




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		):







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							0







   @slow
   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)
             self.assertIsNotNone(_lowerCamelCase)
             self.assertIsInstance(_lowerCamelCase					,						(BertTokenizer, BertTokenizerFast))
             self.assertGreater(len(_lowerCamelCase)					,						0)

        for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)
             self.assertIsNotNone(_lowerCamelCase)
             self.assertIsInstance(_lowerCamelCase					,						(GPTaTokenizer, GPTaTokenizerFast))
             self.assertGreater(len(_lowerCamelCase)					,						0)







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

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)
        self.assertIsInstance(_lowerCamelCase					,						(BertTokenizer, BertTokenizerFast))
        self.assertEqual(tokenizer.vocab_size					,						12)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)
        self.assertIsInstance(_lowerCamelCase					,						(RobertaTokenizer, RobertaTokenizerFast))
        self.assertEqual(tokenizer.vocab_size					,						20)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							AutoConfig.from_pretrained(_lowerCamelCase)
        self.assertIsInstance(_lowerCamelCase					,						_lowerCamelCase)
        # Check that tokenizer_type ≠ model_type
        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						config=_lowerCamelCase)
        self.assertIsInstance(_lowerCamelCase					,						(BertTokenizer, BertTokenizerFast))
        self.assertEqual(tokenizer.vocab_size					,						12)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


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

             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						tokenizer_type='''bert'''					,						use_fast=_lowerCamelCase)
             self.assertIsInstance(_lowerCamelCase					,						_lowerCamelCase)

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

             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						tokenizer_type='''gpt2'''					,						use_fast=_lowerCamelCase)
             self.assertIsInstance(_lowerCamelCase					,						_lowerCamelCase)







   @require_tokenizers
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


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

             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						tokenizer_type='''bert''')
             self.assertIsInstance(_lowerCamelCase					,						_lowerCamelCase)

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

             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						tokenizer_type='''gpt2''')
             self.assertIsInstance(_lowerCamelCase					,						_lowerCamelCase)







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

        """simple docstring"""


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







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

        """simple docstring"""


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

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

             self.assertEqual(tokenizer.model_max_length					,						512)







   @require_tokenizers
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							TOKENIZER_MAPPING.values()
        UpperCAmelCase_          =							[]

        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(_lowerCamelCase)







   @require_tokenizers
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


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







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

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained('''distilbert-base-uncased'''					,						do_lower_case=_lowerCamelCase)
        UpperCAmelCase_          =							'''Hello, world. How are you?'''
        UpperCAmelCase_          =							tokenizer.tokenize(_lowerCamelCase)
        self.assertEqual('''[UNK]'''					,						tokens[0])

        UpperCAmelCase_          =							AutoTokenizer.from_pretrained('''microsoft/mpnet-base'''					,						do_lower_case=_lowerCamelCase)
        UpperCAmelCase_          =							tokenizer.tokenize(_lowerCamelCase)
        self.assertEqual('''[UNK]'''					,						tokens[0])







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

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained('''robot-test/dummy-tokenizer-fast-with-model-config''')
        self.assertEqual(type(_lowerCamelCase)					,						_lowerCamelCase)
        self.assertEqual(tokenizer.model_max_length					,						512)
        self.assertEqual(tokenizer.vocab_size					,						30000)
        self.assertEqual(tokenizer.unk_token					,						'''[UNK]''')
        self.assertEqual(tokenizer.padding_side					,						'''right''')
        self.assertEqual(tokenizer.truncation_side					,						'''right''')







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)
        self.assertIsInstance(_lowerCamelCase					,						(BertTokenizer, BertTokenizerFast))
        with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(_lowerCamelCase)
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)

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







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

        """simple docstring"""


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







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

        """simple docstring"""


        UpperCAmelCase_          =							get_tokenizer_config('''bert-base-cased''')
        UpperCAmelCase_          =							config.pop('''_commit_hash'''					,						_lowerCamelCase)
        # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
        self.assertEqual(_lowerCamelCase					,						{'''do_lower_case''': False})

        # This model does not have a tokenizer_config so we get back an empty dict.
        UpperCAmelCase_          =							get_tokenizer_config(_lowerCamelCase)
        self.assertDictEqual(_lowerCamelCase					,						{})

        # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)
        with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(_lowerCamelCase)
             UpperCAmelCase_          =							get_tokenizer_config(_lowerCamelCase)

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







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


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

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

             UpperCAmelCase_          =							CustomTokenizer.from_pretrained(_lowerCamelCase)
             with tempfile.TemporaryDirectory() as tmp_dir:
                  tokenizer.save_pretrained(_lowerCamelCase)

                  UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)
                  self.assertIsInstance(_lowerCamelCase					,						_lowerCamelCase)

        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 lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


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

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

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

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

             # 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_          =							BertTokenizerFast.from_pretrained(_lowerCamelCase)
                  bert_tokenizer.save_pretrained(_lowerCamelCase)
                  UpperCAmelCase_          =							CustomTokenizerFast.from_pretrained(_lowerCamelCase)

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

                  UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase)
                  self.assertIsInstance(_lowerCamelCase					,						_lowerCamelCase)

                  UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						use_fast=_lowerCamelCase)
                  self.assertIsInstance(_lowerCamelCase					,						_lowerCamelCase)

        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 lowerCamelCase						(      self		:						str):

        """simple docstring"""


        with self.assertRaises(_lowerCamelCase):
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer''')
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(_lowerCamelCase):
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''					,						trust_remote_code=_lowerCamelCase)

        UpperCAmelCase_          =							AutoTokenizer.from_pretrained('''hf-internal-testing/test_dynamic_tokenizer'''					,						trust_remote_code=_lowerCamelCase)
        self.assertTrue(tokenizer.special_attribute_present)
        # Test tokenizer can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
             tokenizer.save_pretrained(_lowerCamelCase)
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						trust_remote_code=_lowerCamelCase)
        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_          =							AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''					,						trust_remote_code=_lowerCamelCase					,						use_fast=_lowerCamelCase)
             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(_lowerCamelCase)
                  UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						trust_remote_code=_lowerCamelCase					,						use_fast=_lowerCamelCase)
             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 lowerCamelCase						(      self		:						int):

        """simple docstring"""


        class __snake_case			(			A_		):
           UpperCAmelCase__  :    Any          =  False







        class __snake_case			(			A_		):
           UpperCAmelCase__  :    int          =  NewTokenizer
           UpperCAmelCase__  :    str          =  False

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

             # If remote code is disabled, we load the local one.
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''					,						trust_remote_code=_lowerCamelCase)
             self.assertEqual(tokenizer.__class__.__name__					,						'''NewTokenizerFast''')
             self.assertFalse(tokenizer.special_attribute_present)
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''					,						trust_remote_code=_lowerCamelCase					,						use_fast=_lowerCamelCase)
             self.assertEqual(tokenizer.__class__.__name__					,						'''NewTokenizer''')
             self.assertFalse(tokenizer.special_attribute_present)

             # If remote is enabled, we load from the Hub
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''					,						trust_remote_code=_lowerCamelCase)
             self.assertEqual(tokenizer.__class__.__name__					,						'''NewTokenizerFast''')
             self.assertTrue(tokenizer.special_attribute_present)
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer'''					,						trust_remote_code=_lowerCamelCase					,						use_fast=_lowerCamelCase)
             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 lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(
            '''hf-internal-testing/test_dynamic_tokenizer_legacy'''					,						trust_remote_code=_lowerCamelCase)
        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_          =							AutoTokenizer.from_pretrained(
                 '''hf-internal-testing/test_dynamic_tokenizer_legacy'''					,						trust_remote_code=_lowerCamelCase					,						use_fast=_lowerCamelCase)
             self.assertTrue(tokenizer.special_attribute_present)
             self.assertEqual(tokenizer.__class__.__name__					,						'''NewTokenizer''')
        else:
             self.assertEqual(tokenizer.__class__.__name__					,						'''NewTokenizer''')







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        with self.assertRaisesRegex(
            _lowerCamelCase					,						'''bert-base is not a local folder and is not a valid model identifier'''):
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained('''bert-base''')







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        with self.assertRaisesRegex(
            _lowerCamelCase					,						r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
             UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_lowerCamelCase					,						revision='''aaaaaa''')







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert''')
        with RequestCounter() as counter:
             UpperCAmelCase_          =							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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Union[str, Any]=False		)     ->					Optional[int]:
  _snake_case					     =       []
  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'''deit.encoder.layer.{i}.layernorm_before.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')		)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''deit.embeddings.cls_token'''),
          ('''dist_token''', '''deit.embeddings.distillation_token'''),
          ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
      ]		)

  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 "deit" from all keys that start with "deit"
    _snake_case					     =       [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''		) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('''norm.weight''', '''deit.layernorm.weight'''),
            ('''norm.bias''', '''deit.layernorm.bias'''),
            ('''head.weight''', '''cls_classifier.weight'''),
            ('''head.bias''', '''cls_classifier.bias'''),
            ('''head_dist.weight''', '''distillation_classifier.weight'''),
            ('''head_dist.bias''', '''distillation_classifier.bias'''),
        ]		)

  return rename_keys
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Tuple=False		)     ->					Tuple:
  for i in range(config.num_hidden_layers		):
    if base_model:
      _snake_case					     =       ''''''
    else:
      _snake_case					     =       '''deit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case					     =       in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case					     =       in_proj_bias[: config.hidden_size]
    _snake_case					     =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case					     =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case					     =       in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case					     =       in_proj_bias[-config.hidden_size :]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Tuple		)     ->					Tuple:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str		)     ->					str:
  _snake_case					     =       DeiTConfig()
  # all deit models have fine-tuned heads
  _snake_case					     =       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case					     =       10_00
  _snake_case					     =       '''huggingface/label-files'''
  _snake_case					     =       '''imagenet-1k-id2label.json'''
  _snake_case					     =       json.load(open(hf_hub_download(__lowerCamelCase ,					__lowerCamelCase ,					repo_type='''dataset'''		) ,					'''r'''		)		)
  _snake_case					     =       {int(__lowerCamelCase		): v for k, v in idalabel.items()}
  _snake_case					     =       idalabel
  _snake_case					     =       {v: k for k, v in idalabel.items()}
  _snake_case					     =       int(deit_name[-6:-4]		)
  _snake_case					     =       int(deit_name[-3:]		)
  # size of the architecture
  if deit_name[9:].startswith('''tiny'''		):
    _snake_case					     =       1_92
    _snake_case					     =       7_68
    _snake_case					     =       12
    _snake_case					     =       3
  elif deit_name[9:].startswith('''small'''		):
    _snake_case					     =       3_84
    _snake_case					     =       15_36
    _snake_case					     =       12
    _snake_case					     =       6
  if deit_name[9:].startswith('''base'''		):
    pass
  elif deit_name[4:].startswith('''large'''		):
    _snake_case					     =       10_24
    _snake_case					     =       40_96
    _snake_case					     =       24
    _snake_case					     =       16

  # load original model from timm
  _snake_case					     =       timm.create_model(__lowerCamelCase ,					pretrained=__lowerCamelCase		)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  _snake_case					     =       timm_model.state_dict()
  _snake_case					     =       create_rename_keys(__lowerCamelCase ,					__lowerCamelCase		)
  for src, dest in rename_keys:
    rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
  read_in_q_k_v(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)

  # load HuggingFace model
  _snake_case					     =       DeiTForImageClassificationWithTeacher(__lowerCamelCase		).eval()
  model.load_state_dict(__lowerCamelCase		)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _snake_case					     =       int(
      (2_56 / 2_24) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  _snake_case					     =       DeiTImageProcessor(size=__lowerCamelCase ,					crop_size=config.image_size		)
  _snake_case					     =       image_processor(images=prepare_img() ,					return_tensors='''pt'''		)
  _snake_case					     =       encoding['''pixel_values''']
  _snake_case					     =       model(__lowerCamelCase		)

  _snake_case					     =       timm_model(__lowerCamelCase		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__lowerCamelCase ,					outputs.logits ,					atol=1E-3		)

  Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
  print(f'''Saving model {deit_name} 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(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT 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.'
 )

 UpperCAmelCase__          = parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



