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

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


snake_case :		Optional[int]							      =  get_tests_dir("fixtures/dummy_feature_extractor_config.json")
snake_case :		List[str]							      =  get_tests_dir("fixtures/vocab.json")
snake_case :		Tuple							      =  get_tests_dir("fixtures")





class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     0







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"		)
				self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with tempfile.TemporaryDirectory() as tmpdirname:
					__magic_name__		:		Tuple	   =     WavaVecaConfig()
					__magic_name__		:		int	   =     AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"		)

					# save in new folder
					model_config.save_pretrained(_a		)
					processor.save_pretrained(_a		)

					__magic_name__		:		int	   =     AutoProcessor.from_pretrained(_a		)

				self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with tempfile.TemporaryDirectory() as tmpdirname:
					# copy relevant files
					copyfile(_a						,   os.path.join(_a						,   _a		)		)
					copyfile(_a						,   os.path.join(_a						,   "vocab.json"		)		)

					__magic_name__		:		List[Any]	   =     AutoProcessor.from_pretrained(_a		)

				self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with tempfile.TemporaryDirectory() as tmpdirname:
					__magic_name__		:		Optional[int]	   =     WavaVecaFeatureExtractor()
					__magic_name__		:		Dict	   =     AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"		)

					__magic_name__		:		Dict	   =     WavaVecaProcessor(_a						,   _a		)

					# save in new folder
					processor.save_pretrained(_a		)

					# drop `processor_class` in tokenizer
					with open(os.path.join(_a						,   _a		)						,   "r"		) as f:
						__magic_name__		:		Optional[Any]	   =     json.load(_a		)
						config_dict.pop("processor_class"		)

					with open(os.path.join(_a						,   _a		)						,   "w"		) as f:
						f.write(json.dumps(_a		)		)

					__magic_name__		:		Any	   =     AutoProcessor.from_pretrained(_a		)

				self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with tempfile.TemporaryDirectory() as tmpdirname:
					__magic_name__		:		Dict	   =     WavaVecaFeatureExtractor()
					__magic_name__		:		Dict	   =     AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"		)

					__magic_name__		:		List[str]	   =     WavaVecaProcessor(_a						,   _a		)

					# save in new folder
					processor.save_pretrained(_a		)

					# drop `processor_class` in feature extractor
					with open(os.path.join(_a						,   _a		)						,   "r"		) as f:
						__magic_name__		:		List[str]	   =     json.load(_a		)
						config_dict.pop("processor_class"		)

					with open(os.path.join(_a						,   _a		)						,   "w"		) as f:
						f.write(json.dumps(_a		)		)

					__magic_name__		:		int	   =     AutoProcessor.from_pretrained(_a		)

				self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				with tempfile.TemporaryDirectory() as tmpdirname:
					__magic_name__		:		int	   =     WavaVecaConfig(processor_class="Wav2Vec2Processor"		)
					model_config.save_pretrained(_a		)
					# copy relevant files
					copyfile(_a						,   os.path.join(_a						,   "vocab.json"		)		)
					# create emtpy sample processor
					with open(os.path.join(_a						,   _a		)						,   "w"		) as f:
						f.write("{}"		)

					__magic_name__		:		str	   =     AutoProcessor.from_pretrained(_a		)

				self.assertIsInstance(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				# If remote code is not set, we will time out when asking whether to load the model.
				with self.assertRaises(_a		):
					__magic_name__		:		Tuple	   =     AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"		)
				# If remote code is disabled, we can't load this config.
				with self.assertRaises(_a		):
					__magic_name__		:		Any	   =     AutoProcessor.from_pretrained(
					    "hf-internal-testing/test_dynamic_processor"						,   trust_remote_code=_a		)

				__magic_name__		:		Any	   =     AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"						,   trust_remote_code=_a		)
				self.assertTrue(processor.special_attribute_present		)
				self.assertEqual(processor.__class__.__name__						,   "NewProcessor"		)

				__magic_name__		:		str	   =     processor.feature_extractor
				self.assertTrue(feature_extractor.special_attribute_present		)
				self.assertEqual(feature_extractor.__class__.__name__						,   "NewFeatureExtractor"		)

				__magic_name__		:		Dict	   =     processor.tokenizer
				self.assertTrue(tokenizer.special_attribute_present		)
				if is_tokenizers_available():
					self.assertEqual(tokenizer.__class__.__name__						,   "NewTokenizerFast"		)

					# Test we can also load the slow version
					__magic_name__		:		str	   =     AutoProcessor.from_pretrained(
					    "hf-internal-testing/test_dynamic_processor"						,   trust_remote_code=_a						,   use_fast=_a		)
					__magic_name__		:		int	   =     new_processor.tokenizer
					self.assertTrue(new_tokenizer.special_attribute_present		)
					self.assertEqual(new_tokenizer.__class__.__name__						,   "NewTokenizer"		)
				else:
					self.assertEqual(tokenizer.__class__.__name__						,   "NewTokenizer"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				try:
					AutoConfig.register("custom"						,   _a		)
					AutoFeatureExtractor.register(_a						,   _a		)
					AutoTokenizer.register(_a						,   slow_tokenizer_class=_a		)
					AutoProcessor.register(_a						,   _a		)
					# Trying to register something existing in the Transformers library will raise an error
					with self.assertRaises(_a		):
						AutoProcessor.register(_a						,   _a		)

					# Now that the config is registered, it can be used as any other config with the auto-API
					__magic_name__		:		str	   =     CustomFeatureExtractor.from_pretrained(_a		)

					with tempfile.TemporaryDirectory() as tmp_dir:
						__magic_name__		:		Dict	   =     os.path.join(_a						,   "vocab.txt"		)
						with open(_a						,   "w"						,   encoding="utf-8"		) as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
						__magic_name__		:		int	   =     CustomTokenizer(_a		)

					__magic_name__		:		Optional[Any]	   =     CustomProcessor(_a						,   _a		)

					with tempfile.TemporaryDirectory() as tmp_dir:
						processor.save_pretrained(_a		)
						__magic_name__		:		Optional[Any]	   =     AutoProcessor.from_pretrained(_a		)
						self.assertIsInstance(_a						,   _a		)

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







			def 			SCREAMING_SNAKE_CASE    (				self		):





				class       _snake_case					(     snake_case				):
							UpperCamelCase__          =    False







				class       _snake_case					(     snake_case				):
							UpperCamelCase__          =    False







				class       _snake_case					(     snake_case				):
							UpperCamelCase__          =    'AutoFeatureExtractor'
							UpperCamelCase__          =    'AutoTokenizer'
							UpperCamelCase__          =    False

				try:
					AutoConfig.register("custom"						,   _a		)
					AutoFeatureExtractor.register(_a						,   _a		)
					AutoTokenizer.register(_a						,   slow_tokenizer_class=_a		)
					AutoProcessor.register(_a						,   _a		)
					# If remote code is not set, the default is to use local classes.
					__magic_name__		:		Optional[Any]	   =     AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"		)
					self.assertEqual(processor.__class__.__name__						,   "NewProcessor"		)
					self.assertFalse(processor.special_attribute_present		)
					self.assertFalse(processor.feature_extractor.special_attribute_present		)
					self.assertFalse(processor.tokenizer.special_attribute_present		)

					# If remote code is disabled, we load the local ones.
					__magic_name__		:		int	   =     AutoProcessor.from_pretrained(
					    "hf-internal-testing/test_dynamic_processor"						,   trust_remote_code=_a		)
					self.assertEqual(processor.__class__.__name__						,   "NewProcessor"		)
					self.assertFalse(processor.special_attribute_present		)
					self.assertFalse(processor.feature_extractor.special_attribute_present		)
					self.assertFalse(processor.tokenizer.special_attribute_present		)

					# If remote is enabled, we load from the Hub.
					__magic_name__		:		Optional[int]	   =     AutoProcessor.from_pretrained(
					    "hf-internal-testing/test_dynamic_processor"						,   trust_remote_code=_a		)
					self.assertEqual(processor.__class__.__name__						,   "NewProcessor"		)
					self.assertTrue(processor.special_attribute_present		)
					self.assertTrue(processor.feature_extractor.special_attribute_present		)
					self.assertTrue(processor.tokenizer.special_attribute_present		)

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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"		)
				self.assertEqual(processor.__class__.__name__						,   "BertTokenizerFast"		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"		)
				self.assertEqual(processor.__class__.__name__						,   "ConvNextImageProcessor"		)







@is_staging_test
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']







			@classmethod
			def 			SCREAMING_SNAKE_CASE    (				cls		):
				__magic_name__		:		List[str]	   =     TOKEN
				HfFolder.save_token(_a		)







			@classmethod
			def 			SCREAMING_SNAKE_CASE    (				cls		):
				try:
					delete_repo(token=cls._token						,   repo_id="test-processor"		)
				except HTTPError:
					pass

				try:
					delete_repo(token=cls._token						,   repo_id="valid_org/test-processor-org"		)
				except HTTPError:
					pass

				try:
					delete_repo(token=cls._token						,   repo_id="test-dynamic-processor"		)
				except HTTPError:
					pass







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     WavaVecaProcessor.from_pretrained(_a		)
				with tempfile.TemporaryDirectory() as tmp_dir:
					processor.save_pretrained(
					    os.path.join(_a						,   "test-processor"		)						,   push_to_hub=_a						,   use_auth_token=self._token		)

					__magic_name__		:		int	   =     WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor'''		)
					for k, v in processor.feature_extractor.__dict__.items():
						self.assertEqual(_a						,   getattr(new_processor.feature_extractor						,   _a		)		)
					self.assertDictEqual(new_processor.tokenizer.get_vocab()						,   processor.tokenizer.get_vocab()		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     WavaVecaProcessor.from_pretrained(_a		)

				with tempfile.TemporaryDirectory() as tmp_dir:
					processor.save_pretrained(
					    os.path.join(_a						,   "test-processor-org"		)						,   push_to_hub=_a						,   use_auth_token=self._token						,   organization="valid_org"						,   )

					__magic_name__		:		Dict	   =     WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"		)
					for k, v in processor.feature_extractor.__dict__.items():
						self.assertEqual(_a						,   getattr(new_processor.feature_extractor						,   _a		)		)
					self.assertDictEqual(new_processor.tokenizer.get_vocab()						,   processor.tokenizer.get_vocab()		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				CustomFeatureExtractor.register_for_auto_class()
				CustomTokenizer.register_for_auto_class()
				CustomProcessor.register_for_auto_class()

				__magic_name__		:		str	   =     CustomFeatureExtractor.from_pretrained(_a		)

				with tempfile.TemporaryDirectory() as tmp_dir:
					__magic_name__		:		List[str]	   =     os.path.join(_a						,   "vocab.txt"		)
					with open(_a						,   "w"						,   encoding="utf-8"		) as vocab_writer:
						vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]		)		)
					__magic_name__		:		List[Any]	   =     CustomTokenizer(_a		)

				__magic_name__		:		Union[str, Any]	   =     CustomProcessor(_a						,   _a		)

				with tempfile.TemporaryDirectory() as tmp_dir:
					create_repo(f'''{USER}/test-dynamic-processor'''						,   token=self._token		)
					__magic_name__		:		Optional[int]	   =     Repository(_a						,   clone_from=f'''{USER}/test-dynamic-processor'''						,   token=self._token		)
					processor.save_pretrained(_a		)

					# This has added the proper auto_map field to the feature extractor config
					self.assertDictEqual(
					    processor.feature_extractor.auto_map						,   {
					        "AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
					        "AutoProcessor": "custom_processing.CustomProcessor",
					    }						,   )

					# This has added the proper auto_map field to the tokenizer config
					with open(os.path.join(_a						,   "tokenizer_config.json"		)		) as f:
						__magic_name__		:		Optional[int]	   =     json.load(_a		)
					self.assertDictEqual(
					    tokenizer_config["auto_map"]						,   {
					        "AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
					        "AutoProcessor": "custom_processing.CustomProcessor",
					    }						,   )

					# The code has been copied from fixtures
					self.assertTrue(os.path.isfile(os.path.join(_a						,   "custom_feature_extraction.py"		)		)		)
					self.assertTrue(os.path.isfile(os.path.join(_a						,   "custom_tokenization.py"		)		)		)
					self.assertTrue(os.path.isfile(os.path.join(_a						,   "custom_processing.py"		)		)		)

					repo.push_to_hub()

				__magic_name__		:		List[str]	   =     AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''						,   trust_remote_code=_a		)
				# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
				self.assertEqual(new_processor.__class__.__name__						,   "CustomProcessor"		)


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()
snake_case :		Optional[Any]							      =  logging.get_logger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Union[str, Any]=False   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     []

	# 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"
		__magic_name__		:		int	   =     [(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   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Any      ,   _snake_case      :  Dict=False   )				->		int:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(_snake_case      ,   _snake_case   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  int      ,   _snake_case      :  Union[str, Any]   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		int	   =     dct.pop(_snake_case   )
	__magic_name__		:		List[Any]	   =     val






def   lowerCAmelCase_						(					)				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
	__magic_name__		:		List[str]	   =     Image.open(requests.get(_snake_case      ,   stream=_snake_case   ).raw   )
	return im






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Any      ,   _snake_case      :  int=False   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     BitConfig(
	    global_padding="same"      ,   layer_type="bottleneck"      ,   depths=(3, 4, 9)      ,   out_features=["stage3"]      ,   embedding_dynamic_padding=_snake_case      ,   )
	__magic_name__		:		List[str]	   =     ViTHybridConfig(backbone_config=_snake_case      ,   image_size=384      ,   num_labels=1000   )
	__magic_name__		:		str	   =     False

	# load original model from timm
	__magic_name__		:		Union[str, Any]	   =     timm.create_model(_snake_case      ,   pretrained=_snake_case   )
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__magic_name__		:		List[Any]	   =     timm_model.state_dict()
	if base_model:
		remove_classification_head_(_snake_case   )
	__magic_name__		:		Tuple	   =     create_rename_keys(_snake_case      ,   _snake_case   )
	for src, dest in rename_keys:
		rename_key(_snake_case      ,   _snake_case      ,   _snake_case   )
	read_in_q_k_v(_snake_case      ,   _snake_case      ,   _snake_case   )

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__magic_name__		:		List[str]	   =     ViTHybridModel(_snake_case   ).eval()
	else:
		__magic_name__		:		str	   =     ViTHybridForImageClassification(_snake_case   ).eval()
	model.load_state_dict(_snake_case   )

	# create image processor
	__magic_name__		:		List[Any]	   =     create_transform(**resolve_data_config({}      ,   model=_snake_case   )   )
	__magic_name__		:		int	   =     transform.transforms

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

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

	__magic_name__		:		List[Any]	   =     prepare_img()
	__magic_name__		:		Any	   =     transform(_snake_case   ).unsqueeze(0   )
	__magic_name__		:		Tuple	   =     processor(_snake_case      ,   return_tensors="pt"   ).pixel_values

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

	# verify logits
	with torch.no_grad():
		__magic_name__		:		Optional[int]	   =     model(_snake_case   )
		__magic_name__		:		List[str]	   =     outputs.logits

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

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

	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__":
				snake_case :		Any							      =  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."
				)

				snake_case :		List[Any]							      =  parser.parse_args()
				convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
