizer.from_pretrained(__lowercase						,				tokenizer_type='''gpt2''' )
          self.assertIsInstance(__lowercase						,				__lowercase )

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        with pytest.raises(__lowercase ):
          AutoTokenizer.from_pretrained('''./'''						,				tokenizer_type='''xxx''' )

      @require_tokenizers
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
          __UpperCAmelCase    :						List[Any]       =						tokenizer_class.from_pretrained('''wietsedv/bert-base-dutch-cased''' )
          self.assertIsInstance(__lowercase						,				(BertTokenizer, BertTokenizerFast) )

          if isinstance(__lowercase						,				__lowercase ):
            self.assertEqual(tokenizer.basic_tokenizer.do_lower_case						,				__lowercase )
          else:
            self.assertEqual(tokenizer.do_lower_case						,				__lowercase )

          self.assertEqual(tokenizer.model_max_length						,				512 )

      @require_tokenizers
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
          with self.assertRaisesRegex(
              __lowercase						,				'''julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier'''						,				):
            __UpperCAmelCase    :						List[str]       =						tokenizer_class.from_pretrained('''julien-c/herlolip-not-exists''' )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						TOKENIZER_MAPPING.values()
        __UpperCAmelCase    :						str       =						[]

        for slow_tok, fast_tok in tokenizers:
          if slow_tok is not None:
            tokenizer_names.append(slow_tok.__name__ )

          if fast_tok is not None:
            tokenizer_names.append(fast_tok.__name__ )

        for tokenizer_name in tokenizer_names:
          # must find the right class
          tokenizer_class_from_name(__lowercase )

      @require_tokenizers
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased'''						,				use_fast=__lowercase )						,				__lowercase )
        self.assertIsInstance(AutoTokenizer.from_pretrained('''bert-base-cased''' )						,				__lowercase )

      @require_tokenizers
      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[Any]       =						AutoTokenizer.from_pretrained('''distilbert-base-uncased'''						,				do_lower_case=__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						'''Hello, world. How are you?'''
        __UpperCAmelCase    :						Optional[int]       =						tokenizer.tokenize(__lowercase )
        self.assertEqual('''[UNK]'''						,				tokens[0] )

        __UpperCAmelCase    :						List[Any]       =						AutoTokenizer.from_pretrained('''microsoft/mpnet-base'''						,				do_lower_case=__lowercase )
        __UpperCAmelCase    :						Optional[int]       =						tokenizer.tokenize(__lowercase )
        self.assertEqual('''[UNK]'''						,				tokens[0] )

      @require_tokenizers
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config''' )
        self.assertEqual(type(__lowercase )						,				__lowercase )
        self.assertEqual(tokenizer.model_max_length						,				512 )
        self.assertEqual(tokenizer.vocab_size						,				30_000 )
        self.assertEqual(tokenizer.unk_token						,				'''[UNK]''' )
        self.assertEqual(tokenizer.padding_side						,				'''right''' )
        self.assertEqual(tokenizer.truncation_side						,				'''right''' )

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						AutoTokenizer.from_pretrained(__lowercase )
        self.assertIsInstance(__lowercase						,				(BertTokenizer, BertTokenizerFast) )
        with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer.save_pretrained(__lowercase )
          __UpperCAmelCase    :						List[Any]       =						AutoTokenizer.from_pretrained(__lowercase )

        self.assertIsInstance(__lowercase						,				tokenizer.__class__ )
        self.assertEqual(tokenizera.vocab_size						,				12 )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						AutoTokenizer.from_pretrained('''ctrl''' )
        # There is no fast CTRL so this always gives us a slow tokenizer.
        self.assertIsInstance(__lowercase						,				__lowercase )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						get_tokenizer_config('''bert-base-cased''' )
        __UpperCAmelCase    :						List[str]       =						config.pop('''_commit_hash'''						,				__lowercase )
        # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
        self.assertEqual(__lowercase						,				{'''do_lower_case''': False} )

        # This model does not have a tokenizer_config so we get back an empty dict.
        __UpperCAmelCase    :						Union[str, Any]       =						get_tokenizer_config(__lowercase )
        self.assertDictEqual(__lowercase						,				{} )

        # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
        __UpperCAmelCase    :						Any       =						AutoTokenizer.from_pretrained(__lowercase )
        with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer.save_pretrained(__lowercase )
          __UpperCAmelCase    :						List[str]       =						get_tokenizer_config(__lowercase )

        # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
        self.assertEqual(config['''tokenizer_class''']						,				'''BertTokenizer''' )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        try:
          AutoConfig.register('''custom'''						,				__lowercase )

          AutoTokenizer.register(__lowercase						,				slow_tokenizer_class=__lowercase )
          # Trying to register something existing in the Transformers library will raise an error
          with self.assertRaises(__lowercase ):
            AutoTokenizer.register(__lowercase						,				slow_tokenizer_class=__lowercase )

          __UpperCAmelCase    :						Tuple       =						CustomTokenizer.from_pretrained(__lowercase )
          with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(__lowercase )

            __UpperCAmelCase    :						Any       =						AutoTokenizer.from_pretrained(__lowercase )
            self.assertIsInstance(__lowercase						,				__lowercase )

        finally:
          if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
          if CustomConfig in TOKENIZER_MAPPING._extra_content:
            del TOKENIZER_MAPPING._extra_content[CustomConfig]

      @require_tokenizers
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        try:
          AutoConfig.register('''custom'''						,				__lowercase )

          # Can register in two steps
          AutoTokenizer.register(__lowercase						,				slow_tokenizer_class=__lowercase )
          self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						,				(CustomTokenizer, None) )
          AutoTokenizer.register(__lowercase						,				fast_tokenizer_class=__lowercase )
          self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						,				(CustomTokenizer, CustomTokenizerFast) )

          del TOKENIZER_MAPPING._extra_content[CustomConfig]
          # Can register in one step
          AutoTokenizer.register(
              __lowercase						,				slow_tokenizer_class=__lowercase						,				fast_tokenizer_class=__lowercase )
          self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						,				(CustomTokenizer, CustomTokenizerFast) )

          # Trying to register something existing in the Transformers library will raise an error
          with self.assertRaises(__lowercase ):
            AutoTokenizer.register(__lowercase						,				fast_tokenizer_class=__lowercase )

          # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
          # and that model does not have a tokenizer.json
          with tempfile.TemporaryDirectory() as tmp_dir:
            __UpperCAmelCase    :						Any       =						BertTokenizerFast.from_pretrained(__lowercase )
            bert_tokenizer.save_pretrained(__lowercase )
            __UpperCAmelCase    :						Optional[Any]       =						CustomTokenizerFast.from_pretrained(__lowercase )

          with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(__lowercase )

            __UpperCAmelCase    :						Tuple       =						AutoTokenizer.from_pretrained(__lowercase )
            self.assertIsInstance(__lowercase						,				__lowercase )

            __UpperCAmelCase    :						Tuple       =						AutoTokenizer.from_pretrained(__lowercase						,				use_fast=__lowercase )
            self.assertIsInstance(__lowercase						,				__lowercase )

        finally:
          if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
          if CustomConfig in TOKENIZER_MAPPING._extra_content:
            del TOKENIZER_MAPPING._extra_content[CustomConfig]

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        with self.assertRaises(__lowercase ):
          __UpperCAmelCase    :						Union[str, Any]       =						AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' )
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(__lowercase ):
          __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained(
              '''hf-internal-testing/test_dynamic_tokenizer'''						,				trust_remote_code=__lowercase )

        __UpperCAmelCase    :						Dict       =						AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''						,				trust_remote_code=__lowercase )
        self.assertTrue(tokenizer.special_attribute_present )
        # Test tokenizer can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer.save_pretrained(__lowercase )
          __UpperCAmelCase    :						Any       =						AutoTokenizer.from_pretrained(__lowercase						,				trust_remote_code=__lowercase )
        self.assertTrue(reloaded_tokenizer.special_attribute_present )

        if is_tokenizers_available():
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizerFast''' )
          self.assertEqual(reloaded_tokenizer.__class__.__name__						,				'''NewTokenizerFast''' )

          # Test we can also load the slow version
          __UpperCAmelCase    :						Any       =						AutoTokenizer.from_pretrained(
              '''hf-internal-testing/test_dynamic_tokenizer'''						,				trust_remote_code=__lowercase						,				use_fast=__lowercase )
          self.assertTrue(tokenizer.special_attribute_present )
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizer''' )
          # Test tokenizer can be reloaded.
          with tempfile.TemporaryDirectory() as tmp_dir:
            tokenizer.save_pretrained(__lowercase )
            __UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained(__lowercase						,				trust_remote_code=__lowercase						,				use_fast=__lowercase )
          self.assertEqual(reloaded_tokenizer.__class__.__name__						,				'''NewTokenizer''' )
          self.assertTrue(reloaded_tokenizer.special_attribute_present )
        else:
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizer''' )
          self.assertEqual(reloaded_tokenizer.__class__.__name__						,				'''NewTokenizer''' )

      @require_tokenizers
      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        class 							snake_case							(			__UpperCAmelCase ):

              '''simple docstring'''



              _A     :       Optional[Any]         =       False




        class 							snake_case							(			__UpperCAmelCase ):

              '''simple docstring'''



              _A     :       Tuple         =       NewTokenizer
              _A     :       Dict         =       False

        try:
          AutoConfig.register('''custom'''						,				__lowercase )
          AutoTokenizer.register(__lowercase						,				slow_tokenizer_class=__lowercase )
          AutoTokenizer.register(__lowercase						,				fast_tokenizer_class=__lowercase )
          # If remote code is not set, the default is to use local
          __UpperCAmelCase    :						int       =						AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''' )
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizerFast''' )
          self.assertFalse(tokenizer.special_attribute_present )
          __UpperCAmelCase    :						int       =						AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''						,				use_fast=__lowercase )
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizer''' )
          self.assertFalse(tokenizer.special_attribute_present )

          # If remote code is disabled, we load the local one.
          __UpperCAmelCase    :						str       =						AutoTokenizer.from_pretrained(
              '''hf-internal-testing/test_dynamic_tokenizer'''						,				trust_remote_code=__lowercase )
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizerFast''' )
          self.assertFalse(tokenizer.special_attribute_present )
          __UpperCAmelCase    :						Optional[Any]       =						AutoTokenizer.from_pretrained(
              '''hf-internal-testing/test_dynamic_tokenizer'''						,				trust_remote_code=__lowercase						,				use_fast=__lowercase )
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizer''' )
          self.assertFalse(tokenizer.special_attribute_present )

          # If remote is enabled, we load from the Hub
          __UpperCAmelCase    :						int       =						AutoTokenizer.from_pretrained(
              '''hf-internal-testing/test_dynamic_tokenizer'''						,				trust_remote_code=__lowercase )
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizerFast''' )
          self.assertTrue(tokenizer.special_attribute_present )
          __UpperCAmelCase    :						Optional[int]       =						AutoTokenizer.from_pretrained(
              '''hf-internal-testing/test_dynamic_tokenizer'''						,				trust_remote_code=__lowercase						,				use_fast=__lowercase )
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizer''' )
          self.assertTrue(tokenizer.special_attribute_present )

        finally:
          if "custom" in CONFIG_MAPPING._extra_content:
            del CONFIG_MAPPING._extra_content["custom"]
          if CustomConfig in TOKENIZER_MAPPING._extra_content:
            del TOKENIZER_MAPPING._extra_content[CustomConfig]

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						AutoTokenizer.from_pretrained(
            '''hf-internal-testing/test_dynamic_tokenizer_legacy'''						,				trust_remote_code=__lowercase )
        self.assertTrue(tokenizer.special_attribute_present )
        if is_tokenizers_available():
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizerFast''' )

          # Test we can also load the slow version
          __UpperCAmelCase    :						Optional[int]       =						AutoTokenizer.from_pretrained(
              '''hf-internal-testing/test_dynamic_tokenizer_legacy'''						,				trust_remote_code=__lowercase						,				use_fast=__lowercase )
          self.assertTrue(tokenizer.special_attribute_present )
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizer''' )
        else:
          self.assertEqual(tokenizer.__class__.__name__						,				'''NewTokenizer''' )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        with self.assertRaisesRegex(
            __lowercase						,				'''bert-base is not a local folder and is not a valid model identifier''' ):
          __UpperCAmelCase    :						Optional[Any]       =						AutoTokenizer.from_pretrained('''bert-base''' )

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        with self.assertRaisesRegex(
            __lowercase						,				R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
          __UpperCAmelCase    :						Optional[Any]       =						AutoTokenizer.from_pretrained(__lowercase						,				revision='''aaaaaa''' )







      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
        with RequestCounter() as counter:
          __UpperCAmelCase    :						Tuple       =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
          self.assertEqual(counter.get_request_count						,				0 )
          self.assertEqual(counter.head_request_count						,				1 )
          self.assertEqual(counter.other_request_count						,				0 )






"""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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Tuple       =						SwinvaConfig()
  __UpperCAmelCase    :						Any       =						swinva_name.split('''_'''  )

  __UpperCAmelCase    :						Optional[Any]       =						name_split[1]
  if "to" in name_split[3]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[3][-3:]  )
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						int(name_split[3]  )
  if "to" in name_split[2]:
    __UpperCAmelCase    :						List[str]       =						int(name_split[2][-2:]  )
  else:
    __UpperCAmelCase    :						str       =						int(name_split[2][6:]  )

  if model_size == "tiny":
    __UpperCAmelCase    :						Union[str, Any]       =						96
    __UpperCAmelCase    :						str       =						(2, 2, 6, 2)
    __UpperCAmelCase    :						Union[str, Any]       =						(3, 6, 12, 24)
  elif model_size == "small":
    __UpperCAmelCase    :						List[Any]       =						96
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(3, 6, 12, 24)
  elif model_size == "base":
    __UpperCAmelCase    :						Any       =						1_28
    __UpperCAmelCase    :						int       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						Dict       =						(4, 8, 16, 32)
  else:
    __UpperCAmelCase    :						Union[str, Any]       =						1_92
    __UpperCAmelCase    :						Union[str, Any]       =						(2, 2, 18, 2)
    __UpperCAmelCase    :						str       =						(6, 12, 24, 48)

  if "to" in swinva_name:
    __UpperCAmelCase    :						Optional[int]       =						(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    __UpperCAmelCase    :						int       =						2_18_41
    __UpperCAmelCase    :						Optional[Any]       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Optional[Any]       =						'''imagenet-22k-id2label.json'''
    __UpperCAmelCase    :						Any       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						List[Any]       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Optional[Any]       =						idalabel
    __UpperCAmelCase    :						Optional[int]       =						{v: k for k, v in idalabel.items()}

  else:
    __UpperCAmelCase    :						List[Any]       =						10_00
    __UpperCAmelCase    :						str       =						'''huggingface/label-files'''
    __UpperCAmelCase    :						Tuple       =						'''imagenet-1k-id2label.json'''
    __UpperCAmelCase    :						Optional[int]       =						json.load(open(hf_hub_download(UpperCAmelCase_  ,			UpperCAmelCase_  ,			repo_type='''dataset'''  )  ,			'''r'''  )  )
    __UpperCAmelCase    :						str       =						{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}
    __UpperCAmelCase    :						Any       =						idalabel
    __UpperCAmelCase    :						List[Any]       =						{v: k for k, v in idalabel.items()}

  __UpperCAmelCase    :						Optional[int]       =						img_size
  __UpperCAmelCase    :						int       =						num_classes
  __UpperCAmelCase    :						Tuple       =						embed_dim
  __UpperCAmelCase    :						str       =						depths
  __UpperCAmelCase    :						Optional[Any]       =						num_heads
  __UpperCAmelCase    :						Optional[int]       =						window_size

  return config


def       lowerCamelCase_       (   UpperCAmelCase_  )						->str:





  """simple docstring"""


  if "patch_embed.proj" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''patch_embed.proj'''  ,			'''embeddings.patch_embeddings.projection'''  )
  if "patch_embed.norm" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''patch_embed.norm'''  ,			'''embeddings.norm'''  )
  if "layers" in name:
    __UpperCAmelCase    :						Optional[int]       =						'''encoder.''' + name
  if "attn.proj" in name:
    __UpperCAmelCase    :						Optional[Any]       =						name.replace('''attn.proj'''  ,			'''attention.output.dense'''  )
  if "attn" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''attn'''  ,			'''attention.self'''  )
  if "norm1" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''norm1'''  ,			'''layernorm_before'''  )
  if "norm2" in name:
    __UpperCAmelCase    :						Dict       =						name.replace('''norm2'''  ,			'''layernorm_after'''  )
  if "mlp.fc1" in name:
    __UpperCAmelCase    :						str       =						name.replace('''mlp.fc1'''  ,			'''intermediate.dense'''  )
  if "mlp.fc2" in name:
    __UpperCAmelCase    :						int       =						name.replace('''mlp.fc2'''  ,			'''output.dense'''  )
  if "q_bias" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''q_bias'''  ,			'''query.bias'''  )
  if "k_bias" in name:
    __UpperCAmelCase    :						List[str]       =						name.replace('''k_bias'''  ,			'''key.bias'''  )
  if "v_bias" in name:
    __UpperCAmelCase    :						str       =						name.replace('''v_bias'''  ,			'''value.bias'''  )
  if "cpb_mlp" in name:
    __UpperCAmelCase    :						Tuple       =						name.replace('''cpb_mlp'''  ,			'''continuous_position_bias_mlp'''  )
  if name == "norm.weight":
    __UpperCAmelCase    :						int       =						'''layernorm.weight'''
  if name == "norm.bias":
    __UpperCAmelCase    :						Optional[Any]       =						'''layernorm.bias'''

  if "head" in name:
    __UpperCAmelCase    :						List[Any]       =						name.replace('''head'''  ,			'''classifier'''  )
  else:
    __UpperCAmelCase    :						List[Any]       =						'''swinv2.''' + name

  return name


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->int:





  """simple docstring"""


  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase    :						Any       =						orig_state_dict.pop(UpperCAmelCase_  )

    if "mask" in key:
      continue
    elif "qkv" in key:
      __UpperCAmelCase    :						int       =						key.split('''.'''  )
      __UpperCAmelCase    :						int       =						int(key_split[1]  )
      __UpperCAmelCase    :						str       =						int(key_split[3]  )
      __UpperCAmelCase    :						List[str]       =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __UpperCAmelCase    :						Optional[Any]       =						val[:dim, :]
        __UpperCAmelCase    :						Union[str, Any]       =						val[dim : dim * 2, :]
        __UpperCAmelCase    :						List[Any]       =						val[-dim:, :]
      else:
        __UpperCAmelCase    :						Optional[int]       =						val[:dim]
        __UpperCAmelCase    :						Any       =						val[
            dim : dim * 2
        ]
        __UpperCAmelCase    :						List[str]       =						val[-dim:]
    else:
      __UpperCAmelCase    :						Tuple       =						val

  return orig_state_dict


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						timm.create_model(UpperCAmelCase_  ,			pretrained=UpperCAmelCase_  )
  timm_model.eval()

  __UpperCAmelCase    :						Any       =						get_swinva_config(UpperCAmelCase_  )
  __UpperCAmelCase    :						List[str]       =						SwinvaForImageClassification(UpperCAmelCase_  )
  model.eval()

  __UpperCAmelCase    :						int       =						convert_state_dict(timm_model.state_dict()  ,			UpperCAmelCase_  )
  model.load_state_dict(UpperCAmelCase_  )

  __UpperCAmelCase    :						Optional[Any]       =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

  __UpperCAmelCase    :						Optional[Any]       =						AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''  ,			'''-'''  )  )  )
  __UpperCAmelCase    :						List[Any]       =						Image.open(requests.get(UpperCAmelCase_  ,			stream=UpperCAmelCase_  ).raw  )
  __UpperCAmelCase    :						Union[str, Any]       =						image_processor(images=UpperCAmelCase_  ,			return_tensors='''pt'''  )

  __UpperCAmelCase    :						List[Any]       =						timm_model(inputs['''pixel_values''']  )
  __UpperCAmelCase    :						Tuple       =						model(**UpperCAmelCase_  ).logits

  assert torch.allclose(UpperCAmelCase_  ,			UpperCAmelCase_  ,			atol=1e-3  )

  print(f'''Saving model {swinva_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_  )

  model.push_to_hub(
      repo_path_or_name=Path(UpperCAmelCase_  ,			UpperCAmelCase_  )  ,			organization='''nandwalritik'''  ,			commit_message='''Add model'''  ,			)


if __name__ == "__main__":
  lowercase__				:List[Any]				   =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--swinv2_name',
      default='swinv2_tiny_patch4_window8_256',
      type=str,
      help='Name of the Swinv2 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.'
  )

  lowercase__				:List[Any]				   =       parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)