
"""simple docstring"""







import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=14 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=99 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=37 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=None ,	):
			__lowerCAmelCase    :		List[Any]		    =   parent
			__lowerCAmelCase    :		Dict		    =   batch_size
			__lowerCAmelCase    :		Union[str, Any]		    =   seq_length
			__lowerCAmelCase    :		List[str]		    =   is_training
			__lowerCAmelCase    :		int		    =   use_token_type_ids
			__lowerCAmelCase    :		Dict		    =   use_input_mask
			__lowerCAmelCase    :		Any		    =   use_labels
			__lowerCAmelCase    :		List[str]		    =   use_mc_token_ids
			__lowerCAmelCase    :		str		    =   vocab_size
			__lowerCAmelCase    :		int		    =   hidden_size
			__lowerCAmelCase    :		Union[str, Any]		    =   num_hidden_layers
			__lowerCAmelCase    :		Dict		    =   num_attention_heads
			__lowerCAmelCase    :		Union[str, Any]		    =   intermediate_size
			__lowerCAmelCase    :		Optional[Any]		    =   hidden_act
			__lowerCAmelCase    :		List[str]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Tuple		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Tuple		    =   max_position_embeddings
			__lowerCAmelCase    :		Any		    =   type_vocab_size
			__lowerCAmelCase    :		List[str]		    =   type_sequence_label_size
			__lowerCAmelCase    :		int		    =   initializer_range
			__lowerCAmelCase    :		str		    =   num_labels
			__lowerCAmelCase    :		str		    =   num_choices
			__lowerCAmelCase    :		Tuple		    =   scope
			__lowerCAmelCase    :		str		    =   self.vocab_size - 1

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

			__lowerCAmelCase    :		Dict		    =   None
			if self.use_input_mask:
				__lowerCAmelCase    :		str		    =   random_attention_mask([self.batch_size, self.seq_length]				)

			__lowerCAmelCase    :		int		    =   None
			if self.use_token_type_ids:
				__lowerCAmelCase    :		Dict		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.type_vocab_size				)

			__lowerCAmelCase    :		Tuple		    =   None
			if self.use_mc_token_ids:
				__lowerCAmelCase    :		int		    =   ids_tensor([self.batch_size, self.num_choices] ,	self.seq_length				)

			__lowerCAmelCase    :		Optional[Any]		    =   None
			__lowerCAmelCase    :		List[Any]		    =   None
			__lowerCAmelCase    :		int		    =   None
			if self.use_labels:
				__lowerCAmelCase    :		Optional[int]		    =   ids_tensor([self.batch_size] ,	self.type_sequence_label_size				)
				__lowerCAmelCase    :		str		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.num_labels				)
				__lowerCAmelCase    :		List[Any]		    =   ids_tensor([self.batch_size] ,	self.num_choices				)

			__lowerCAmelCase    :		Tuple		    =   self.get_config()

			__lowerCAmelCase    :		Dict		    =   ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,	2				)

			return (
			    config,
			    input_ids,
			    input_mask,
			    head_mask,
			    token_type_ids,
			    mc_token_ids,
			    sequence_labels,
			    token_labels,
			    choice_labels,
			)

		def 							__lowerCamelCase							(   self				):
			return CTRLConfig(
			    vocab_size=self.vocab_size ,	n_embd=self.hidden_size ,	n_layer=self.num_hidden_layers ,	n_head=self.num_attention_heads ,	n_positions=self.max_position_embeddings ,	pad_token_id=self.pad_token_id ,	)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	*_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   CTRLModel(config=__lowerCamelCase				)
			model.to(__lowerCamelCase				)
			model.eval()

			model(__lowerCamelCase ,	token_type_ids=__lowerCamelCase ,	head_mask=__lowerCamelCase				)
			model(__lowerCamelCase ,	token_type_ids=__lowerCamelCase				)
			__lowerCAmelCase    :		Union[str, Any]		    =   model(__lowerCamelCase				)
			self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)
			self.parent.assertEqual(len(result.past_key_values				) ,	config.n_layer				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	*_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		int		    =   CTRLLMHeadModel(__lowerCamelCase				)
			model.to(__lowerCamelCase				)
			model.eval()

			__lowerCAmelCase    :		Any		    =   model(__lowerCamelCase ,	token_type_ids=__lowerCamelCase ,	labels=__lowerCamelCase				)
			self.parent.assertEqual(result.loss.shape ,	()				)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.seq_length, self.vocab_size)				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.prepare_config_and_inputs()

			(
			    __lowerCAmelCase     
			)    :		Tuple		    =   config_and_inputs

			__lowerCAmelCase    :		Optional[Any]		    =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

			return config, inputs_dict







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	*_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[Any]		    =   self.num_labels
			__lowerCAmelCase    :		List[Any]		    =   CTRLForSequenceClassification(__lowerCamelCase				)
			model.to(__lowerCamelCase				)
			model.eval()
			__lowerCAmelCase    :		Tuple		    =   ids_tensor([self.batch_size] ,	self.type_sequence_label_size				)
			__lowerCAmelCase    :		Dict		    =   model(__lowerCamelCase ,	token_type_ids=__lowerCamelCase ,	labels=__lowerCamelCase				)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)				)




@require_torch
class 				A__ (			lowercase__ ,  lowercase__ ,  lowercase__ ,  unittest.TestCase):
		A_		: Union[str, Any]            =			(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
		A_		: Tuple            =			(CTRLLMHeadModel,) if is_torch_available() else ()
		A_		: int            =			(
		    {
		        'feature-extraction': CTRLModel,
		        'text-classification': CTRLForSequenceClassification,
		        'text-generation': CTRLLMHeadModel,
		        'zero-shot': CTRLForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		A_		: Optional[int]            =			True
		A_		: Any            =			False
		A_		: Optional[int]            =			False

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):



			if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
				# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
				# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
				# config could not be created.
				return True

			return False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   CTRLModelTester(self				)
			__lowerCAmelCase    :		str		    =   ConfigTester(self ,	config_class=__lowerCamelCase ,	n_embd=37				)

		def 							__lowerCamelCase							(   self				):
			super().tearDown()
			# clean-up as much as possible GPU memory occupied by PyTorch
			gc.collect()
			torch.cuda.empty_cache()

		def 							__lowerCamelCase							(   self				):
			self.config_tester.run_common_tests()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_ctrl_model(*__lowerCamelCase				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_lm_head_model(*__lowerCamelCase				)

		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
		def 							__lowerCamelCase							(   self				):
			pass

		@slow
		def 							__lowerCamelCase							(   self				):



			for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase    :		str		    =   CTRLModel.from_pretrained(__lowerCamelCase				)
				self.assertIsNotNone(__lowerCamelCase				)







		@unittest.skip('The model doesn\'t support left padding'				)  # and it's not used enough to be worth fixing :)
		def 							__lowerCamelCase							(   self				):
			pass




@require_torch
class 				A__ (			unittest.TestCase):

		def 							__lowerCamelCase							(   self				):
			super().tearDown()
			# clean-up as much as possible GPU memory occupied by PyTorch
			gc.collect()
			torch.cuda.empty_cache()







		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   CTRLLMHeadModel.from_pretrained('ctrl'				)
			model.to(__lowerCamelCase				)
			__lowerCAmelCase    :		Optional[int]		    =   torch.tensor(
			    [[1_18_59, 0, 16_11, 8]] ,	dtype=torch.long ,	device=__lowerCamelCase				)  # Legal the president is
			__lowerCAmelCase    :		Optional[Any]		    =   [
			    1_18_59,
			    0,
			    16_11,
			    8,
			    5,
			    1_50,
			    2_64_49,
			    2,
			    19,
			    3_48,
			    4_69,
			    3,
			    25_95,
			    48,
			    2_07_40,
			    24_65_33,
			    24_65_33,
			    19,
			    30,
			    5,
			]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

			__lowerCAmelCase    :		List[Any]		    =   model.generate(__lowerCamelCase ,	do_sample=__lowerCamelCase				)
			self.assertListEqual(output_ids[0].tolist() ,	__lowerCamelCase				)

"""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 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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[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"
		__lowerCAmelCase    :		Optional[Any]		    =   [(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       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	for i in range(config.num_hidden_layers	):
		if base_model:
			__lowerCAmelCase    :		Tuple		    =   ''
		else:
			__lowerCAmelCase    :		int		    =   'vit.'
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		__lowerCAmelCase    :		int		    =   state_dict.pop(F"blocks.{i}.attn.qkv.weight"	)
		__lowerCAmelCase    :		Dict		    =   state_dict.pop(F"blocks.{i}.attn.qkv.bias"	)
		# next, add query, keys and values (in that order) to the state dict
		__lowerCAmelCase    :		Tuple		    =   in_proj_weight[
		    : config.hidden_size, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[: config.hidden_size]
		__lowerCAmelCase    :		List[Any]		    =   in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		__lowerCAmelCase    :		str		    =   in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		__lowerCAmelCase    :		Optional[int]		    =   in_proj_weight[
		    -config.hidden_size :, :
		]
		__lowerCAmelCase    :		Union[str, Any]		    =   in_proj_bias[-config.hidden_size :]
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(_UpperCamelCase ,						_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   dct.pop(_UpperCamelCase	)
	__lowerCAmelCase    :		List[Any]		    =   val
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Optional[int]		    =   'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase    :		Optional[Any]		    =   Image.open(requests.get(_UpperCamelCase ,						stream=_UpperCamelCase	).raw	)
	return im
@torch.no_grad()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=False	):
	__lowerCAmelCase    :		List[str]		    =   BitConfig(
	    global_padding='same' ,						layer_type='bottleneck' ,						depths=(3, 4, 9) ,						out_features=['stage3'] ,						embedding_dynamic_padding=_UpperCamelCase ,						)
	__lowerCAmelCase    :		Tuple		    =   ViTHybridConfig(backbone_config=_UpperCamelCase ,						image_size=384 ,						num_labels=1000	)
	__lowerCAmelCase    :		Optional[Any]		    =   False

	# load original model from timm
	__lowerCAmelCase    :		Tuple		    =   timm.create_model(_UpperCamelCase ,						pretrained=_UpperCamelCase	)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	__lowerCAmelCase    :		str		    =   timm_model.state_dict()
	if base_model:
		remove_classification_head_(_UpperCamelCase	)
	__lowerCAmelCase    :		Dict		    =   create_rename_keys(_UpperCamelCase ,						_UpperCamelCase	)
	for src, dest in rename_keys:
		rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
	read_in_q_k_v(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		__lowerCAmelCase    :		str		    =   ViTHybridModel(_UpperCamelCase	).eval()
	else:
		__lowerCAmelCase    :		Optional[Any]		    =   ViTHybridForImageClassification(_UpperCamelCase	).eval()
	model.load_state_dict(_UpperCamelCase	)

	# create image processor
	__lowerCAmelCase    :		Union[str, Any]		    =   create_transform(**resolve_data_config({} ,						model=_UpperCamelCase	)	)
	__lowerCAmelCase    :		Optional[int]		    =   transform.transforms

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

	__lowerCAmelCase    :		List[str]		    =   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() ,						)

	__lowerCAmelCase    :		Optional[Any]		    =   prepare_img()
	__lowerCAmelCase    :		Union[str, Any]		    =   transform(_UpperCamelCase	).unsqueeze(0	)
	__lowerCAmelCase    :		Union[str, Any]		    =   processor(_UpperCamelCase ,						return_tensors='pt'	).pixel_values

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

	# verify logits
	with torch.no_grad():
		__lowerCAmelCase    :		Any		    =   model(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   outputs.logits

	print('Predicted class:' ,						logits.argmax(-1	).item()	)
	if base_model:
		__lowerCAmelCase    :		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:
		__lowerCAmelCase    :		List[str]		    =   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)