


import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     get_tests_dir('fixtures/test_sentencepiece.model')

if is_sentencepiece_available():
 import sentencepiece as sp


SCREAMING_SNAKE_CASE__					:     List[Any]       =     5
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     10


@require_sentencepiece
@require_tokenizers
class 		_SCREAMING_SNAKE_CASE     (						A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    SpeechaTextTokenizer
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    True


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

       _UpperCAmelCase		:							Union[str, Any]							=     sp.SentencePieceProcessor()
       spm_model.Load(__UpperCAmelCase    )
       _UpperCAmelCase		:							List[Any]							=     ["<s>", "<pad>", "</s>", "<unk>"]

       vocab += [spm_model.IdToPiece(id_    ) for id_ in range(len(__UpperCAmelCase    )    )]
       _UpperCAmelCase		:							Union[str, Any]							=     dict(zip(__UpperCAmelCase							,					range(len(__UpperCAmelCase    )    )    )    )

       _UpperCAmelCase		:							Tuple							=     Path(self.tmpdirname    )
       save_json(__UpperCAmelCase							,					save_dir / VOCAB_FILES_NAMES["""vocab_file"""]    )
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
         copyfile(__UpperCAmelCase							,					save_dir / VOCAB_FILES_NAMES["""spm_file"""]    )

       _UpperCAmelCase		:							Any							=     SpeechaTextTokenizer.from_pretrained(self.tmpdirname    )
       tokenizer.save_pretrained(self.tmpdirname    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							int							=     "<pad>"
       _UpperCAmelCase		:							List[Any]							=     1

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


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]							,					"""<s>"""    )
       self.assertEqual(vocab_keys[1]							,					"""<pad>"""    )
       self.assertEqual(vocab_keys[-1]							,					"""j"""    )
       self.assertEqual(len(__UpperCAmelCase    )							,					10_01    )


     def        __snake_case(  self    ):
       self.assertEqual(self.get_tokenizer().vocab_size							,					10_01    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Dict							=     SpeechaTextTokenizer.from_pretrained(self.tmpdirname    )

       _UpperCAmelCase		:							List[str]							=     tokenizer.tokenize("""This is a test"""    )
       self.assertListEqual(__UpperCAmelCase							,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]    )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(__UpperCAmelCase    )							,					[2_89, 50, 14, 1_74, 3_86]							,					)

       _UpperCAmelCase		:							List[str]							=     tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
       self.assertListEqual(
           __UpperCAmelCase							,					[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """."""]							,					)
       _UpperCAmelCase		:							Optional[int]							=     tokenizer.convert_tokens_to_ids(__UpperCAmelCase    )
       self.assertListEqual(__UpperCAmelCase							,					[12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8]    )

       _UpperCAmelCase		:							Union[str, Any]							=     tokenizer.convert_ids_to_tokens(__UpperCAmelCase    )
       self.assertListEqual(
           __UpperCAmelCase							,					[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """."""]							,					)




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[int]							=     {"input_ids": [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__UpperCAmelCase							,					model_name="""facebook/s2t-small-mustc-en-de-st"""							,					revision="""a14f04cf0776c02f62a8cb800cf7909e15ea23ad"""							,					)


@require_sentencepiece
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    "valhalla/s2t_mustc_multilinguial_medium"

     __SCREAMING_SNAKE_CASE     					=    "C'est trop cool"
     __SCREAMING_SNAKE_CASE     					=    "Esto es genial"


     @classmethod
     def        __snake_case(  cls    ):
       _UpperCAmelCase		:							SpeechaTextTokenizer							=     SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name    )
       return cls


     def        __snake_case(  self    ):
       self.assertEqual(self.tokenizer.lang_code_to_id["""pt"""]							,					4    )
       self.assertEqual(self.tokenizer.lang_code_to_id["""ru"""]							,					6    )
       self.assertEqual(self.tokenizer.lang_code_to_id["""it"""]							,					9    )
       self.assertEqual(self.tokenizer.lang_code_to_id["""de"""]							,					11    )


     def        __snake_case(  self    ):
       self.assertEqual(self.tokenizer.vocab_size							,					1_00_00    )


     def        __snake_case(  self    ):
       self.assertIn(__UpperCAmelCase							,					self.tokenizer.all_special_ids    )
       _UpperCAmelCase		:							Optional[int]							=     [ES_CODE, 4, 16_01, 47, 76_47, 2]
       _UpperCAmelCase		:							Tuple							=     self.tokenizer.decode(__UpperCAmelCase							,					skip_special_tokens=__UpperCAmelCase    )
       _UpperCAmelCase		:							List[Any]							=     self.tokenizer.decode(generated_ids[1:]							,					skip_special_tokens=__UpperCAmelCase    )
       self.assertEqual(__UpperCAmelCase							,					__UpperCAmelCase    )
       self.assertNotIn(self.tokenizer.eos_token							,					__UpperCAmelCase    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     "fr"
       _UpperCAmelCase		:							Any							=     self.tokenizer(self.french_text    ).input_ids
       self.assertEqual(encoded[0]							,					__UpperCAmelCase    )
       self.assertEqual(encoded[-1]							,					self.tokenizer.eos_token_id    )




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     "fr"
       self.assertListEqual(self.tokenizer.prefix_tokens							,					[FR_CODE]    )

       _UpperCAmelCase		:							Union[str, Any]							=     "es"
       self.assertListEqual(self.tokenizer.prefix_tokens							,					[ES_CODE]    )










import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=False )	->						Tuple:
				a__	:							int      						= []

				# fmt: off
				# stem:
				rename_keys.append(("cls_token", "vit.embeddings.cls_token") )
				rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings") )

				rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight") )
				rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias") )

				# backbone
				rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight") )
				rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight") )
				rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias") )

				for stage_idx in range(len(config.backbone_config.depths ) ):
								for layer_idx in range(config.backbone_config.depths[stage_idx] ):
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight') )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias') )

								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight') )
								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight') )
								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias') )

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

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

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								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"),
								    ] )
				# fmt: on

				return rename_keys






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False )	->						Union[str, Any]:
				for i in range(config.num_hidden_layers ):
								if base_model:
												a__	:							Tuple      						= ""
								else:
												a__	:							Dict      						= "vit."
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								a__	:							int      						= state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
								a__	:							Tuple      						= state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
								# next, add query, keys and values (in that order) to the state dict
								a__	:							Optional[int]      						= in_proj_weight[
								    : config.hidden_size, :
								]
								a__	:							List[str]      						= in_proj_bias[: config.hidden_size]
								a__	:							Any      						= in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								a__	:							Any      						= in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								a__	:							Union[str, Any]      						= in_proj_weight[
								    -config.hidden_size :, :
								]
								a__	:							Dict      						= in_proj_bias[-config.hidden_size :]






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Tuple:
				a__	:							Optional[int]      						= ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(__UpperCamelCase	,				__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Optional[Any]:
				a__	:							Optional[Any]      						= dct.pop(__UpperCamelCase )
				a__	:							str      						= val






def     SCREAMING_SNAKE_CASE(      )	->						str:
				a__	:							Optional[int]      						= "http://images.cocodataset.org/val2017/000000039769.jpg"
				a__	:							Any      						= Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase ).raw )
				return im






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False )	->						Tuple:
				a__	:							Dict      						= BitConfig(
				    global_padding="same"	,				layer_type="bottleneck"	,				depths=(3, 4, 9)	,				out_features=["stage3"]	,				embedding_dynamic_padding=__UpperCamelCase	,				)
				a__	:							List[Any]      						= ViTHybridConfig(backbone_config=__UpperCamelCase	,				image_size=3_84	,				num_labels=10_00 )
				a__	:							Union[str, Any]      						= False

				# load original model from timm
				a__	:							List[Any]      						= timm.create_model(__UpperCamelCase	,				pretrained=__UpperCamelCase )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				a__	:							Optional[Any]      						= timm_model.state_dict()
				if base_model:
								remove_classification_head_(__UpperCamelCase )
				a__	:							Union[str, Any]      						= create_rename_keys(__UpperCamelCase	,				__UpperCamelCase )
				for src, dest in rename_keys:
								rename_key(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
				read_in_q_k_v(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )

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

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								a__	:							str      						= ViTHybridModel(__UpperCamelCase ).eval()
				else:
								a__	:							Dict      						= ViTHybridForImageClassification(__UpperCamelCase ).eval()
				model.load_state_dict(__UpperCamelCase )

				# create image processor
				a__	:							Union[str, Any]      						= create_transform(**resolve_data_config({}	,				model=__UpperCamelCase ) )
				a__	:							str      						= transform.transforms

				a__	:							List[Any]      						= {
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

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

				a__	:							Optional[Any]      						= prepare_img()
				a__	:							List[str]      						= transform(__UpperCamelCase ).unsqueeze(0 )
				a__	:							Optional[Any]      						= processor(__UpperCamelCase	,				return_tensors="pt" ).pixel_values

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

				# verify logits
				with torch.no_grad():
								a__	:							Dict      						= model(__UpperCamelCase )
								a__	:							Optional[Any]      						= outputs.logits

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

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

				if push_to_hub:
								print(F'Pushing model and processor to the hub {vit_name}' )
								model.push_to_hub(F'ybelkada/{vit_name}' )
								processor.push_to_hub(F'ybelkada/{vit_name}' )


if __name__ == "__main__":
						lowerCamelCase		       =      argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--vit_name""",
						    default="""vit_base_r50_s16_384""",
						    type=str,
						    help="""Name of the hybrid ViT timm model you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
						)
						parser.add_argument(
						    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
						)

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


